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Machine Learning for VLSI CAD: A Case Study in On-Chip Power Grid Design

Researchers review some of the on-chip power grid design solutions using AI/ML approaches.

“With the improvement of VLSI technology, on-chip power grid design is becoming more challenging than before. In this design phase of VLSI CAD, power grids are generated in order to make power and ground connections to transistors or logic blocks. However, due to the scaling of supply voltage and increase in the number of transistors per unit area of the chip, power grid design has become a considerable challenge. The two major issues encountered during power transfer via power grid are IR drop and Electromigration (EM). For a large chip, designers have to perform many iterations of a design in order to minimize IR drop and EM violations, which increases design cycle time. Recently, machine learning (ML) techniques have attracted the VLSI CAD community and are found to be very effective in solving VLSI CAD problems. However, very few works attempted to solve on-chip power grid design problem using machine learning. Therefore, this paper reviews some of the on-chip power grid design solutions using AI/ML approaches.”

Find the technical paper here (author’s site) or here (IEEE Xplore). Published 2021.

Dey, Sukanta, Sukumar Nandi, and Gaurav Trivedi. Machine Learning for VLSI CAD: A Case Study in On-Chip Power Grid Design.” 2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE, 2021. DOI: 10.1109/ISVLSI51109.2021.00075.

Visit Semiconductor Engineering’s Technical Paper library here and discover many more chip industry academic papers.

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A Case Study in VLSI Design for Communications: Design of a Pipelined Adaptive DFE for GMSK over Indoor Radio Channels

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L. D. Kabulepa 5 ,
B. Voss 5 &
M. Glesner 5

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Advances in microelectronics stemming from integrated circuit technology to novel system-level solutions are very promising for high speed wireless communications and mobile computing. However, a long transition time often occurs between the algorithm development and the final VLSI implementation because of the mismatch between algorithm-domain goal considerations and VLSI design requirements. While the algorithm designers focus on the performance improvement in terms of bit error rate (BER) or signal-to-noise ratio (SNR), the VLSI designers stress on implementation oriented issues such as speed, power consumption and chip area. Therefore, the skills in trading off VLSI design issues with performance constraints during the course of the VLSI implementation of digital signal processing and communications algorithms is of great interest. The education on these skills is usually not addressed in the existing courses of electronic and communications engineering.

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N. R. Shanbhag and K. K. Parhi, Pipelined Adaptive Digital Filters , Kluwer Academic Press, 1994.

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T. S. Rappaport, Wireless Communications , Prentice Hall, NJ, 1996.

E. Biglieri, J. Proakis, S. Shamai, “Fading Channels: Information-Theoritic and Communications Aspects”, IEEE Trans. Information Theory , vol. 44, no. 6, Oct. 1998, pp. 310–312.

Article MathSciNet Google Scholar

N. R. Shanbhag and K. K. Parhi, “Relaxed Look-Ahead Pipelined Kalman Architecture”, Proc. 27th Asimolar Conference on Cir., Syst. and Comp., Pacific Grove, CA, 1993, pp. 1225–1229.

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Institute of Microlectronic Systems, Darmstadt University of Technology, Karlstr. 15, 64283, Darmstadt, Germany

L. D. Kabulepa, B. Voss & M. Glesner

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Kabulepa, L.D., Voss, B., Glesner, M. (2000). A Case Study in VLSI Design for Communications: Design of a Pipelined Adaptive DFE for GMSK over Indoor Radio Channels. In: Courtois, B., Guillemot, N., Kamarinos, G., Stéhelin, G. (eds) Microelectronics Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9506-3_55

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Latest Research topics in vlsi design

Latest research topics in vlsi design.

If we narrow down our discussion to research in areas like electronics, electrical, computer science, artificial intelligence , wireless communication and related fields, which are the base of everything in this high-tech world. In these fields researchers have developed applications (aided with technology) for every field ranging from biomedical to aerospace and construction, which were nowhere related to electronics or even current.

As the research fields we are talking about are providing base to the developing world and providing it with reliable technologies which are being used in real time, the work of researcher becomes more wide starting with an idea to the realization of the idea in the real world in form of application or product.

To make a reliable and working model the idea of the VLSI design project ( i.e speech processing application, biomedical monitoring system etc) needs to be implemented and re-implemented, re-tested and improvised. The there are many development cycles and techniques available which eases up the implementation like:

Behavioral simulation
Software based model
Hardware Implementation (ASIC)
Programmable hardware (FPGA)
Co-simulation

Behavioral simulation is used at initial phase and it is not appropriate for testing the real time behavior of the system in actual environment as it is more close to systems behavior in ideal environment.

We can simulate the actual environment by using different software models (more like software models of channels used to test communication systems) but its capabilities are also limited to human capability to model the environmental conditions in mathematical equations and models.

All of us are familiar with ASIC, their high performance and hardwired implementation. These are good for final implementation but not for intermediate stages of implementation and testing. Nothing is better than ASIC for real time testing of analog VLSI circuits. But for digital circuits and DSP applications we have a better option of FPGA (Field Programmable Gate Array).

The hardware co-simulation is a good idea to test and monitor systems in real time. To get more details about PhD thesis in VLSI you can do online research or contact us.

latest Low power research topics in vlsi design

The Research Support Centre provides expert advice and support across the whole Engineering and Technical research lifecycle, from discovery through exploitation of technical and translational research. The centre has two primary functions:

i) to facilitate the delivery of the Engineering Sciences research strategy and to build partnerships andii) to bring together all the technical research management and support services for Students.

To achieve these goals the centre is made up of two inter-relating components. The Academic Research Support Centre consists of the Research Coordination Office, Platform Technologies team and a Translational Research Office. The Technical Research Support Centre is made up of the Joint Research Office.

The Research Support Centre encompasses a wide range of expertise and facilities. By coordinating these resources, we can provide researchers with a package of support that is integrated, high quality and streamlined – and clearly accountable.

Once a researcher has a proposal for high quality research that will benefit, they can access all the help and resources they need through one gateway. This includes support with the approval process and funding applications and help setting up technical trials.

VLSI PHD Projects

Our research interests cover low power processor architectures, low power circuit design techniques, analog and mixed signal circuit design, rapid prototyping of digital systems, reconfigurable processors, Digital arithmetic, advanced processor architectures, vlsi implementation of signal and image processing algorithms, testing verification, memory design, Embedded vlsi and asynchronous circuits.

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Description for “Ph.d guidance with project assitance” Ph.d/ M.Phil PROJECT ASSISTANCE We look forward to welcoming you to one of our “Research and Development Division” for all Ph.D., Research scholars. We will arrange you the following details for completing your Ph.d Degree

Any University Admission- We provides a step-to-step guide to completing the application form, and will help make the process as straight forward as possible.
Guide Arrangement
Survey Paper Preparation
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Implementation in all domains
Mobile Ad hoc Networks
Wireless Networks
Image Processing
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Wireless Senor Networks

Delivering effective support on your Ph. D work:

Companies represents a simple and practical advice on the problems of getting started, getting organized with the working on Ph.D projects.

We make you understand the practicalities of surviving the ordeal. We just make you divide the huge task into less challenging pieces. The training includes a suggested structure and a guide to what should go in each section.

We afford complete support with real-time exposure in your Ph.D works in the field of VLSI. Our Mission drives us in the way of delivering applications as well as products with complete integrity, innovative & interesting ideas with 100% accuracy.

Assistance in ALL Stages of your PhD Research in VLSI from Topic Selection to Thesis Submission.
Creating 100% confident in submitting your thesis work.
Our experienced professionals support you in your research works.
Providing complete solutions for the Research Scholars in many advanced domains.

Technologies used in VLSI:

Modelsim 6.5b Simulator
Xilinx ISE 10.1 System generator

III. Quartus 11.1

Tanner v7 EDA tool

iii. W-Edit

Microwind & DSCH v2

VII. P-spice

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Digital signal processing Vlsi
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Vlsi based projects like image processing projects, low power projects, matlab with vlsi projects , cryptography projects, OFDM projects, SDR projects, communication projects, zigbee projects, digital signal processing projects, and also protocol interfacing projects like uart ,i2c,spi projects.

Signal and Image processing projects can be simulated by using Modelsim 6.5b and synthesized by Xilinx 10.1 using Spartan IIIe fpga and by Quartus 11.1using altera de2 fpga. In image processing projects, the input image or video can be converted to coefficients using Matlab. Low power projects can be designed using Tanner, Microwind and spice tools.

We spotlights on imparting an overall exposure to the concept and design methodologies of all major aspects of vlsi engineering relevant to industry needs and ground-breaking thoughts with 100% pure accuracy.

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Emerging VLSI Trends in 2023

by Maven Silicon
July 19, 2023
3 minutes read

Looking for the latest VLSI trends and VLSI jobs in 2023? Maven Silicon, a leading VLSI training institute, is here to guide you. VLSI is revolutionizing industries with its ability to integrate millions of transistors onto a single chip. In this blog post, we’ll explore the emerging VLSI trends in 2023 that are shaping the future and highlight the exciting job openings in this field. Discover the benefits of pursuing a career in VLSI and how Maven Silicon can help you kick-start your journey.

VLSI Application & Trends in 2023

The applications of VLSI span across various industries, including telecommunications, automotive, healthcare, and artificial intelligence. As we move into 2023, several VLSI trends are making waves:

AI-driven VLSI

Artificial Intelligence (AI) has merged with VLSI, opening up endless possibilities. AI-driven VLSI solutions have gained significant traction in industries like autonomous vehicles, robotics, smart homes, and beyond. The integration of AI algorithms directly into VLSI chips allows for the real-time processing of massive amounts of data, leading to intelligent decision-making and unprecedented levels of efficiency. This trend empowers autonomous vehicles to analyze complex surroundings, robots to navigate dynamically changing environments, and smart homes to adapt to residents’ preferences seamlessly. The synergy between AI and VLSI has propelled us toward a new era of intelligent and responsive technologies.

IoT and VLSI

The Internet of Things (IoT) revolution is in full swing, and VLSI plays a pivotal role in shaping this interconnected ecosystem. Emerging trends in VLSI focus on designing chips optimized for IoT-enabled devices, ensuring efficient data communication, low power consumption, and enhanced security. These specialized VLSI chips enable IoT devices to communicate seamlessly over the internet, exchanging data with other devices and cloud services. Moreover, with advancements in low-power design techniques, IoT devices can operate for extended periods on battery power, making them more practical and environmentally friendly. VLSI’s contribution to IoT is driving the proliferation of smart homes, smart cities, and industrial automation, transforming the way we interact with our surroundings.

Edge Computing and VLSI

Edge computing has emerged as a game-changer in handling real-time data processing and analysis. VLSI’s role in this trend is crucial, as it enables the development of high-performance, energy-efficient chips tailored for edge devices. By processing data locally at the edge, these VLSI chips significantly reduce latency and response times, making them ideal for applications that demand immediate results. Edge devices, such as sensors and cameras, benefit from low-power VLSI solutions that allow for prolonged operation without compromising performance. The combination of edge computing and VLSI has unlocked a new realm of possibilities, from responsive AI applications to smart infrastructure like traffic management and environmental monitoring.

Benefits of VLSI

Exciting and challenging work.

The field of VLSI indeed provides a dynamic and intellectually stimulating work environment for engineers and professionals. As a VLSI engineer, you get the opportunity to be at the forefront of designing complex integrated circuits that power a wide range of electronic devices, from smartphones and computers to IoT devices and automotive electronics.

Lucrative Job Opportunities

The demand for VLSI professionals is on the rise, making it a highly sought-after field with numerous job opportunities across various industries. As technology continues to advance and electronic devices become an integral part of our lives, the need for skilled VLSI engineers has grown significantly.

Positions such as VLSI Design Engineer, Verification Engineer, and Physical Design Engineer are in high demand. VLSI Design Engineers are responsible for designing and architecting integrated circuits, while Verification Engineers focus on validating and testing chip designs. Physical Design Engineers, on the other hand, play a crucial role in implementing the circuit layout to optimize performance and power consumption.

Also read: Skills required to become a VLSI engineer?

Job Openings

If you’re eager to embark on a VLSI career, numerous job openings await you. Maven Silicon is renowned for its VLSI training with 100% placement assistance. Explore exciting roles like VLSI Design Engineer, Verification Engineer, Physical Design Engineer, FPGA Engineer, and Analog/Mixed-Signal Design Engineer.

Also read: Salary of VLSI Engineers in India

As we step into 2023, the world of VLSI presents abundant opportunities. Stay updated with the latest VLSI trends, leverage the benefits of this field, and secure a rewarding career in VLSI. Maven Silicon can equip you with the necessary skills to excel in the ever-evolving VLSI landscape. Start your journey towards a successful VLSI career today with our job-oriented courses .

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Why should i do vlsi training.

All the Integrated Chips we use in mobiles, TVs, computers, satellites, and automobiles, etc. are designed with VLSI technology. Hence, there is a huge scope and growth in the VLSI Industry and it is full of job opportunities. Since there is a huge gap between what the college education offers and the industry expectation, it is recommended to go for the VLSI training which bridges that gap and gives you a great hands-on experience.

What is chip designing?

Steps involved in Chip design Chip’s architecture: Create circuit designs, Run simulations, Supervise layout, Tape out the chip to the foundry and Evaluate the prototype once the chip comes back from the laboratory. Chip designers work to make faster, cheaper and more innovative chips that can automate parts or the entire function of electronic devices. A chip design engineer’s job involves architecture, logic design, circuit design and physical design of the chip, testing, and verification of the final product.

Is VLSI a good career?

VLSI is a very good domain to build a career with a huge number of opportunities. There is a demand for chips in every sector, be it automobiles, consumer electronics or high-end servers. You should have good command on Verilog, SystemVerilog, and UVM to start your career as VLSI Design or VLSI Verification Engineer

What is the eligibility for VLSI Chip Designing Courses?

The undergraduates, graduates, or postgraduates from below streams can take up VLSI Chip Design Course and make a career in VLSI Industry. BE/BTech in EEE/ECE/TE or ME/MTech/MS in Electronics/MSc Electronics

To join the industry as a VLSI verification engineer, you must have hands-on experience of below topics: SystemVerilog, Universal Verification Methodologies UVM, Assertion based Verification SVA

Maven Silicon provides the best quality VLSI training through a variety of design and verification courses to suit your need and demand. We offer online VLSI courses, Job-oriented fulltime and Blended VLSI courses, Internship programs, part time courses and corporate training.Explore our offerings at https://www.maven-silicon.com/

Every course has a different admission procedure: 1. For Advanced VLSI Design and Verification course at Maven Silicon, you can apply while you are in the final semester, graduation or post-graduation. 2. For the Internship program, you can apply in your pre-final/final year. Advise you to book your seats in advance, pertaining to limited admissions and increased demand. 3. You can subscribe to our online courses directly from our elearn portal https://elearn.maven-silicon.com/ You can apply for our Online, Job-oriented, Part-time and Corporate courses on https://www.maven-silicon.com/application

We do have an entrance exam for our job-oriented courses VLSI RN and VLSI VM. After you meet the eligibility criteria you have to undergo an Online Entrance Test which would check you on the concepts of Basic Electronics and Digital Electronics. Post scoring 60% in this test, you are processed for the technical interview with our technical experts. Based on your performance during the interview, you will be selected for the Advanced VLSI Design and Verification course. For our online VLSI courses, we do not have any entrance exams. You can directly subscribe the courses from our elearn portal https://elearn.maven-silicon.com/

Yes, we do provide the scholarship on our job-oriented courses VLSI RN and VLSI VM based on your performance in the technical interview. To excel in the Online entrance test and the technical interview, we suggest you take our Online Digital electronics course at https://elearn.maven-silicon.com/digital-electronics This online Digital electronics course will help you to learn and refresh the complete fundamentals of digital electronics, highly needed for any VLSI course. Contact us for more details.

We provide 100% placement assistance with our job-oriented course until you get placed. You can refer the link for the placement updates and know more about our hiring partners: https://www.maven-silicon.com/placement

VLSI Frontend course imparts training in the Design and Verification of a chip which mostly includes RTL(Register Transfer Level) coding using either VHDL/Verilog/SystemVerilog and the verification of the DUT(can be an IP or SOC) by building verification Environment or Testbench using SystemVerilog/UVM/.You also learn to meet the timing constraints of the chip using STA(Static Timing Analysis) and Synthesizing the design using synthesizable constructs. The maximum number of VLSI job opportunities are available in the Verification segment. Backend courses mostly deal with the physical design part of the chip which includes Floorplan, Map, Place and route and DFT and ATPG scan insertion and checks for the flip flops. It also includes the physical verification part of the chip, memory characterization, analog layout, and design.

Yes. VLSI is a high growth domain with huge job opportunities. Electronics is the basic knowledge required to get into the VLSI industry. Engineers with Electronics background can enter into VLSI Industry easily. The VLSI Course is helpful for ECE/EEE students to learn and build up the skill set as per the Industry requirement to enter the Chip/IC Design and Verification Domain.

Inexpensive courses with the utmost quality are our unique selling points. You can explore our courses at https://elearn.maven-silicon.com/

We help you with support material to enhance your basic knowledge of Digital electronics and perform your best. Our online Digital electronics course will help you to learn and refresh the complete fundamentals of digital electronics, which are highly needed for any VLSI course. Contact us for more details.

We do have online VLSI courses for engineers like you. You can start learning with our hands-on online VLSI courses which comes with labs, project, reference material. We also connect with live Q&A, doubt clarification sessions and Whatsapp support group. Click here to explore and subscribe https://elearn.maven-silicon.com/ . If you are looking for online VLSI course with Placement support, then you refer our Blended VLSI learning program at https://www.maven-silicon.com/blended-vlsi-design-asic-verification

We always encourage you to join the course along with friends because it motivates you to learn and finish the course at a fast pace. Contact us to know about group discount options.

Yes. It is good to start early. You can explore and subscribe to our online VLSI design methodologies course or our Internship Program. It is a front-end VLSI course that imparts the VLSI Design Flow, Digital Design and RTL programming using Verilog HDL. After completing the online VLSI DM course/Internship Program, you can easily crack college campus interviews or you can also take up our Advanced ASIC Verification course with 100% placement assistance and can avail up to 100% scholarship based on your grades in our Online VLSI Design Course and the scores of technical interview with our experts.

Yes, we have part-time/Weekend VLSI courses for working professionals. They are specially designed to help you strike a balance between your job and learning. Explore VLSI DM and VM part-time course under Part-time VLSI course in Program offerings at our website https://www.maven-silicon.com/systemverilog-uvm-functional-verification-course

Our Job oriented VLSI courses are highly effective and rigorous programs and follow a continuous evaluation scheme. Candidates are evaluated in the courses through lab reports, project reports, practice tests, assignments, technical presentations, and mock interviews. We also have an evaluation program in our Online VLSI courses through quizzes, tests, and assignments.

You do not need to pay extra for the requisite learning material. We do provide free library access and free online VLSI Courses to our trainees enrolled for job oriented courses for reference and support.

Once you complete your online VLSI course you can upgrade to job oriented VLSI Courses with a very good scholarship. We provide 100% placement assistance for the job oriented VLSI Courses. Advanced VLSI Design and Verification [VLSI – RN ] and Advanced ASIC Verification [ VLSI-VM ] are the job oriented VLSI Courses.

Maven Silicon offers customized in-house and onsite corporate VLSI training courses. This program is specially designed for engineers keeping in view the ever-changing demands of the industry. The participants are equipped with the latest tools, techniques, and skills needed to excel as Verification Engineers. Some of our Corporate training VLSI Courses are SystemVerilog HVL, Verilog HDL, Universal Verification Methodology and Assertion based Verification. Click here for more details: https://www.maven-silicon.com/corporate-training

Yes. Our courses will be very useful. We have had many students taking up our course before going to foreign universities for their Master’s program in VLSI. The practical approach of the courses could help them get campus job opportunities and assistantships..

You can opt for online or offline course but you must choose the right mode considering the time you can spend and the flexibility you need. The online course also provides you Live Q&A, doubt clarification, handy technical support and reference material. So, it is a great offering with best of both worlds. You can learn on the go along with your college studies/ regular office hours and upskill yourself. With Maven Silicon’s Online Verification course, you can master VLSI even if you stay in a remote corner of the world.

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Group Discussion Topics for Engineering Students in VLSI

Rahul David
August 3, 2023

VLSI is a crucial domain within the field of engineering, focusing on the design and implementation of integrated circuits. With the rapid advancements in technology, VLSI plays a pivotal role in various sectors such as electronics, communication, healthcare, and automotive industries. As an engineering student specializing in VLSI, it is essential to stay updated with the latest trends and developments in the field. Participating in group discussion topics for engineering students can facilitate knowledge enhancement, foster better communication skills, and encourage the exchange of ideas among peers.

Also read: What is design for testability and why is it important ?

Engaging Group Discussion Topics for Engineering Students

Here are some engaging group discussion topics for engineering students in VLSI:

The Impact of VLSI Design on Modern Electronic Devices

Discuss how VLSI design has revolutionized electronic devices such as smartphones, wearables, and IoT devices. Analyze the challenges faced by VLSI designers in meeting the increasing demand for smaller, faster, and energy-efficient devices.

Future Trends in VLSI Technology

Explore emerging trends in VLSI technology such as 3D IC integration, neuromorphic computing, quantum computing, and nanotechnology. Discuss their potential applications, advantages, and challenges.

Job-Oriented Offline VLSI Courses banner

Role of VLSI Physical Design in Integrated Circuit Manufacturing

Delve into the significance of VLSI physical design in optimizing chip performance, power consumption, and area utilization. Discuss the methodologies and tools employed in physical design and their impact on manufacturing yield.

Introduction to DFT (Design for Testability) in VLSI

Explain the concept of DFT and its importance in ensuring the quality and reliability of integrated circuits. Discuss various DFT techniques such as scan chains, built-in self-test (BIST), and boundary scans.

Overview of VLSI Companies in Bangalore

Explore the prominent VLSI companies in Bangalore , India’s Silicon Valley. Discuss their contributions to the VLSI industry, the nature of their work, and the career opportunities they offer to VLSI engineers.

Choosing the Right VLSI Training Institute in Bangalore

Discuss the factors to consider when selecting a VLSI training institute in Bangalore . Analyze the curriculum, faculty expertise, industry collaborations, hands-on projects, and placement assistance offered by different institutes.

VLSI Companies in Hyderabad and Their Specializations

Explore the VLSI companies in Hyderabad and their specific areas of expertise, such as FPGA design, ASIC development, verification, or physical design. Discuss the opportunities available for VLSI professionals in Hyderabad.

Advantages of Pursuing a VLSI Online Course

Comparison of VLSI Courses Online

Compare different VLSI courses online in terms of their syllabus, teaching methodology, hands-on projects, certification, and industry recognition. Discuss the factors to consider while choosing an online course.

By engaging in group discussions on these topics, engineering students specializing in VLSI can broaden their understanding of the field, exchange ideas, and gain valuable insights from their peers. These discussions will not only enhance their technical knowledge but also improve their communication and critical thinking skills, preparing them for a successful career in the dynamic field of VLSI.

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Intel Cuts $2.2 Billion VLSI Verdict With Appellate Reversal (1)

By Michael Shapiro

A $2.2 billion verdict against Intel Corp. —one of the biggest patent damages awards in US history—was wiped out Monday by the Federal Circuit.

The appeals court said one of the two patents asserted by VLSI Technology LLC against the semiconductor giant wasn’t infringed. The three-judge panel affirmed a Waco, Texas, jury’s finding that a second patent was infringed, but vacated the damages award of $1.5 billion tied to that patent—US Patent No. 7,523,373 .

The jury’s infringement findings and associated damages amounts—$1.5 billion tied to the ‘373 patent and $675 million tied to US Patent No. 7,725,759 —would’ve been blockbuster verdicts by themselves. Taken together the March 2021 verdict was a landmark win for VLSI.

VLSI alleged that Intel used inventions claimed in the ‘373 patent to create power saving for two of its popular microprocessor products. In an opinion for a three-judge panel, Judge Richard G. Taranto of the US Court of Appeals for the Federal Circuit wrote that VLSI’s infringement theory was backed by “ample expert testimony” and “adequate support in Intel’s internal documents,” turned over as part of the case at the US District Court for the Western District of Texas.

But, Taranto continued, the jurors were led astray by VLSI when it came to damages, and specifically the way the firm quantified the power-saving features in Intel’s microprocessors attributable to the VLSI patent.

A VLSI expert, Murali Annavaram, fed the wrong inputs into a regression analysis the company employed to reach its estimate that Intel owed it $1.6 billion for infringement of the patent, Taranto wrote. The opinion says Annavaram erred by relying on a set of data labeled “Core C7" rather than, or in addition to, “Package C7" data. “The difference between the Core C7 residency data and the Package C7 residency data is on its face significant,” it states.

The appeals court didn’t rule on several other arguments raised by Intel taking issue with the district court proceedings, including whether VLSI should’ve been allowed to tell the jury about “concededly noncomparable licenses” that Intel entered into for use of different technologies or patents.

It also said Intel had not “persuasively shown that the regression analysis used” by VLSI is inherently “an improper or unreasonable one.”

The panel reversed the Waco jury’s separate finding that Intel infringed ‘759 patent, which relates to “managing clock speed,” under the “doctrine of equivalents.” That doctrine allows for patent owners to win verdicts when an infringing product differs from a patented invention only in an “insubstantial” way, according to Taranto’s order.

Taranto wrote that “VLSI offered no meaningful testimony” showing how Intel’s products “were substantially the same as the elements” laid out in the patent.

Taranto also wrote that Waco-based federal judge Alan D. Albright erred by preventing Intel from arguing it’s immune from VLSI’s suit because of its 2012 patent licensing agreement with a third company, Finjan Inc.

Intel’s Finjan license wouldn’t have had relevance to VLSI’s lawsuit when it was filed in 2019. VLSI parent Fortress Investment Group LLC, however, acquired Finjan in July 2020. That acquisition, Intel argued in a September 2020 motion, made Finjan and VLSI “affiliates” as that term was defined in its license agreement with Finjan, which in turn meant Intel was licensed to use VLSI’s patents, in addition to Finjan’s.

Albright ruled after the trial concluded that Intel’s license defense had been asserted too late in the case, would cause prejudice to VLSI, and would’ve been futile even if it’d been allowed.

Taranto said that Intel had been diligent in pursuing the defense after it learned of Fortress’s Finjan acquisition. He wrote that Albright was wrong to toss out the defense without a “fuller analysis of the governing law than has yet occurred.” He didn’t rule on whether Intel or VLSI would ultimately prevail with regard to the defense, remanding it back to the Western District court.

Intel was “pleased the appeals court vacated the judgment below,” according to a statement released Monday. It noted that both patents were held invalid in a parallel administrative proceeding at the US Patent and Trademark Office’s Patent Trial and Appeal Board.

“Intel looks forward to making its case to a jury that the VLSI patent sent back to the trial court is also of little value,” it said.

Lawyers for VLSI didn’t immediately respond to a request for comment.

Judges Alan D. Lourie and Timothy B. Dyk joined the opinion.

Intel is represented by Wilmer Cutler Pickering Hale and Dorr LLP. Irell & Manella LLP and MoloLamken LLP represent VLSI.

The case is VLSI Tech. LLC v. Intel Corp. , Fed. Cir., 22-1906, aff’d in part, rev’d in part, and vac’d 12/4/23.

To contact the reporter on this story: Michael Shapiro in Washington at [email protected]

To contact the editor responsible for this story: Adam M. Taylor at [email protected]

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Protecting Navajo children with asthma: A case study

A young Navajo girl holding plastic medical equipment

Joncita Todechine, a mother of four who lives on the Navajo Nation, knows all too well what can trigger asthma symptoms in her daughter Ashley. But she didn’t always. She recalls a time in 2013, living in Phoenix and attending medical assistant school, when she rushed her then-three-year-old to the Indian Medical Center.

“She was really sick,” Todechine said. “She was fevering, coughing, and had shortness of breath. We had no idea what was wrong.”

Ashley was admitted to the hospital and stayed for an entire week before the doctors could make a diagnosis of asthma. Now a thriving 13-year-old, Ashley loves gaming, social media, and riding on her hoverboard. These days she lives on the Navajo reservation with her family, who moved there shortly after her mother finished school. For the most part, she keeps her asthma under control by taking medication and doing her best to avoid her asthma triggers.

But that can be challenging.

On the Navajo Nation, there are many asthma triggers. The semi-arid environment is plagued by drought, so on windy days, the gusts kick up ever-present dust and sand into the air. Shuttered coal-fired powerplants dot the landscape and, though they are closed, residual soot still dirties the air. Uranium and other heavy metals contaminate the landscape, and people breathe diesel fumes from the buses that take children to and from school every day. The many dogs and livestock roaming the reservation carry other allergens.

“And that’s just the outdoor pollution,” said Bruce Bender, Ph.D., professor in the pediatrics department at National Jewish Health in Denver, Colorado. “Seventy percent of households heat with indoor stoves that burn wood or charcoal and can leak a lot of smoke into the air.”

Bender would know. He’s co-project leader of an NHLBI-funded project focused on reducing health disparities in children living on the Navajo Nation, and he’s studied some of the factors that make those disparities worse. He’s also looked at the health data overall and found that while Native adults suffer from higher rates of chronic conditions like cardiovascular diseases and diabetes, it’s asthma that remains one of the most common chronic diseases in children. Some 18% of children on the Navajo reservation have it, compared to 10.2% of children nationwide.

“Asthma can be incredibly scary for children and their families, especially those who cannot get emergency care easily or quickly,” said Michelle Freemer, M.D., M.P.H., director of the asthma program in NHLBI’s Division of Lung Diseases.

The Navajo Nation extends across more than 27,000 square miles, making it the largest Native land area in the U.S. "For families of children with asthma, the distances and travel conditions on the reservation may add challenges,” said Freemer. “The investigators partnered with the community to find solutions that work where they live, not simply provide asthma care that has been shown to work in other places."

A local solution

Bender and his colleague, Lynn B. Gerald, Ph.D., M.S.P.H., assistant vice chancellor for population health at the University of Illinois-Chicago, started a large-scale effort to teach educators, children, their families, and local medical providers on the Navajo Nation how to identify an asthma attack and what to do in an emergency. (Gerald had worked previously at the University of Arizona and had gained a wealth of knowledge from the university's Native collaborators.) The program rolled out in three Arizona communities on the reservation: Tuba City, Chinle, and Fort Defiance. Combined, these towns represent 43% of the Navajo Nation population and are home to more than 8,000 children with asthma.

But before they began, the investigators knew they needed to build relationships with the Navajo people – who refer to themselves as Diné – as the community’s prior experiences with non-Native researchers had left them skeptical. The research team began by ensuring the program was tailored to the needs and wishes of the community itself.

“The Navajo Nation human research review board is very careful and thorough,” Bender said. “They’re protecting their population. We had to earn their trust.”

Once the investigators got approval, they hit the ground running, starting in Tuba City. In the hospitals, the research team provided tools for medical professionals, using self-directed online learning and in-person workshops, to increase their use of practices that have been shown to be important in asthma care.

In the schools, the investigators provided education using the American Lung Association’s Asthma Basics and Open Airways for Schools® training, to teach school staff about asthma, its triggers, and what to do when a child is having an attack and to teach Diné children how to manage their asthma.

Using a “train-the-trainer” model, school staff, community health workers, respiratory therapists, and pharmacists became students and then instructors. This made it possible for the Diné participants to teach additional staff, ensuring the community can sustain the program after the research funding ends.

Still, there was another urgent need that Bender and Gerald realized had not been addressed. “Less than 15% of children with asthma actually have an inhaler at school when they need it,” Gerald said. In response, the team helped start a program in two of the three communities that provided stock inhalers to schools for children who need them.

A global threat

After starting the program in Tuba City as planned and spending a year there, the research team moved their focus to Chinle. The goal was to be able to compare how well the program worked in each of the three communities. But a global pandemic had other plans.

“The COVID-19 pandemic hit right in the middle of our time in Chinle,” Bender said. “After that, we weren’t allowed on the reservation for two years.”

While the pandemic changed life for all Americans, it devastated many Native communities. Schools closed and medical clinics focused on emergencies. The research team pivoted: they continued some training virtually and were able to keep learning from families about their needs, especially using the Diné members of the research team who were on the reservation.

Taking stock

Today, despite the challenges of the pandemic, all three communities have completed the original program, and 439 Diné members have been trained to identify asthma and its triggers. Yet the work is far from over. The investigators are analyzing the data they collected. “Particularly important is returning the results to the community,” Gerald said. As soon as they are ready, she said, they will be meeting with the school boards and health boards and joining community meetings to share them.

Freemer said that all the materials the researchers developed through their NHLBI funding are available to the community and have also been shared with those at the Indian Health Service leading the Asthma Control in Tribal Communities program.

“The researchers also took the opportunity to build research capacity,” she said. They developed an agreement with Diné College, the only four-year college on the reservation, to provide training through their Summer Research Experience Program. “In that program, students learned about research and were able to readily reach the families who appreciated the interactions with Diné research team members.”

Todechine said knowing that her child will be cared for if the worst happens has given her peace of mind. “Now the school systems have their own asthma alert systems that the employees and even the bus drivers take part in,” she said. “For me, I feel safer for her to be at school without me.”

Resources:

Learn more about Asthma in Our Communities with specific resources for American Indians.

Lessons from Beyoncé on Navigating Exclusion

Ella F. Washington,
Hildana Haileyesus,
Laura Morgan Roberts

The star’s path from CMA Awards backlash to Cowboy Carter is a case study in strategic response.

In 2016, Beyoncé’s performance at the CMA Awards sparked backlash from fans complaining about everything from her attire to her lack of connection to the genre. This year, she released her first country album, which debuted at number one on the Billboard 200. Her actions over the past eight years have been a case study in how to navigate workplace exclusion. As a first step, it often makes sense to exit the conversation and wait for a better moment to respond. Then, work behind the scenes, ideally with collaborators, to push for change. Finally, consider focusing on your own authenticity and strengths to create your own lane within your organization or outside it.

Beyoncé, the globally revered singer, songwriter, and entrepreneur, last month released her new album Cowboy Carter. However, this project is much more than another musical release from a leading star. It offers a case study in how to navigate workplace exclusion.

Ella F. Washington is an organizational psychologist; the founder and CEO of Ellavate Solutions, a DEI strategy firm; and a professor of practice at Georgetown University’s McDonough School of Business. She is the author of The Necessary Journey: Making Real Progress on Equity and Inclusion (HBR Press, November 2022) and Unspoken: A Guide to Cracking the Hidden Corporate Code (Forbes Books, May 2024).
Hildana Haileyesus is a DEI consultant at Ellavate Solutions with a background in training and facilitation, client strategy, and research. She has worked across higher education and business and applies a sociological lens to equity-driven change efforts.
Laura Morgan Roberts is a Frank M. Sands Sr. Associate Professor of Business Administration at the University of Virginia’s Darden School of Business. She is an organizational psychologist and the coeditor of Race, Work and Leadership: New Perspectives on the Black Experience (Harvard Business Review Press, 2019).

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Published: 22 April 2024

Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study

Kannan Sridharan 1 &
Reginald P. Sequeira 1

BMC Medical Education volume 24 , Article number: 431 ( 2024 ) Cite this article

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Artificial intelligence (AI) tools are designed to create or generate content from their trained parameters using an online conversational interface. AI has opened new avenues in redefining the role boundaries of teachers and learners and has the potential to impact the teaching-learning process.

In this descriptive proof-of- concept cross-sectional study we have explored the application of three generative AI tools on drug treatment of hypertension theme to generate: (1) specific learning outcomes (SLOs); (2) test items (MCQs- A type and case cluster; SAQs; OSPE); (3) test standard-setting parameters for medical students.

Analysis of AI-generated output showed profound homology but divergence in quality and responsiveness to refining search queries. The SLOs identified key domains of antihypertensive pharmacology and therapeutics relevant to stages of the medical program, stated with appropriate action verbs as per Bloom’s taxonomy. Test items often had clinical vignettes aligned with the key domain stated in search queries. Some test items related to A-type MCQs had construction defects, multiple correct answers, and dubious appropriateness to the learner’s stage. ChatGPT generated explanations for test items, this enhancing usefulness to support self-study by learners. Integrated case-cluster items had focused clinical case description vignettes, integration across disciplines, and targeted higher levels of competencies. The response of AI tools on standard-setting varied. Individual questions for each SAQ clinical scenario were mostly open-ended. The AI-generated OSPE test items were appropriate for the learner’s stage and identified relevant pharmacotherapeutic issues. The model answers supplied for both SAQs and OSPEs can aid course instructors in planning classroom lessons, identifying suitable instructional methods, establishing rubrics for grading, and for learners as a study guide. Key lessons learnt for improving AI-generated test item quality are outlined.

Conclusions

AI tools are useful adjuncts to plan instructional methods, identify themes for test blueprinting, generate test items, and guide test standard-setting appropriate to learners’ stage in the medical program. However, experts need to review the content validity of AI-generated output. We expect AIs to influence the medical education landscape to empower learners, and to align competencies with curriculum implementation. AI literacy is an essential competency for health professionals.

Peer Review reports

Artificial intelligence (AI) has great potential to revolutionize the field of medical education from curricular conception to assessment [ 1 ]. AIs used in medical education are mostly generative AI large language models that were developed and validated based on billions to trillions of parameters [ 2 ]. AIs hold promise in the incorporation of history-taking, assessment, diagnosis, and management of various disorders [ 3 ]. While applications of AIs in undergraduate medical training are being explored, huge ethical challenges remain in terms of data collection, maintaining anonymity, consent, and ownership of the provided data [ 4 ]. AIs hold a promising role amongst learners because they can deliver a personalized learning experience by tracking their progress and providing real-time feedback, thereby enhancing their understanding in the areas they are finding difficult [ 5 ]. Consequently, a recent survey has shown that medical students have expressed their interest in acquiring competencies related to the use of AIs in healthcare during their undergraduate medical training [ 6 ].

Pharmacology and Therapeutics (P & T) is a core discipline embedded in the undergraduate medical curriculum, mostly in the pre-clerkship phase. However, the application of therapeutic principles forms one of the key learning objectives during the clerkship phase of the undergraduate medical career. Student assessment in pharmacology & therapeutics (P&T) is with test items such as multiple-choice questions (MCQs), integrated case cluster questions, short answer questions (SAQs), and objective structured practical examination (OSPE) in the undergraduate medical curriculum. It has been argued that AIs possess the ability to communicate an idea more creatively than humans [ 7 ]. It is imperative that with access to billions of trillions of datasets the AI platforms hold promise in playing a crucial role in the conception of various test items related to any of the disciplines in the undergraduate medical curriculum. Additionally, AIs provide an optimized curriculum for a program/course/topic addressing multidimensional problems [ 8 ], although robust evidence for this claim is lacking.

The existing literature has evaluated the knowledge, attitude, and perceptions of adopting AI in medical education. Integration of AIs in medical education is the need of the hour in all health professional education. However, the academic medical fraternity facing challenges in the incorporation of AIs in the medical curriculum due to factors such as inadequate grounding in data analytics, lack of high-quality firm evidence favoring the utility of AIs in medical education, and lack of funding [ 9 ]. Open-access AI platforms are available free to users without any restrictions. Hence, as a proof-of-concept, we chose to explore the utility of three AI platforms to identify specific learning objectives (SLOs) related to pharmacology discipline in the management of hypertension for medical students at different stages of their medical training.

Study design and ethics

The present study is observational, cross-sectional in design, conducted in the Department of Pharmacology & Therapeutics, College of Medicine and Medical Sciences, Arabian Gulf University, Kingdom of Bahrain, between April and August 2023. Ethical Committee approval was not sought given the nature of this study that neither had any interaction with humans, nor collection of any personal data was involved.

Study procedure

We conducted the present study in May-June 2023 with the Poe© chatbot interface created by Quora© that provides access to the following three AI platforms:

Sage Poe [ 10 ]: A generative AI search engine developed by Anthropic © that conceives a response based on the written input provided. Quora has renamed Sage Poe as Assistant © from July 2023 onwards.

Claude-Instant [ 11 ]: A retrieval-based AI search engine developed by Anthropic © that collates a response based on pre-written responses amongst the existing databases.

ChatGPT version 3.5 [ 12 ]: A generative architecture-based AI search engine developed by OpenAI © trained on large and diverse datasets.

We queried the chatbots to generate SLOs, A-type MCQs, integrated case cluster MCQs, integrated SAQs, and OSPE test items in the domain of systemic hypertension related to the P&T discipline. Separate prompts were used to generate outputs for pre-clerkship (preclinical) phase students, and at the time of graduation (before starting residency programs). Additionally, we have also evaluated the ability of these AI platforms to estimate the proportion of students correctly answering these test items. We used the following queries for each of these objectives:

Specific learning objectives

Can you generate specific learning objectives in the pharmacology discipline relevant to undergraduate medical students during their pre-clerkship phase related to anti-hypertensive drugs?

Can you generate specific learning objectives in the pharmacology discipline relevant to undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

A-type MCQs

In the initial query used for A-type of item, we specified the domains (such as the mechanism of action, pharmacokinetics, adverse reactions, and indications) so that a sample of test items generated without any theme-related clutter, shown below:

Write 20 single best answer MCQs with 5 choices related to anti-hypertensive drugs for undergraduate medical students during the pre-clerkship phase of which 5 MCQs should be related to mechanism of action, 5 MCQs related to pharmacokinetics, 5 MCQs related to adverse reactions, and 5 MCQs should be related to indications.

The MCQs generated with the above search query were not based on clinical vignettes. We queried again to generate MCQs using clinical vignettes specifically because most medical schools have adopted problem-based learning (PBL) in their medical curriculum.

Write 20 single best answer MCQs with 5 choices related to anti-hypertensive drugs for undergraduate medical students during the pre-clerkship phase using a clinical vignette for each MCQ of which 5 MCQs should be related to the mechanism of action, 5 MCQs related to pharmacokinetics, 5 MCQs related to adverse reactions, and 5 MCQs should be related to indications.

We attempted to explore whether AI platforms can provide useful guidance on standard-setting. Hence, we used the following search query.

Can you do a simulation with 100 undergraduate medical students to take the above questions and let me know what percentage of students got each MCQ correct?

Integrated case cluster MCQs

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students during the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette. Please do not include ‘none of the above’ as the choice. (This modified search query was used because test items with ‘None of the above’ option were generated with the previous search query).

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students at the time of graduation integrating pharmacology and physiology related to systemic hypertension with a case vignette.

Integrated short answer questions

Write a short answer question scenario with difficult questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Write a short answer question scenario with moderately difficult questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Write a short answer question scenario with questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students at the time of graduation with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises for the assessment of undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises containing appropriate instructions for the patients for the assessment of undergraduate medical students during their pre-clerkship phase related to anti-hypertensive drugs?

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises containing appropriate instructions for the patients for the assessment of undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

Both authors independently evaluated the AI-generated outputs, and a consensus was reached. We cross-checked the veracity of answers suggested by AIs as per the Joint National Commission Guidelines (JNC-8) and Goodman and Gilman’s The Pharmacological Basis of Therapeutics (2023), a reference textbook [ 13 , 14 ]. Errors in the A-type MCQs were categorized as item construction defects, multiple correct answers, and uncertain appropriateness to the learner’s level. Test items in the integrated case cluster MCQs, SAQs and OSPEs were evaluated with the Preliminary Conceptual Framework for Establishing Content Validity of AI-Generated Test Items based on the following domains: technical accuracy, comprehensiveness, education level, and lack of construction defects (Table 1 ). The responses were categorized as complete and deficient for each domain.

The pre-clerkship phase SLOs identified by Sage Poe, Claude-Instant, and ChatGPT are listed in the electronic supplementary materials 1 – 3 , respectively. In general, a broad homology in SLOs generated by the three AI platforms was observed. All AI platforms identified appropriate action verbs as per Bloom’s taxonomy to state the SLO; action verbs such as describe, explain, recognize, discuss, identify, recommend, and interpret are used to state the learning outcome. The specific, measurable, achievable, relevant, time-bound (SMART) SLOs generated by each AI platform slightly varied. All key domains of antihypertensive pharmacology to be achieved during the pre-clerkship (pre-clinical) years were relevant for graduating doctors. The SLOs addressed current JNC Treatment Guidelines recommended classes of antihypertensive drugs, the mechanism of action, pharmacokinetics, adverse effects, indications/contraindications, dosage adjustments, monitoring therapy, and principles of monotherapy and combination therapy.

The SLOs to be achieved by undergraduate medical students at the time of graduation identified by Sage Poe, Claude-Instant, and ChatGPT listed in electronic supplementary materials 4 – 6 , respectively. The identified SLOs emphasize the application of pharmacology knowledge within a clinical context, focusing on competencies needed to function independently in early residency stages. These SLOs go beyond knowledge recall and mechanisms of action to encompass competencies related to clinical problem-solving, rational prescribing, and holistic patient management. The SLOs generated require higher cognitive ability of the learner: action verbs such as demonstrate, apply, evaluate, analyze, develop, justify, recommend, interpret, manage, adjust, educate, refer, design, initiate & titrate were frequently used.

The MCQs for the pre-clerkship phase identified by Sage Poe, Claude-Instant, and ChatGPT listed in the electronic supplementary materials 7 – 9 , respectively, and those identified with the search query based on the clinical vignette in electronic supplementary materials ( 10 – 12 ).

All MCQs generated by the AIs in each of the four domains specified [mechanism of action (MOA); pharmacokinetics; adverse drug reactions (ADRs), and indications for antihypertensive drugs] are quality test items with potential content validity. The test items on MOA generated by Sage Poe included themes such as renin-angiotensin-aldosterone (RAAS) system, beta-adrenergic blockers (BB), calcium channel blockers (CCB), potassium channel openers, and centrally acting antihypertensives; on pharmacokinetics included high oral bioavailability/metabolism in liver [angiotensin receptor blocker (ARB)-losartan], long half-life and renal elimination [angiotensin converting enzyme inhibitors (ACEI)-lisinopril], metabolism by both liver and kidney (beta-blocker (BB)-metoprolol], rapid onset- short duration of action (direct vasodilator-hydralazine), and long-acting transdermal drug delivery (centrally acting-clonidine). Regarding the ADR theme, dry cough, angioedema, and hyperkalemia by ACEIs in susceptible patients, reflex tachycardia by CCB/amlodipine, and orthostatic hypotension by CCB/verapamil addressed. Clinical indications included the drug of choice for hypertensive patients with concomitant comorbidity such as diabetics (ACEI-lisinopril), heart failure and low ejection fraction (BB-carvedilol), hypertensive urgency/emergency (alpha cum beta receptor blocker-labetalol), stroke in patients with history recurrent stroke or transient ischemic attack (ARB-losartan), and preeclampsia (methyldopa).

Almost similar themes under each domain were identified by the Claude-Instant AI platform with few notable exceptions: hydrochlorothiazide (instead of clonidine) in MOA and pharmacokinetics domains, respectively; under the ADR domain ankle edema/ amlodipine, sexual dysfunction and fatigue in male due to alpha-1 receptor blocker; under clinical indications the best initial monotherapy for clinical scenarios such as a 55-year old male with Stage-2 hypertension; a 75-year-old man Stage 1 hypertension; a 35-year-old man with Stage I hypertension working on night shifts; and a 40-year-old man with stage 1 hypertension and hyperlipidemia.

As with Claude-Instant AI, ChatGPT-generated test items on MOA were mostly similar. However, under the pharmacokinetic domain, immediate- and extended-release metoprolol, the effect of food to enhance the oral bioavailability of ramipril, and the highest oral bioavailability of amlodipine compared to other commonly used antihypertensives were the themes identified. Whereas the other ADR themes remained similar, constipation due to verapamil was a new theme addressed. Notably, in this test item, amlodipine was an option that increased the difficulty of this test item because amlodipine therapy is also associated with constipation, albeit to a lesser extent, compared to verapamil. In the clinical indication domain, the case description asking “most commonly used in the treatment of hypertension and heart failure” is controversial because the options listed included losartan, ramipril, and hydrochlorothiazide but the suggested correct answer was ramipril. This is a good example to stress the importance of vetting the AI-generated MCQ by experts for content validity and to assure robust psychometrics. The MCQ on the most used drug in the treatment of “hypertension and diabetic nephropathy” is more explicit as opposed to “hypertension and diabetes” by Claude-Instant because the therapeutic concept of reducing or delaying nephropathy must be distinguished from prevention of nephropathy, although either an ACEI or ARB is the drug of choice for both indications.

It is important to align student assessment to the curriculum; in the PBL curriculum, MCQs with a clinical vignette are preferred. The modification of the query specifying the search to generate MCQs with a clinical vignette on domains specified previously gave appropriate output by all three AI platforms evaluated (Sage Poe; Claude- Instant; Chat GPT). The scenarios generated had a good clinical fidelity and educational fit for the pre-clerkship student perspective.

The errors observed with AI outputs on the A-type MCQs are summarized in Table 2 . No significant pattern was observed except that Claude-Instant© generated test items in a stereotyped format such as the same choices for all test items related to pharmacokinetics and indications, and all the test items in the ADR domain are linked to the mechanisms of action of drugs. This illustrates the importance of reviewing AI-generated test items by content experts for content validity to ensure alignment with evidence-based medicine and up-to-date treatment guidelines.

The test items generated by ChatGPT had the advantage of explanations supplied rendering these more useful for learners to support self-study. The following examples illustrate this assertion: “ A patient with hypertension is started on a medication that works by blocking beta-1 receptors in the heart (metoprolol)”. Metoprolol is a beta blocker that works by blocking beta-1 receptors in the heart, which reduces heart rate and cardiac output, resulting in a decrease in blood pressure. However, this explanation is incomplete because there is no mention of other less important mechanisms, of beta receptor blockers on renin release. Also, these MCQs were mostly recall type: Which of the following medications is known to have a significant first-pass effect? The explanation reads: propranolol is known to have a significant first pass-effect, meaning that a large portion of the drug is metabolized by the liver before it reaches systemic circulation. Losartan, amlodipine, ramipril, and hydrochlorothiazide do not have significant first-pass effect. However, it is also important to extend the explanation further by stating that the first-pass effect of propranolol does not lead to total loss of pharmacological activity because the metabolite hydroxy propranolol also has potent beta-blocking activity. Another MCQ test item had a construction defect: “A patient with hypertension is started on a medication that can cause photosensitivity. Which of the following medications is most likely responsible?” Options included: losartan, amlodipine, ramipril, hydrochlorothiazide, hydrochlorothiazide/triamterene. The explanation states that hydrochlorothiazide (HCTZ) can cause photosensitivity, which is increased sensitivity to sunlight that can cause skin rashes and sunburns. Losartan, amlodipine, ramipril, and HCTZ/triamterene are less likely to cause photosensitivity. However, it is irrational to claim that HCTZ/triamterene, one of the wrong options, is less likely to cause photosensitivity. The difficulty index of such test items is likely unacceptable in high-stake tests to make equitable psychometric decisions.

The responses from Sage Poe, Claude-Instant, and ChatGPT on guidance for standard-setting methods are listed in the electronic supplementary materials 13 – 15 . Sage Poe offered this information with the caveat that “the results of this simulation were purely hypothetical and do not reflect the actual performance of any real students”. The simulation estimates of examinees correctly answering test items ranged from 60 to 90% for the mechanism of action, 65–85% for pharmacokinetics, 60–80% for adverse reactions, and 75–90% for clinical indications of antihypertensives.

In contrast, Claude-Instant admitted that “Performing a full simulation with 100 medical students is beyond the scope of what I can do as an AI assistant. I can generate sample MCQ questions and answers, but I do not have access to a population of medical students to administer the questions. A true simulation that administers questions to real students and analyzes the results would require access to a database of medical students, a platform to deliver the questions, and code to analyze the responses. As an AI assistant, I lack these capabilities. While I cannot perform a full simulation, here are some considerations for administering MCQ questions to medical students: provide details on the purpose and instructions for the test to ensure students understand what is expected out of them; consider including some easier questions in addition to more challenging ones to boost students’ confidence and motivation; avoid ambiguous wording and ensure each question has only one unambiguously correct answer option; when analyzing results, look for questions factors that may have impacted performance, like unclear wording or answer options that are too similar. Focusing on weak questions can help improve future tests; compare performance across different student groups to identify areas where more teaching may be needed; provide detailed answer explanations and review sessions after the test to help students learn from any errors and reinforce correct concepts”. However, ChatGPT offered the simulation result estimates for the 20 MCQ on antihypertensive drugs: mechanism of action ranged from 67 to 92%, pharmacokinetics 63–86%, adverse effects 65–82%, and clinical indications 64–89%. Furthermore, it also stated that “Overall, the performance of the students was quite good, with most questions having a response rate of over 70%. However, there were some questions where the projected correct response rate was rather low, such as Question #5 (Mechanism of Action of Hydrochlorothiazide; see Electronic Supplementary Material 12 ) and Question 18 (Indications for Verapamil; see Electronic Supplementary Material 10 ). This may suggest areas where students need more focused education or review.”

We asked AI assistants to generate 20 integrated case cluster MCQs with 2 test items in each cluster with five options for undergraduate medical students in the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette and the responses by Sage Poe, Claude-Instant, and ChatGPT are listed in the electronic supplementary materials ( 16 – 18 ). In all instances, the test items generated had focused case descriptions in the form of a clinical vignette, and horizontal integration across the pathophysiology of hypertension and pharmacology of antihypertensive drugs. These test items mostly targeted the ‘knows (knowledge)’ or ‘knows how (competence)’ level on Miller’s pyramid and are suitable for assessing the clinical competence of pre-clerkship medical students, especially in an integrated PBL curriculum. Both the AI assistants generated excellent clinical vignettes and themes; however, most of the cluster MCQs by ChatGPT had “None of the above” as an option, which is often considered a test item construction flaw. Notwithstanding these limitations, case cluster integrated test items are valuable for learners to integrate their knowledge of different basic medical sciences and their application to clinical sciences. This integrated approach can be used for both instructional and student assessment purposes to make the course more meaningful. Indeed, one of the basic tenets of PBL is curriculum integration.

In the next query, we asked AI assistants to write integrated case cluster MCQs with 2 test items in each cluster with 5 options for undergraduate medical students at the time of graduation integrating pharmacology and physiology of hypertension with a case vignette. The responses are listed in the electronic supplementary materials 19 – 21 . Claude-Instant has provided only partial responses to both the queries. Overall, the information provided in case vignettes requires the learner to interpret the clinical information and make therapeutic decisions. Such test items are appropriate for assessing the minimum competency of medical school graduates to start internship or residency programs; these MCQs are generally comparable to test items used in USMLE Part 2. The evaluation of the integrated case cluster test items provided by all the AI platforms is summarized in Table 3 . In general, the AI outputs had at least one construction defect, and did not include all the core sub-topics to a sufficient degree.

When asked to simulate the above-generated test items to 100 undergraduate medical students at the time of graduation and provide a percentage of students who are likely to correctly answer each item, the AI assistant responded:

“ As an AI language model, I cannot simulate the responses of 100 undergraduate medical students likely to obtain the correct answer for each question. However, I can provide some general information about the difficulty level of each question based on the complexity of the concepts being tested and the level of integration between pharmacology and physiology ”.

AI assistant went on to clarify that questions that require knowledge of basic pharmacology principles, such as the mechanism of action of specific drugs, are likely to be easier for students to answer correctly. Test items that require an understanding of the physiological mechanisms underlying hypertension and correlating with symptoms are likely to be more challenging for students. The AI assistant sorted these test items into two categories accordingly. Overall, the difficulty level of the test item is based on the level of integration between pharmacology and pathophysiology. Test items that require an understanding of both pharmacological and physiological mechanisms are likely to be more challenging for students requiring a strong foundation in both pharmacology and physiology concepts to be able to correctly answer integrated case-cluster MCQs.

Short answer questions

The responses to a search query on generating SAQs appropriate to the pre-clerkship phase Sage Poe, Claude-Instant, and ChatGPT generated items are listed in the electronic supplementary materials 22 – 24 for difficult questions and 25–27 for moderately difficult questions.

It is apparent from these case vignette descriptions that the short answer question format varied. Accordingly, the scope for asking individual questions for each scenario is open-ended. In all instances, model answers are supplied which are helpful for the course instructor to plan classroom lessons, identify appropriate instructional methods, and establish rubrics for grading the answer scripts, and as a study guide for students.

We then wanted to see to what extent AI can differentiate the difficulty of the SAQ by replacing the search term “difficult” with “moderately difficult” in the above search prompt: the changes in the revised case scenarios are substantial. Perhaps the context of learning and practice (and the level of the student in the MD/medical program) may determine the difficulty level of SAQ generated. It is worth noting that on changing the search from cardiology to internal medicine rotation in Sage Poe the case description also changed. Thus, it is essential to select an appropriate AI assistant, perhaps by trial and error, to generate quality SAQs. Most of the individual questions tested stand-alone knowledge and did not require students to demonstrate integration.

The responses of Sage Poe, Claude-Instant, and ChatGPT for the search query to generate SAQs at the time of graduation are listed in the electronic supplementary materials 28 – 30 . It is interesting to note how AI assistants considered the stage of the learner while generating the SAQ. The response by Sage Poe is illustrative for comparison. “You are a newly graduated medical student who is working in a hospital” versus “You are a medical student in your pre-clerkship.”

Some questions were retained, deleted, or modified to align with competency appropriate to the context (Electronic Supplementary Materials 28 – 30 ). Overall, the test items at both levels from all AI platforms were technically accurate and thorough addressing the topics related to different disciplines (Table 3 ). The differences in learning objective transition are summarized in Table 4 . A comparison of learning objectives revealed that almost all objectives remained the same except for a few (Table 5 ).

A similar trend was apparent with test items generated by other AI assistants, such as ChatGPT. The contrasting differences in questions are illustrated by the vertical integration of basic sciences and clinical sciences (Table 6 ).

Taken together, these in-depth qualitative comparisons suggest that AI assistants such as Sage Poe and ChatGPT consider the learner’s stage of training in designing test items, learning outcomes, and answers expected from the examinee. It is critical to state the search query explicitly to generate quality output by AI assistants.

The OSPE test items generated by Claude-Instant and ChatGPT appropriate to the pre-clerkship phase (without mentioning “appropriate instructions for the patients”) are listed in the electronic supplementary materials 31 and 32 and with patient instructions on the electronic supplementary materials 33 and 34 . For reasons unknown, Sage Poe did not provide any response to this search query.

The five OSPE items generated were suitable to assess the prescription writing competency of pre-clerkship medical students. The clinical scenarios identified by the three AI platforms were comparable; these scenarios include patients with hypertension and impaired glucose tolerance in a 65-year-old male, hypertension with chronic kidney disease (CKD) in a 55-year-old woman, resistant hypertension with obstructive sleep apnea in a 45-year-old man, and gestational hypertension at 32 weeks in a 35-year-old (Claude-Instant AI). Incorporating appropriate instructions facilitates the learner’s ability to educate patients and maximize safe and effective therapy. The OSPE item required students to write a prescription with guidance to start conservatively, choose an appropriate antihypertensive drug class (drug) based on the patients’ profile, specifying drug name, dose, dosing frequency, drug quantity to be dispensed, patient name, date, refill, and caution as appropriate, in addition to prescribers’ name, signature, and license number. In contrast, ChatGPT identified clinical scenarios to include patients with hypertension and CKD, hypertension and bronchial asthma, gestational diabetes, hypertension and heart failure, and hypertension and gout (ChatGPT). Guidance for dosage titration, warnings to be aware, safety monitoring, and frequency of follow-up and dose adjustment. These test items are designed to assess learners’ knowledge of P & T of antihypertensives, as well as their ability to provide appropriate instructions to patients. These clinical scenarios for writing prescriptions assess students’ ability to choose an appropriate drug class, write prescriptions with proper labeling and dosing, reflect drug safety profiles, and risk factors, and make modifications to meet the requirements of special populations. The prescription is required to state the drug name, dose, dosing frequency, patient name, date, refills, and cautions or instructions as needed. A conservative starting dose, once or twice daily dosing frequency based on the drug, and instructions to titrate the dose slowly if required.

The responses from Claude-Instant and ChatGPT for the search query related to generating OSPE test items at the time of graduation are listed in electronic supplementary materials 35 and 36 . In contrast to the pre-clerkship phase, OSPEs generated for graduating doctors’ competence assessed more advanced drug therapy comprehension. For example, writing a prescription for:

(1) A 65-year- old male with resistant hypertension and CKD stage 3 to optimize antihypertensive regimen required the answer to include starting ACEI and diuretic, titrating the dosage over two weeks, considering adding spironolactone or substituting ACEI with an ARB, and need to closely monitor serum electrolytes and kidney function closely.

(2) A 55-year-old woman with hypertension and paroxysmal arrhythmia required the answer to include switching ACEI to ARB due to cough, adding a CCB or beta blocker for rate control needs, and adjusting the dosage slowly and monitoring for side effects.

(3) A 45-year-old man with masked hypertension and obstructive sleep apnea require adding a centrally acting antihypertensive at bedtime and increasing dosage as needed based on home blood pressure monitoring and refer to CPAP if not already using one.

(4) A 75-year-old woman with isolated systolic hypertension and autonomic dysfunction to require stopping diuretic and switching to an alpha blocker, upward dosage adjustment and combining with other antihypertensives as needed based on postural blood pressure changes and symptoms.

(5) A 35-year-old pregnant woman with preeclampsia at 29 weeks require doubling methyldopa dose and consider adding labetalol or nifedipine based on severity and educate on signs of worsening and to follow-up immediately for any concerning symptoms.

These case scenarios are designed to assess the ability of the learner to comprehend the complexity of antihypertensive regimens, make evidence-based regimen adjustments, prescribe multidrug combinations based on therapeutic response and tolerability, monitor complex patients for complications, and educate patients about warning signs and follow-up.

A similar output was provided by ChatGPT, with clinical scenarios such as prescribing for patients with hypertension and myocardial infarction; hypertension and chronic obstructive pulmonary airway disease (COPD); hypertension and a history of angina; hypertension and a history of stroke, and hypertension and advanced renal failure. In these cases, wherever appropriate, pharmacotherapeutic issues like taking ramipril after food to reduce side effects such as giddiness; selection of the most appropriate beta-blocker such as nebivolol in patients with COPD comorbidity; the importance of taking amlodipine at the same time every day with or without food; preference for telmisartan among other ARBs in stroke; choosing furosemide in patients with hypertension and edema and taking the medication with food to reduce the risk of gastrointestinal adverse effect are stressed.

The AI outputs on OSPE test times were observed to be technically accurate, thorough in addressing core sub-topics suitable for the learner’s level and did not have any construction defects (Table 3 ). Both AIs provided the model answers with explanatory notes. This facilitates the use of such OSPEs for self-assessment by learners for formative assessment purposes. The detailed instructions are helpful in creating optimized therapy regimens, and designing evidence-based regimens, to provide appropriate instructions to patients with complex medical histories. One can rely on multiple AI sources to identify, shortlist required case scenarios, and OSPE items, and seek guidance on expected model answers with explanations. The model answer guidance for antihypertensive drug classes is more appropriate (rather than a specific drug of a given class) from a teaching/learning perspective. We believe that these scenarios can be refined further by providing a focused case history along with relevant clinical and laboratory data to enhance clinical fidelity and bring a closer fit to the competency framework.

In the present study, AI tools have generated SLOs that comply with the current principles of medical education [ 15 ]. AI tools are valuable in constructing SLOs and so are especially useful for medical fraternities where training in medical education is perceived as inadequate, more so in the early stages of their academic career. Data suggests that only a third of academics in medical schools have formal training in medical education [ 16 ] which is a limitation. Thus, the credibility of alternatives, such as the AIs, is evaluated to generate appropriate course learning outcomes.

We observed that the AI platforms in the present study generated quality test items suitable for different types of assessment purposes. The AI-generated outputs were similar with minor variation. We have used generative AIs in the present study that could generate new content from their training dataset [ 17 ]. Problem-based and interactive learning approaches are referred to as “bottom-up” where learners obtain first-hand experience in solving the cases first and then indulge in discussion with the educators to refine their understanding and critical thinking skills [ 18 ]. We suggest that AI tools can be useful for this approach for imparting the core knowledge and skills related to Pharmacology and Therapeutics to undergraduate medical students. A recent scoping review evaluating the barriers to writing quality test items based on 13 studies has concluded that motivation, time constraints, and scheduling were the most common [ 19 ]. AI tools can be valuable considering the quick generation of quality test items and time management. However, as observed in the present study, the AI-generated test items nevertheless require scrutiny by faculty members for content validity. Moreover, it is important to train faculty in AI technology-assisted teaching and learning. The General Medical Council recommends taking every opportunity to raise the profile of teaching in medical schools [ 20 ]. Hence, both the academic faculty and the institution must consider investing resources in AI training to ensure appropriate use of the technology [ 21 ].

The AI outputs assessed in the present study had errors, particularly with A-type MCQs. One notable observation was that often the AI tools were unable to differentiate the differences between ACEIs and ARBs. AI platforms access several structured and unstructured data, in addition to images, audio, and videos. Hence, the AI platforms can commit errors due to extracting details from unauthenticated sources [ 22 ] created a framework identifying 28 factors for reconstructing the path of AI failures and for determining corrective actions. This is an area of interest for AI technical experts to explore. Also, this further iterates the need for human examination of test items before using them for assessment purposes.

There are concerns that AIs can memorize and provide answers from their training dataset, which they are not supposed to do [ 23 ]. Hence, the use of AIs-generated test items for summative examinations is debatable. It is essential to ensure and enhance the security features of AI tools to reduce or eliminate cross-contamination of test items. Researchers have emphasized that AI tools will only reach their potential if developers and users can access full-text non-PDF formats that help machines comprehend research papers and generate the output [ 24 ].

AI platforms may not always have access to all standard treatment guidelines. However, in the present study, it was observed that all three AI platforms generally provided appropriate test items regarding the choice of medications, aligning with recommendations from contemporary guidelines and standard textbooks in pharmacology and therapeutics. The prompts used in the study were specifically focused on the pre-clerkship phase of the undergraduate medical curriculum (and at the time of their graduation) and assessed fundamental core concepts, which were also reflected in the AI outputs. Additionally, the recommended first-line antihypertensive drug classes have been established for several decades, and information regarding their pharmacokinetics, ADRs, and indications is well-documented in the literature.

Different paradigms and learning theories have been proposed to support AI in education. These paradigms include AI- directed (learner as recipient), AI-supported (learner as collaborator), and AI-empowered (learner as leader) that are based on Behaviorism, Cognitive-Social constructivism, and Connectivism-Complex adaptive systems, respectively [ 25 ]. AI techniques have potential to stimulate and advance instructional and learning sciences. More recently a three- level model that synthesizes and unifies existing learning theories to model the roles of AIs in promoting learning process has been proposed [ 26 ]. The different components of our study rely upon these paradigms and learning theories as the theoretical underpinning.

Strengths and limitations

To the best of our knowledge, this is the first study evaluating the utility of AI platforms in generating test items related to a discipline in the undergraduate medical curriculum. We have evaluated the AI’s ability to generate outputs related to most types of assessment in the undergraduate medical curriculum. The key lessons learnt for improving the AI-generated test item quality from the present study are outlined in Table 7 . We used a structured framework for assessing the content validity of the test items. However, we have demonstrated using a single case study (hypertension) as a pilot experiment. We chose to evaluate anti-hypertensive drugs as it is a core learning objective and one of the most common disorders relevant to undergraduate medical curricula worldwide. It would be interesting to explore the output from AI platforms for other common (and uncommon/region-specific) disorders, non-/semi-core objectives, and disciplines other than Pharmacology and Therapeutics. An area of interest would be to look at the content validity of the test items generated for different curricula (such as problem-based, integrated, case-based, and competency-based) during different stages of the learning process. Also, we did not attempt to evaluate the generation of flowcharts, algorithms, or figures for generating test items. Another potential area for exploring the utility of AIs in medical education would be repeated procedural practices such as the administration of drugs through different routes by trainee residents [ 27 ]. Several AI tools have been identified for potential application in enhancing classroom instructions and assessment purposes pending validation in prospective studies [ 28 ]. Lastly, we did not administer the AI-generated test items to students and assessed their performance and so could not comment on the validity of test item discrimination and difficulty indices. Additionally, there is a need to confirm the generalizability of the findings to other complex areas in the same discipline as well as in other disciplines that pave way for future studies. The conceptual framework used in the present study for evaluating the AI-generated test items needs to be validated in a larger population. Future studies may also try to evaluate the variations in the AI outputs with repetition of the same queries.

Notwithstanding ongoing discussions and controversies, AI tools are potentially useful adjuncts to optimize instructional methods, test blueprinting, test item generation, and guidance for test standard-setting appropriate to learners’ stage in the medical program. However, experts need to critically review the content validity of AI-generated output. These challenges and caveats are to be addressed before the use of widespread use of AIs in medical education can be advocated.

Data availability

All the data included in this study are provided as Electronic Supplementary Materials.

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Sridharan, K., Sequeira, R.P. Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study. BMC Med Educ 24 , 431 (2024). https://doi.org/10.1186/s12909-024-05365-7

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