Large Language Models For Biomedical Research

On Friday July 12, 2024, Yuan Luo, PhD presented a talk on Large Language Models for Biomedical Research. Professor Luo is Chief AI Officer at Clinical and Translational Sciences Institute and Associate Professor at Northwestern University Feinberg School of Medicine. This talk was hosted by the AI Precision Health Institute at the University of Hawai'i Cancer Center as part of a popular seminar series featuring leading AI researchers from around the world.

LLMs such as transformer-based models have been wildly successful in setting state-of-the-art benchmarks on a broad range of natural language processing tasks, including question answering, document classification, machine translation, text summarization, and others. Recently, the release of OpenAI’s free tool ChatGPT demonstrated the ability of LLMs to generate content, with anticipations on its possible uses and potential controversies. The ethical and acceptable boundaries of ChatGPT’s use in scientific writing remain unclear.

In this talk, Dr. Luo discussed his research exploring LLMs (long-sequence transformers and GPT style models) in the clinical and biomedical domains, and the adaptability of these LLMs to a series of clinical NLP tasks including clinical inferencing, biomedical named entity recognition, EHR based question answering, and interoperability.

Highlights & Slides

Yuan Luo, PhD

Yuan Luo, PhD,?is Chief AI Officer at the Clinical and Translational Sciences Institute and Institute for AI in Medicine, and Associate Professor at Department of Preventive Medicine, at Feinberg School of Medicine in Northwestern University. Globally recognized for his leadership and significant contributions to biomedical AI, Dr.?Luo?has been elected as?Fellow of the International Academy of Health Sciences Informatics, Fellow of the American College of Medical Informatics and Fellow of the American Medical Informatics Association. A visionary leader in the field, Dr.?Luo?is at the forefront of building next-generation biomedical informatics and collaborative AI for healthcare. His exemplary leadership shapes strategies across various levels, ranging from university settings to entire health systems to national research consortia.

With a commitment to democratizing AI literacy, Dr.?Luo?has been featured in eminent venues such as The Economist, JAMA Network and Becker's Hospital Review to share unique visions on delivering data and AI strategies that power Research & Development and drive business value. As a pioneer in the development of multi-modal AI and data science frameworks, Dr.?Luo's work focuses on understanding complex diseases and informing targeted therapies. His groundbreaking research has been featured in leading journals, including JAMA, Nature Medicine, and Nature Biotechnology etc. Dr.?Luo?has given numerous keynotes to both academia and industry and has chaired multiple conferences and workshops. With a publication record of over 190 peer-reviewed papers, Dr.?Luo's work has been cited by scientists across more than 30 different countries and 25 research areas.

“There is a major unmet need for enabling a multidisciplinary workforce that interacts synergistically in the dynamic healthcare landscape. We aim to provide fertile ground for cross-pollinating next-generation clinicians and scientists to bring AI in healthcare to fruition."

Yuan Luo, PhD, Chief AI Officer and Associate Professor, Northwestern University Feinberg School of Medicine

Center for Collaborative AI in Healthcare

Dr. Luo leads the Center for Collaborative AI in Healthcare at Northwestern University Feinberg School of Medicine. The Center's mission is to improve precision medicine by advancing AI science, engineering and translation across healthcare specialties. The new center plans to democratize access to AI infrastructure and training resources and engage new people previously facing AI and machine learning barriers including clinicians, hospital administrators, trainees and basic scientists both inside and outside the Feinberg School of Medicine.

The Center for Collaborative AI in Healthcare plans to enable AI as a collaborator and serve as a university-wide hub for research, strategic infrastructure and training in collaborative AI. The Center will provide assistance to clinicians, hospital administrators and basic scientists in applying AI methods to their research and clinical practice. A key focus of the center will be hosting research, infrastructure, training and collaborative activities for investigators throughout Northwestern. These activities include developing comprehensive AI and machine learning models and resources that integrate multiple modalities of healthcare data; building flagship and strategic datasets that integrate both phenotypic and multi-omic data modalities; providing mentoring, training and educational opportunities; and supporting the integration of data assets from other Feinberg centers.

AI Precision Health Institute

In 2022 we formed the AI Precision Health Institute Affinity Group Seminar Series to discuss current trends and applications of AI in cancer research and clinical practice. The group brings together AI researchers in a variety of fields including computer science, engineering, nutrition, epidemiology, and radiology with clinicians and advocates. The goal is to foster collaborative interactions to solve problems in cancer that were thought to be unsolvable a decade ago before the broad use of deep learning and AI in medicine.

Past Seminars In This Seminar Series

What Happens If We Use Synthetic Data Without Any Curation, June 2024

Predictive AI Models - Data Standards In Action, May 2024

AI Powered Dermatology Tools and Consumer Decision Making, April 2024

AI Decodes Waveforms To Help Prevent Sudden Cardiac Death, March 2024

Mitigating Unintended Consequences of AI in Biomedicine, February 2024

How To Build Responsible, Safe, Trusted AI For Precision Health, January 2024

Robust Interpretability Methods For Large Language Models, December 2023

Machine Learning Captures Insights Into Brain Tumor Biology, November 2023

Comparing AI Algorithms To Predict 5 Year Breast Cancer Risk, October 2023

Disrupting the Indigenous DNA SupplyChain, September 2023

AI Based Lab Test Approved To Phenotype, Grade Breast Cancer, July 2023

Trustworthy AI and Clinical Validation In Breast Cancer Imaging, June 2023

AI For Ultrasound For Real-Time Breast Cancer Decision Support, May 2023

Deep Learning To Diagnose Breast Cancer With High Accuracy, April 2023

Precision Oncology: Empowering Radiologists With AI, January 2023

Machine Learning For Personalized Cancer Screening, December 2022

AI Driven Surgical Robots To Diagnose/Treat Prostate Cancer, November 2022

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Copyright ? 2024 Margaretta Colangelo. All Rights Reserved.

This article was written by Margaretta Colangelo. Margaretta is a leading AI analyst who tracks significant milestones in AI in healthcare. She consults with AI healthcare companies and writes about some of the companies she consults with. Margaretta serves on the advisory board of the AI Precision Health Institute at the University of Hawai?i?Cancer Center @realmargaretta