AMIA KDDM and KRS Working Groups’ Collaborative Workshop: Empowering Healthcare with Knowledge-Augmented Large Language Models -- Innovations and Applications

Presentation Time: 08:30 AM - 12:00 PM

Abstract Keywords: Large Language Models (LLMs), Information Retrieval, Information Extraction, Natural Language Processing, Fairness and Elimination of Bias
Working Group: Knowledge Discovery and Data Mining Working Group
Primary Track: Applications
Programmatic Theme: Clinical Informatics

In the rapidly evolving healthcare technology landscape, the integration of Large Language Models (LLMs) stands at the forefront of innovation, offering unprecedented opportunities to transform patient care, medical research, and healthcare operations. However, current mainstream generative LLMs such as OpenAI ChatGPT and Meta LlaMa 2 may generate inaccurate, misleading, or even harmful content due to their inherent uncertainly, incompleteness, or bias, which can have serious consequences for patient safety when they are directly used for health applications. Our collaborative workshop, jointly organized by AMIA KDDM, NLP, and KRS Working Groups aims to delve into the cutting-edge advancements that augment LLM capabilities, focusing on Retrieval-Augmented Generation (RAG), in-context learning, contrastive learning, LLM funetuning (e.g., low-rank adaptation), and few-shot learning. These techniques can be broadly categorized into prompting strategies, retrieval-based strategies, and finetuning strategies and their optimal use is still in debate. These sophisticated techniques are pivotal in enhancing the quality, accuracy, and reliability of LLM-based applications across a broad spectrum of healthcare domains. One prime example is the development of advanced medical Question Answering (QA) systems that leverage RAG to provide precise, evidence-based answers drawn from vast medical literature and patient data. Beyond these, the workshop will explore the explainability of LLMs, as well as the ethical considerations in deploying these advanced AI tools in sensitive healthcare environments. By convening experts from healthcare, AI research, and ethics, this workshop seeks to explore the potential of these technologies to provide up-to-date medical information, facilitate better decision-making, and improve patient outcomes.

Speaker(s):
Zhe He, PhD, FAMIA
Florida State University

Nansu Zong, Ph.D.
Mayo Clinic

Rui Zhang, PhD, FAMIA
University of Minnesota, Twin Cities

Zhiyong Lu, PhD
National Library of Medicine, NIH

Qiao Jin, M.D.
National Institutes of Health

Aokun Chen


Yifan Peng, PhD
Weill Cornell Medicine; Dept of Population Health Sciences; Div of Health Informatics

Author(s):
Zhe He, PhD, FAMIA - Florida State University; Ying Li, Ph.D. - Regeneron Pharmaceuticals; Nansu Zong, Ph.D. - Mayo Clinic; Rui Zhang, PhD, FAMIA - University of Minnesota, Twin Cities; Zhiyong Lu, PhD - National Library of Medicine, NIH; Qiao Jin, M.D. - National Institutes of Health; Yifan Peng, PhD - Weill Cornell Medicine; Dept of Population Health Sciences; Div of Health Informatics; Aokun Chen; Jiang Bian, PhD - University of Florida;