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Presentation Time: 08:30 AM - 11:30 AM

Abstract Keywords: Artificial Intelligence, Large Language Models (LLMs), Natural Language Processing, Bioinformatics
Primary Track: Foundations
Programmatic Theme: Academic Informatics / LIEAF

Due to the rapid growth of publications varying in quality, there exists a pressing need to help clinicians and biomedical researchers digest and evaluate relevant medical papers, thereby accelerating medical discovery and ultimately improving patient care. This creates several urgent questions; however, human-computer collaboration in the medical research lifecycle is still in the exploratory stage and lacks a unified framework for analyzing the relevant tasks. Additionally, with the recent significant success of large language models (LLMs), they have increasingly played an important role in medical research. In this tutorial, we aim to provide an all-encompassing overview of the biomedical research lifecycle, detailing how machines can augment every stage of the research process for biomedical researchers and clinicians, including AI-assisted medical literature understanding, hypothesis generation, experiment development, manuscript/report draft writing, and, finally, medical fact-checking. This tutorial is devised for researchers and clinicians interested in this rapidly developing field of AI-augmented medical research lifecycle. Furthermore, we will address current challenges, explore future directions, and discuss potential ethical issues.

Speakers:
Qingyun Wang, PhD
William & Mary

Yao Ge, Master
Emory University

Qingyu Chen, PhD
Yale University

Zhiyong Lu, PhD
NCBI

Authors:
Qingyun Wang, PhD - William & Mary; Yao Ge, Master - Emory University; Qingyu Chen, PhD - Yale University; Zhiyong Lu, PhD - NCBI;

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