Title: Leveraging Large Language Models for Decision Support in Personalized Oncology
Tuesday, July 23, 2024
11:00am-12:00pm ET
Description
Large Language Models (LLMs) are AI tools trained to generate new text from large datasets. Since the release of ChatGPT, they have received significant public attention and sparked a debate about the benefits and risks of Artificial Intelligence to an unprecedented degree. LLMs enable tasks such as answering questions, summarizing text, or translating, with a quality that approaches human level. The use of LLMs is increasingly entering the medical field, changing how research is conducted on medical issues, how medicine is taught, and how it is practiced by physicians.
In this presentation, we will give an introduction into LLMs and present a study, which evaluates the potential of conversational large language models (LLMs) as tools for personalized decision-making in precision oncology. Ten fictional cases of advanced cancer with genetic alterations were assessed by four different LLMs (ChatGPT, Galactica, Perplexity, and BioMedLM) and one expert physician to identify treatment options. While LLMs provided a higher number of treatment options compared to the expert, their suggestions often deviated from expert recommendations. However, LLMs did propose some reasonable strategies not easily identified by experts, suggesting potential for improvement in LLM-based decision-making tools. Despite not yet being suitable for routine clinical use, LLMs may enhance existing methods in oncology decision support, particularly as technology advances.
Target Audience
All medical and healthcare professionals and researchers interested in understanding cancer genomic testing and somatic and germline variant interpretation methods. This series is presented as a collaboration between ClinGen Somatic, VICC, and ACMG consortia.
Agenda
Presentation followed by live Q&A.
Learning Objectives
At the conclusion of this session, participants should be able to:
1. Name different Large Language Models which are currently available.
2. Discuss possible constraints of LLMs in the field of precision oncology.
3. Differentiate between prompting strategies.