Leveraging Large Language Models for Decision Support in Personalized Oncology

By M. Benary, X. Wang, Max Schmidt, D. Soll, G. Hilfenhaus, M. Nassir, C. Sigler, M. Knödler, U. Keller, D. Beule and 3 more. JAMA Network Open, 2023

297 citations recorded by the source corpus.

Key Points Question Can current conversational large language models (LLMs) be used as a tool for personalized decision-making in precision oncology? Findings In this diagnostic study, treatment option identification from 4 LLMs for 10 fictional patients deviated substantially from expert recommendations. Nevertheless, LLMs correctly identified several important treatment strategies and partly provided reasonable suggestions that were not easily found by experts. Meaning These results suggest that LLMs are not yet applicable as a routine tool for aiding personalized clinical decision-making in oncology, but do improve upon existing LLM-based methods.

Subfields: Cancer Research, Genetics, Health Informatics

View Paper: https://www.semanticscholar.org/paper/575a215f1080dfc0e9f8d055775c294e765aac84

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Kai He
2. Qika Lin
3. Xiang Lan
4. Rui Mao
5. Mengling Feng
6. Yucheng Ruan
7. Erik Cambria
8. Soujanya Poria

Citations in the corpus

• The application of large language models in medicine: A scoping review (2024, unknown): “60 Given thestringent standards inhealthcare for accuracy,safety, reliability,andclarity,theapplicationofsuchtoolsinmedicaldecision-makingissubjecttorigorousscrutiny.”
• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, methodology): “Recently, OpenAI launched their Large Language Models (LLMs) ChatGPT and GPT-4, which shows superior performance in various NLP-related tasks, as well as scientific knowledge, such as Biology, Chemistry, and Medical exams [15].”