Utilizing large language models in breast cancer management: systematic review

Author:

Sorin Vera,Glicksberg Benjamin S.,Artsi Yaara,Barash Yiftach,Konen Eli,Nadkarni Girish N.,Klang Eyal

Abstract

Abstract Purpose Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field. Methods We searched MEDLINE for relevant studies published before December 22, 2023. Keywords included: “large language models”, “LLM”, “GPT”, “ChatGPT”, “OpenAI”, and “breast”. The risk bias was evaluated using the QUADAS-2 tool. Results Six studies evaluating either ChatGPT-3.5 or GPT-4, met our inclusion criteria. They explored clinical notes analysis, guideline-based question-answering, and patient management recommendations. Accuracy varied between studies, ranging from 50 to 98%. Higher accuracy was seen in structured tasks like information retrieval. Half of the studies used real patient data, adding practical clinical value. Challenges included inconsistent accuracy, dependency on the way questions are posed (prompt-dependency), and in some cases, missing critical clinical information. Conclusion LLMs hold potential in breast cancer care, especially in textual information extraction and guideline-driven clinical question-answering. Yet, their inconsistent accuracy underscores the need for careful validation of these models, and the importance of ongoing supervision.

Publisher

Springer Science and Business Media LLC

Reference27 articles.

1. Brin D, Sorin V, Konen E, Nadkarni G, Glicksberg BS, Klang E (2023) How large language models perform on the united states medical licensing examination: a systematic review. medRxiv 23:543

2. Bubeck S, Chandrasekaran V, Eldan R, et al. (2023) Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712

3. Chaudhry HJ, Katsufrakis PJ, Tallia AF (2020) The USMLE step 1 decision. JAMA 323(20):2017

4. Choi HS, Song JY, Shin KH, Chang JH, Jang B-S (2023) Developing prompts from large language model for extracting clinical information from pathology and ultrasound reports in breast cancer. Radiat Oncol J 41(3):209–216

5. Decker H, Trang K, Ramirez J et al (2023) Large language Model−based Chatbot vs Surgeon-generated informed consent documentation for common procedures. JAMA Netw Open 6(10):e2336997

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3