2026-025-28 ペンシルベニア州立大学（Penn State）
Large language models like ChatGPT respond to health queries with nearly 76% accuracy, raising concerns about their trustworthiness in real-world applications, according to Penn State researchers. Credit: fizkes/Getty Images. All Rights Reserved.
＜関連情報＞
- https://www.psu.edu/news/research/story/calling-doctor-gpt-ai-responses-healthcare-queries-are-nearly-76-accurate
- https://arxiv.org/abs/2506.13805
GPT博士が診察いたしますが、本当に必要でしょうか？クラウドソーシングによる臨床症例を用いた、医療診断における大規模言語モデルの利点と欠点の検証 Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
Bonam Mingole, Aditya Majumdar, Firdaus Ahmed Choudhury, Jennifer L. Kraschnewski, Shyam S. Sundar, Amulya Yadav
arXiv Submitted on 13 Jun 2025
DOI:https://doi.org/10.48550/arXiv.2506.13805
Abstract
The proliferation of Large Language Models (LLMs) in high-stakes applications such as medical (self-)diagnosis and preliminary triage raises significant ethical and practical concerns about the effectiveness, appropriateness, and possible harmfulness of the use of these technologies for health-related concerns and queries. Some prior work has considered the effectiveness of LLMs in answering expert-written health queries/prompts, questions from medical examination banks, or queries based on pre-existing clinical cases. Unfortunately, these existing studies completely ignore an in-the-wild evaluation of the effectiveness of LLMs in answering everyday health concerns and queries typically asked by general users, which corresponds to the more prevalent use case for LLMs. To address this research gap, this paper presents the findings from a university-level competition that leveraged a novel, crowdsourced approach for evaluating the effectiveness of LLMs in answering everyday health queries. Over the course of a week, a total of 34 participants prompted four publicly accessible LLMs with 212 real (or imagined) health concerns, and the LLM generated responses were evaluated by a team of nine board-certified physicians. At a high level, our findings indicate that on average, 76% of the 212 LLM responses were deemed to be accurate by physicians. Further, with the help of medical professionals, we investigated whether RAG versions of these LLMs (powered with a comprehensive medical knowledge base) can improve the quality of responses generated by LLMs. Finally, we also derive qualitative insights to explain our quantitative findings by conducting interviews with seven medical professionals who were shown all the prompts in our competition. This paper aims to provide a more grounded understanding of how LLMs perform in real-world everyday health communication.
