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

• URL: http://arxiv.org/abs/2506.13805v1
• Date: Fri, 13 Jun 2025 17:12:47 GMT
• Title: 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
• Authors: Bonam Mingole, Aditya Majumdar, Firdaus Ahmed Choudhury, Jennifer L. Kraschnewski, Shyam S. Sundar, Amulya Yadav,
• Abstract summary: Large Language Models (LLMs) are used in high-stakes applications such as medical (self-diagnosis) and preliminary triage.<n>This paper presents the findings from a university-level competition that leveraged a novel, crowdsourced approach for evaluating the effectiveness of LLMs.
• Score: 7.894865736540358
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MIRIAD: Augmenting LLMs with millions of medical query-response pairs [36.32674607022871]
  We introduce MIRIAD, a large-scale, curated corpus of 5,821,948 medical QA pairs.<n>We show that MIRIAD improves accuracy up to 6.7% compared to unstructured RAG baselines.<n>We also introduce MIRIAD-Atlas, an interactive map of MIRIAD spanning 56 medical disciplines.
  arXiv  Detail & Related papers  (2025-06-06T13:52:32Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Fact or Guesswork? Evaluating Large Language Model's Medical Knowledge with Structured One-Hop Judgment [108.55277188617035]
  Large language models (LLMs) have been widely adopted in various downstream task domains, but their ability to directly recall and apply factual medical knowledge remains under-explored.<n>Most existing medical QA benchmarks assess complex reasoning or multi-hop inference, making it difficult to isolate LLMs' inherent medical knowledge from their reasoning capabilities.<n>We introduce the Medical Knowledge Judgment, a dataset specifically designed to measure LLMs' one-hop factual medical knowledge.
  arXiv  Detail & Related papers  (2025-02-20T05:27:51Z)
• The Potential of LLMs in Medical Education: Generating Questions and Answers for Qualification Exams [13.469665087042614]
  Conventional medical education requires sophisticated clinicians to formulate questions and answers based on prototypes from EHRs.<n>We found that mainstream LLMs could generate questions and answers with real-world EHRs at levels close to clinicians.
  arXiv  Detail & Related papers  (2024-10-31T09:33:37Z)
• Language Models And A Second Opinion Use Case: The Pocket Professional [0.0]
  This research tests the role of Large Language Models (LLMs) as formal second opinion tools in professional decision-making. The work analyzed 183 challenging medical cases from Medscape over a 20-month period, testing multiple LLMs' performance against crowd-sourced physician responses.
  arXiv  Detail & Related papers  (2024-10-27T23:48:47Z)
• CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios [50.032101237019205]
  CliMedBench is a comprehensive benchmark with 14 expert-guided core clinical scenarios. The reliability of this benchmark has been confirmed in several ways.
  arXiv  Detail & Related papers  (2024-10-04T15:15:36Z)
• GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI [67.09501109871351]
  Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals. GMAI-MMBench is the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format.
  arXiv  Detail & Related papers  (2024-08-06T17:59:21Z)
• A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
  Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language. This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-09T02:55:58Z)
• Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering [48.17095875619711]
  We present a system called LLMs Augmented with Medical Textbooks (LLM-AMT)<n>LLM-AMT integrates authoritative medical textbooks into the LLMs' framework using plug-and-play modules.<n>We found that medical textbooks as a retrieval corpus is proven to be a more effective knowledge database than Wikipedia in the medical domain.
  arXiv  Detail & Related papers  (2023-09-05T13:39:38Z)
• An Automatic Evaluation Framework for Multi-turn Medical Consultations Capabilities of Large Language Models [22.409334091186995]
  Large language models (LLMs) often suffer from hallucinations, leading to overly confident but incorrect judgments. This paper introduces an automated evaluation framework that assesses the practical capabilities of LLMs as virtual doctors during multi-turn consultations.
  arXiv  Detail & Related papers  (2023-09-05T09:24:48Z)
• Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models [4.8775268199830935]
  This study aims to assess the effectiveness of large language models (LLMs) as a self-diagnostic tool and their role in spreading healthcare misinformation.<n>We use open-ended questions to mimic real-world self-diagnosis use cases, and perform sentence dropout to mimic realistic self-diagnosis with missing information.<n>The results highlight the modest capabilities of LLMs, as their responses are often unclear and inaccurate.
  arXiv  Detail & Related papers  (2023-07-10T21:28:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.