Human-AI collectives produce the most accurate differential diagnoses

• URL: http://arxiv.org/abs/2406.14981v1
• Date: Fri, 21 Jun 2024 08:46:30 GMT
• Title: Human-AI collectives produce the most accurate differential diagnoses
• Authors: N. Zöller, J. Berger, I. Lin, N. Fu, J. Komarneni, G. Barabucci, K. Laskowski, V. Shia, B. Harack, E. A. Chu, V. Trianni, R. H. J. M. Kurvers, S. M. Herzog,
• Abstract summary: We show that hybrid collectives of physicians and large language models (LLMs) outperform both single physicians and physician collectives. Our approach highlights the potential for collective human and machine intelligence to improve accuracy in complex, open-ended domains like medical diagnostics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence systems, particularly large language models (LLMs), are increasingly being employed in high-stakes decisions that impact both individuals and society at large, often without adequate safeguards to ensure safety, quality, and equity. Yet LLMs hallucinate, lack common sense, and are biased - shortcomings that may reflect LLMs' inherent limitations and thus may not be remedied by more sophisticated architectures, more data, or more human feedback. Relying solely on LLMs for complex, high-stakes decisions is therefore problematic. Here we present a hybrid collective intelligence system that mitigates these risks by leveraging the complementary strengths of human experience and the vast information processed by LLMs. We apply our method to open-ended medical diagnostics, combining 40,762 differential diagnoses made by physicians with the diagnoses of five state-of-the art LLMs across 2,133 medical cases. We show that hybrid collectives of physicians and LLMs outperform both single physicians and physician collectives, as well as single LLMs and LLM ensembles. This result holds across a range of medical specialties and professional experience, and can be attributed to humans' and LLMs' complementary contributions that lead to different kinds of errors. Our approach highlights the potential for collective human and machine intelligence to improve accuracy in complex, open-ended domains like medical diagnostics.

Related papers

• 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. 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. 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)
• When One LLM Drools, Multi-LLM Collaboration Rules [98.71562711695991]
  We argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people. We organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange. We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development.
  arXiv  Detail & Related papers  (2025-02-06T21:13:44Z)
• 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)
• RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment [54.91736546490813]
  We introduce the RuleAlign framework, designed to align Large Language Models with specific diagnostic rules. We develop a medical dialogue dataset comprising rule-based communications between patients and physicians. Experimental results demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-08-22T17:44:40Z)
• MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making [45.74980058831342]
  We introduce a novel multi-agent framework, named Medical Decision-making Agents (MDAgents) The assigned solo or group collaboration structure is tailored to the medical task at hand, emulating real-world medical decision-making processes. MDAgents achieved the best performance in seven out of ten benchmarks on tasks requiring an understanding of medical knowledge.
  arXiv  Detail & Related papers  (2024-04-22T06:30:05Z)
• Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionals [1.6574413179773761]
  This paper explores the evolving relationship between clinician trust in LLMs and the impact of data sources from predominantly human-generated to AI-generated content. One of the primary concerns identified is the potential feedback loop that arises as LLMs become more reliant on their outputs for learning. A key takeaway from our investigation is the critical role of user expertise and the necessity for a discerning approach to trusting and validating LLM outputs.
  arXiv  Detail & Related papers  (2024-03-15T04:04:45Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• MacGyver: Are Large Language Models Creative Problem Solvers? [87.70522322728581]
  We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. We create MACGYVER, an automatically generated dataset consisting of over 1,600 real-world problems. We present our collection to both LLMs and humans to compare and contrast their problem-solving abilities.
  arXiv  Detail & Related papers  (2023-11-16T08:52:27Z)
• Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review [16.008511195589925]
  Large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This paper provides a comprehensive review on the applications and implications of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-03T13:51:36Z)

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.