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<p>Early studies of large language models (LLMs) in clinical settings have largely treated artificial intelligence (AI) as a tool rather than an active collaborator. As LLMs demonstrate expert-level diagnostic performance, the focus shifts from whether AI can offer valuable suggestions to how it integrates into physicians’ diagnostic workflows. We conducted a randomized controlled trial (n = 70 clinicians) to assess a custom system designed for collaborative diagnostic reasoning. The design involved independent diagnostic assessments by the clinician and AI, followed by an AI-generated synthesis integrating both perspectives, highlighting agreements, disagreements, and offering commentary. We evaluated two collaborative workflows: AI as first opinion (preceding clinician) and AI as second opinion (following clinician). Both improved clinician diagnostic accuracy over conventional resources, (85% and 82% vs. 75%). Performance was comparable across workflows and not statistically different from AI-alone accuracy (90%), highlighting the potential of collaborative AI to complement clinician expertise. Qualitative analyses illustrate how workflow design shapes human-AI interaction. <a href="http://clinicaltrials.gov">C</a>: NCT06911645.</p>

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From tool to teammate in a randomized controlled trial of clinician-AI collaborative workflows for diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

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  • Selin S. Everett,
  • Bryan J. Bunning,
  • Priyank Jain,
  • Ivan Lopez,
  • Anup Agarwal,
  • Manisha Desai,
  • Robert Gallo,
  • Ethan Goh,
  • Vinay B. Kadiyala,
  • Zahir Kanjee,
  • Jacob M. Koshy,
  • Andrew Olson,
  • Adam Rodman,
  • Kevin Schulman,
  • Eric Strong,
  • Jonathan H. Chen,
  • Eric Horvitz

摘要

Early studies of large language models (LLMs) in clinical settings have largely treated artificial intelligence (AI) as a tool rather than an active collaborator. As LLMs demonstrate expert-level diagnostic performance, the focus shifts from whether AI can offer valuable suggestions to how it integrates into physicians’ diagnostic workflows. We conducted a randomized controlled trial (n = 70 clinicians) to assess a custom system designed for collaborative diagnostic reasoning. The design involved independent diagnostic assessments by the clinician and AI, followed by an AI-generated synthesis integrating both perspectives, highlighting agreements, disagreements, and offering commentary. We evaluated two collaborative workflows: AI as first opinion (preceding clinician) and AI as second opinion (following clinician). Both improved clinician diagnostic accuracy over conventional resources, (85% and 82% vs. 75%). Performance was comparable across workflows and not statistically different from AI-alone accuracy (90%), highlighting the potential of collaborative AI to complement clinician expertise. Qualitative analyses illustrate how workflow design shapes human-AI interaction. C: NCT06911645.