ArticleNPJ digital medicine2026
From tool to teammate in a randomized controlled trial of clinician-AI collaborative workflows for diagnosis.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06911645 (Evaluating the Performance of LLMs and Clinicians in Complex Diagnostic Cases), which is not on this map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluating the Performance of LLMs and Clinicians in Complex Diagnostic Cases: A Randomized Controlled Trial
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital compassion fatigue and digital empathy in healthcare: mapping the evidence and informing a human-centered clinical framework.Frontiers in psychology · 2026Pooled it
- Real-Time Artificial Intelligence Diagnostic Copilot in Simulated Primary Care Consultations: Randomized Simulation Study.JMIR formative research · 2026Trial
- Realizing the value of the divergence event in clinical human-AI interaction.NPJ digital medicine · 2026Article
- The next frontier in healthcare simulation is human-AI teaming.Advances in simulation (London, England) · 2026Article
- When the Algorithm Speaks First: Trust, Reliance and the Reconstruction of Clinical Metacognition.Journal of evaluation in clinical practice · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Randomized Controlled Trial of Large Language Model-Assisted Diagnostic Accuracy in Nephrology.Kidney international reports · 2026Article
- Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999-2025].Journal of thoracic disease · 2026Article
- Evaluating foundation models on official German medical licensing examinations: Implications for high-stakes assessment and AI-assisted medical education.NPJ digital medicine · 2026Article
- Intelligent Automation Improved Efficiency in Pharmacovigilance Safety Signal Assessment.Clinical pharmacology and therapeutics · 2026Article
- Six misconceptions about large language models: A minimal model and diagnostic taxonomy.PNAS nexus · 2026Article
- Large language models approach clinician performance in ESC cardiovascular risk stratification: a vignette-based benchmark study.European heart journal. Digital health · 2026Article
- Hospitalists Are Already Using AI-Why Implementation Will Determine Its Impact.Journal of medical Internet research · 2026Article
- The effect of medical explanations from large language models on diagnostic accuracy in radiology.NPJ digital medicine · 2026Article
- Human-large language model collaboration in clinical medicine: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- State of clinical AI in 2026.BMJ digital health & AI · 2026Review
- Prompt injection of OpenAI custom GPTs leaks informatics secrets.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Value Co-Creation Between Nurses and Generative Artificial Intelligence: A Grounded Theory Study.Journal of nursing management · 2026Article
Corrections and comments
- Update of
Authors and funding
17 authors.
Funding
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.