ArticleNPJ digital medicine2026
Human-large language model collaboration in clinical medicine: a systematic review and meta-analysis.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Article
- Large Language Models in Preclinical Spine Research: A Scoping Review and Expert Perspective on Evidence-Aware Experimental Workflows.JOR spine · 2026Article
- A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- From Integrated Care to Learning Systems.Healthcare (Basel, Switzerland) · 2026Review
- Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance-A Scoping Review of Reviews.Healthcare (Basel, Switzerland) · 2026Review
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- More AI, Less Care-Seeking? A National Survey Experiment on the Impact of AI Intensity on Patient Care-Seeking Intention in Chinese Family Doctor Services.Healthcare (Basel, Switzerland) · 2026Article
- A generative two-stage semantic intermediary framework for explainable mental health early warning in higher education.Frontiers in psychiatry · 2026Review
- Value Co-Creation Between Nurses and Generative Artificial Intelligence: A Grounded Theory Study.Journal of nursing management · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- From model failure to system harm: operationalizing a sociotechnical pathway for healthcare AI safety.Frontiers in artificial intelligence · 2026Article
- How professional logics shape AI implementation in mental healthcare: a qualitative study of an LLM-enhanced chatbot.Frontiers in digital health · 2026Article
- Closing the loop: human-augmented, mechanistically enhanced AI for proactive management of drug-drug interactions.Frontiers in pharmacology · 2026Article
- Academic AI overreliance scale: development and preliminary psychometric validation in health sciences university students.Frontiers in digital health · 2026Article
- Dynamic alignment of large language models for evidence-grounded heart failure decision support.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Human-AI collaboration (H + AI) using large language models (LLMs) offers a promising approach to enhance clinical reasoning, documentation, and interpretation tasks. Following PRISMA 2020 (PROSPERO registration: CRD420251068272), we systematically compared H + AI with human-only (H) workflows, searching four databases through June 28, 2025. Ten peer-reviewed studies met eligibility criteria, with three preprints informing sensitivity analyses only. Diagnostic/interpretation accuracy (k = 2) showed a positive trend for H + AI (Risk Ratio [RR] 1.59), but was statistically imprecise and non-significant (95% CI 0.08 to 32.74), with 95% prediction intervals (PI) crossing the null. Composite diagnostic/management scores (k = 2) showed a statistically significant improvement (Mean Difference [MD] +4.88 percentage points, 95% CI + 0.65 to +9.12), yet the PI (-31.65 to 41.42) indicates high real-world uncertainty. Time efficiency (k = 3) showed no overall difference (MD + 0.4 min, 95%CI -4.18 to +4.97; I² = 70.1%). While documentation quality improved, but factual error rates remained high (~26-36%), undermining quality gains. In three-arm settings, H + AI did not universally outperform AI-only. Evidence remains preliminary yet highly uncertain and context-dependent. We recommend preregistered, pragmatic, multicenter trials embedded in real workflows, with harmonized core outcomes that prioritize safety/error metrics and interfaces that surface uncertainty and support verification.
Identifiers
What OpenQuestion holds
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.