SynthesisNature human behaviour2024
When combinations of humans and AI are useful: A systematic review and meta-analysis.
Synthesis in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 2 of them syntheses that pooled it.
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
91 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Human learning is an understudied but promising lever for boosting human-AI synergy.Proceedings of the National Academy of Sciences of the United States of America · 2026Pooled it
- Conceptual Understanding and Conceptual Change Through Human AI Collaboration in Science Education: An Integrated Conceptual Framework from a Systematic Literature Review.F1000Research · 2026Pooled it
- Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial.BMC medical education · 2026Trial
- AI-Based EMG Reporting: A Randomized Controlled Trial.Journal of neurology · 2025Trial
- The next frontier in healthcare simulation is human-AI teaming.Advances in simulation (London, England) · 2026Article
- Diagnostic performance of commercial AI systems versus participating radiologists for pulmonary nodule detection in routine clinical practice.Japanese journal of radiology · 2026Article
- Introducing AI to an online petition platform changed outputs but not outcomes.Nature human behaviour · 2026Article
- Theoretical Exploration of Error Thresholds for Clinical AI Decision Support in Nursing: Exploratory Simulation Study Grounded in Human-AI Reliance Data.JMIR nursing · 2026Article
- Seven Pillars for a Community-Led AI-POCUS Future - A WINFOCUS Manifesto.The ultrasound journal · 2026Article
- Large reasoning models as thinking machines for medicine.Nature biomedical engineering · 2026Review
- [Artificial intelligence and occupational participation in Germany: a narrative review].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026Review
- Leadership and Human-AI Collaboration: A Measurement Scale.Behavioral sciences (Basel, Switzerland) · 2026Article
- A systematic map of generative AI guidelines and reporting in ecology and evolutionary biology: towards the framework of AI disclosure for Improved Transparency (AIdIT).Research integrity and peer review · 2026Review
- Benchmarking large language models against practicing clinicians on psychopathological assessment.NPJ digital medicine · 2026Article
- Artificial stupidity or logimorphism? How misuse of language warps our thinking about 'artificial intelligence'.European heart journal. Digital health · 2026Article
- Doctors vs. Algorithms: Physicians, too, struggle to learn from evidence that contradicts AI suggestions.PLOS digital health · 2026Article
- Article
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- Human-AI collaboration for dysphagia rehabilitation from effectiveness to implementation complexity: a systematic review.NPJ digital medicine · 2026Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human-AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes. We searched an interdisciplinary set of databases (the Association for Computing Machinery Digital Library, the Web of Science and the Association for Information Systems eLibrary) for studies published between 1 January 2020 and 30 June 2023. Each study was required to include an original human-participants experiment that evaluated the performance of humans alone, AI alone and human-AI combinations. First, we found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone (Hedges' g = -0.23; 95% confidence interval, -0.39 to -0.07). Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when AI outperformed humans alone, we found losses. Limitations of the evidence assessed here include possible publication bias and variations in the study designs analysed. Overall, these findings highlight the heterogeneity of the effects of human-AI collaboration and point to promising avenues for improving human-AI systems.
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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.