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Human–AI collaboration in high-stakes decisions: a meta-analysis of healthcare and public sectors
- Truvace record
- TRV-2026-2772raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
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This meta-analysis of 146 experiments in the healthcare and public sectors examines human–AI synergy versus augmentation amid substantial heterogeneity. We find that AI augmentation reliably improves human performance (Hedges’ g = 0.622), whereas synergy effects are generally negative, with AI alone often outperforming human–AI teams (Hedges’ g = −0.380), although publication bias favours positive augmentation results. Additionally, task type, AI transparency, and user expertise significantly moderate outcomes.…
The problem
A meta-analysis of 146 healthcare and public-sector experiments found that human-AI teams generally did worse than AI working alone (Hedges' g = −0.380).
What this doesn’t fix
The experiments showed substantial heterogeneity, and publication bias favours positive augmentation results, which may inflate the estimated benefit.
Sources
1 on file- Applied Economics Lettersdoi:10.1080/13504851.2025.2586160
Cite this record
v1Truvace Impact Record TRV-2026-2772, v1: “Human–AI collaboration in high-stakes decisions: a meta-analysis of healthcare and public sectors.” Truvace, 2026-11-12. /record/TRV-2026-2772 (accessed on the day you copy it). sha256 fb5229f9b01a0bb9…
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