ArticleTranslational psychiatry2021
How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 4 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.
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Who cites it
66 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Contrastive explainable AI in healthcare: a systematic review of trends, benefits, research gaps, and future directions.BMC medical informatics and decision making · 2026Pooled it
- Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies.BMC medical informatics and decision making · 2025Pooled it
- Improving Explainability and Integrability of Medical AI to Promote Health Care Professional Acceptance and Use: Mixed Systematic Review.Journal of medical Internet research · 2025Pooled it
- When combinations of humans and AI are useful: A systematic review and meta-analysis.Nature human behaviour · 2024Pooled it
- Out with AI, in with the psychiatrist: a preference for human-derived clinical decision support in depression care.Translational psychiatry · 2023Trial
- Beyond the Black Box: Is Artificial Intelligence Ready to Reshape Neurosurgical Decision-Making? A Narrative Review.Health science reports · 2026Article
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- How Best to Explain Machine Learning Models to Clinicians: A User Study of Explanation Types.medRxiv : the preprint server for health sciences · 2026Article
- Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study.PLoS medicine · 2026Article
- Doctors vs. Algorithms: Physicians, too, struggle to learn from evidence that contradicts AI suggestions.PLOS digital health · 2026Article
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
- Do AI-based contouring algorithms influence physicians in the online adaptive radiotherapy of patients with bladder cancer?Technical innovations & patient support in radiation oncology · 2026Article
- Factors influencing the effectiveness of artificial intelligence-assisted decision-making in medicine: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026Article
- Artificial Intelligence and the Potential Transformation of Mental Health.JAMA psychiatry · 2026Article
- Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2026Article
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- AI-augmented decision-making in face matching: comparing concurrent and non-concurrent advice presentation.Cognitive research: principles and implications · 2026Article
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
6 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Decision support systems embodying machine learning models offer the promise of an improved standard of care for major depressive disorder, but little is known about how clinicians' treatment decisions will be influenced by machine learning recommendations and explanations. We used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation. We found that interacting with ML recommendations did not significantly improve clinicians' treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently. Interacting with incorrect recommendations paired with explanations that included limited but easily interpretable information did lead to a significant reduction in treatment selection accuracy compared to baseline questions. These results suggest that incorrect ML recommendations may adversely impact clinician treatment selections and that explanations are insufficient for addressing overreliance on imperfect ML algorithms. More generally, our findings challenge the common assumption that clinicians interacting with ML tools will perform better than either clinicians or ML algorithms individually.
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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.