ArticleFrontiers in psychiatry2026
Bridging algorithmic prediction and clinical agency: an exploratory pilot study of AI-augmented physician antidepressant choice.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Effective psychiatric decision-making requires balancing data-driven predictions with clinical agency. This challenge is particularly acute in the pharmacological management of Major Depressive Disorder (MDD), where clinicians must navigate complex, patient-specific trade-offs between remission probabilities and diverse side-effect risks. Although AI-driven Clinical Decision Support Systems (AI-CDSS) can support the prediction of individual treatment outcomes, the optimal mechanism for aggregating multiple clinical criteria remains an open research challenge. Methods: We investigated how the locus of control in the aggregation mechanism affects clinical utility and treatment decisions. Three weighting schemes were evaluated: (1) an Implicit Weighting baseline, in which raw probabilities were presented; (2) a Static Expert-Derived Weighting scheme, using linear aggregation with fixed expert-based weights; and (3) a Dynamic Clinician-Determined Weighting scheme, using linear aggregation with adjustable clinician-defined weights. These schemes were implemented within a prototype decision support system for antidepressant selection and evaluated in a user study with 22 physicians. Results: The Dynamic Clinician-Determined Weighting scheme significantly enhanced perceived clinical utility compared with the alternative approaches (p < 0.01). It also led to the most frequent data-informed revision of physicians' initial unassisted antidepressant choices, occurring in 33.3% of cases. This effect was observed among both psychiatrists and primary care physicians, suggesting that adjustable weighting can support more informed treatment decisions across clinical specialties. Discussion: These findings suggest that effective integration of AI into psychiatric practice requires flexible decision support systems that preserve clinical agency while incorporating data-driven predictions. By allowing clinicians to determine the relative importance of remission probabilities and side-effect risks, dynamic weighting may better reflect the nuanced and individualized nature of mental health care.
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