Bridging algorithmic prediction and clinical agency: an exploratory pilot study of AI-augmented physician antidepressant choice - Takeaways - MDSpire

Bridging algorithmic prediction and clinical agency: an exploratory pilot study of AI-augmented physician antidepressant choice

  • By

  • Akiva Kleinerman

  • David Benrimoh

  • Amit Yaniv-Rosenfeld

  • Grace Golden

  • Myriam Tanguay-Sela

  • Howard C. Margolese

  • Teddy Lazebnik

  • Ben H. Amit

  • Hadar Samuel

  • Ariel Rosenfeld

  • July 3, 2026

  • 0 min

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  • 1

    The study evaluated three weighting schemes for AI-assisted antidepressant selection: Implicit, Static Expert-Derived, and Dynamic Clinician-Determined.

  • 2

    Dynamic Clinician-Determined Weighting significantly improved perceived clinical utility and led to data-informed revisions in 33.3% of cases.

  • 3

    The pilot study involved 22 physicians and assessed how different weighting mechanisms affect treatment decisions in Major Depressive Disorder.

  • 4

    The findings suggest that flexible decision support systems can enhance the integration of AI into psychiatric practice while preserving clinician agency.

  • 5

    The research highlights the importance of balancing algorithmic predictions with clinical judgment in the pharmacological management of MDD.

Original Source(s)

Bridging algorithmic prediction and clinical agency: an exploratory pilot study of AI-augmented physician antidepressant choice

  • by Akiva Kleinerman, David Benrimoh, Amit Yaniv-Rosenfeld, Grace Golden, Myriam Tanguay-Sela, Howard C. Margolese, Teddy Lazebnik, Ben H. Amit, Hadar Samuel, Ariel Rosenfeld

  • July 3, 2026

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