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<p>Psychiatry’s reliance on symptom-based diagnosis limits its ability to provide personalized and effective treatments. This Perspective proposes Precision Predictive Priors (P³), a conceptual smartphone-based framework intended to complement symptom-based diagnosis by profiling how an individual’s brain manages prediction error. Using brief gamified tasks and passive sensing, P³ aims to derive a dynamic ‘precision signature’ across four transdiagnostic domains—interoceptive, exteroceptive, action-outcome and social—classifying each as hyper-precise (rigidly adhering to prior beliefs), hypo-precise (overly influenced by new data) or flexible. A privacy-preserving, on-device artificial intelligence agent is intended to update this personal model over time, enabling a learning health system that could eventually simulate and suggest targeted micro-interventions only when the predicted benefit is high. This mechanistic vocabulary moves beyond symptom overlap and supports hypothesis-driven targeting of interventions. For example, two individuals with depression might ultimately receive different treatments based on their precision profiles. By translating computational neuroscience into a minimally viable phenotype, this Perspective outlines an empirically grounded roadmap for precision psychiatry that is intended to be rigorous, scalable and clinically relevant, with clinical success measured primarily by improvements in real-world functioning and quality of life, with symptom severity/distress treated as secondary outcomes.</p>

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From symptoms to signatures: a transdiagnostic predictive coding framework for precision psychiatry

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Stanley Lyndon

摘要

Psychiatry’s reliance on symptom-based diagnosis limits its ability to provide personalized and effective treatments. This Perspective proposes Precision Predictive Priors (P³), a conceptual smartphone-based framework intended to complement symptom-based diagnosis by profiling how an individual’s brain manages prediction error. Using brief gamified tasks and passive sensing, P³ aims to derive a dynamic ‘precision signature’ across four transdiagnostic domains—interoceptive, exteroceptive, action-outcome and social—classifying each as hyper-precise (rigidly adhering to prior beliefs), hypo-precise (overly influenced by new data) or flexible. A privacy-preserving, on-device artificial intelligence agent is intended to update this personal model over time, enabling a learning health system that could eventually simulate and suggest targeted micro-interventions only when the predicted benefit is high. This mechanistic vocabulary moves beyond symptom overlap and supports hypothesis-driven targeting of interventions. For example, two individuals with depression might ultimately receive different treatments based on their precision profiles. By translating computational neuroscience into a minimally viable phenotype, this Perspective outlines an empirically grounded roadmap for precision psychiatry that is intended to be rigorous, scalable and clinically relevant, with clinical success measured primarily by improvements in real-world functioning and quality of life, with symptom severity/distress treated as secondary outcomes.