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TRUVACE RECORD VERSION
record: TRV-2026-1158
version: 2
kind: revised
reason: Direction audit: the problem line stated no AI consequence and was removed. Filed as context; not counted as a gain or a problem.
timestamp: 2026-10-26T06:38:41.139457Z
status: published
lens: general
sector: health
headline: [Tailoring care to the mind: The emerging role of artificial intelligence in precision psychiatry. Part II: Validating, regulating and integrating artificial intelligence into care]
dek: Objectives Precision psychiatry seeks to tailor care to the clinical, biological, cognitive, behavioral, and contextual characteristics of each patient. Artificial intelligence may help achieve this goal by combining multimodal data, identifying complex patterns, following individual trajectories, and refining prognostic or therapeutic stratification. Yet strong statistical performance does not automatically translate into meaningful clinical benefit. This second part of Tailoring Care to the Mind: The Emerging…
gain_title: (none)
problem_title: [Tailoring care to the mind: The emerging role of artificial intelligence in precision psychiatry. Part II: Validating, regulating and integrating artificial intelligence into care]: Particular attention was given to discrimination, calibration, decision usefulness, external validation, bias, acceptability, interpretability, responsibility, and human oversight.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: [Tailoring care to the mind: The emerging role of artificial intelligence in precision psychiatry. Part II: Validating, regulating and integrating artificial intelligence into care]: Particular attention was given to discrimination, calibration, decision usefulness, external validation, bias, acceptability, interpretability, responsibility, and human oversight.
problem_evidence: (none)
quick_read: Objectives Precision psychiatry seeks to tailor care to the clinical, biological, cognitive, behavioral, and contextual characteristics of each patient. Artificial intelligence may help achieve this goal by combining multimodal data, identifying complex patterns, following individual trajectories, and refining prognostic or therapeutic stratification.

This second part of Tailoring Care to the Mind: The Emerging Role of Artificial Intelligence in Precision Psychiatry explores the conditions required for an algorithm to become a reliable, acceptable, equitable, and responsible clinical innovation. Ethical legitimacy further requires clear rules for data access, secondary use, accountability, and the possibility of questioning or overriding an algorithmic recommendation.
limitation: 
tag: Context
key_points: Objectives Precision psychiatry seeks to tailor care to the clinical, biological, cognitive, behavioral, and contextual characteristics of each patient. | Artificial intelligence may help achieve this goal by combining multimodal data, identifying complex patterns, following individual trajectories, and refining prognostic or therapeutic stratification. | Yet strong statistical performance does not automatically translate into meaningful clinical benefit.
rundown: Objectives Precision psychiatry seeks to tailor care to the clinical, biological, cognitive, behavioral, and contextual characteristics of each patient. Artificial intelligence may help achieve this goal by combining multimodal data, identifying complex patterns, following individual trajectories, and refining prognostic or therapeutic stratification.

Yet strong statistical performance does not automatically translate into meaningful clinical benefit. This second part of Tailoring Care to the Mind: The Emerging Role of Artificial Intelligence in Precision Psychiatry explores the conditions required for an algorithm to become a reliable, acceptable, equitable, and responsible clinical innovation.
sources:
- peer_reviewed | L'Encéphale | https://doi.org/10.1016/j.encep.2026.08.007 | 2026-09-19
prev: 18097b0a292fe405cf37636c541ff61633a45aeeb85903bfc9881deae486b97e

SHA-256 hash

7c97b356342c67d24e477a1b51bab4c930263d689b72354b64ca414de8511bdf

Previous version’s hash

18097b0a292fe405cf37636c541ff61633a45aeeb85903bfc9881deae486b97e