GuidelineThe international journal of neuropsychopharmacology2026
Responsible artificial intelligence integration framework for psychiatric guidelines.
Guideline in The international journal of neuropsychopharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Leveraging prompt-driven generative AI for systematic reviews in digital psychiatry: A stage-matched comparative proof-of-concept for healthcare researchers and clinicians.PLOS digital health · 2026Article
- Disclosure is not documentation: an open science framework for documenting generative AI use in scholarly research and publication workflows.Research integrity and peer review · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Artificial intelligence (AI) is reshaping medicine, promising advances in diagnosis, monitoring, and treatment, and psychiatry will be no exception. Yet the field remains fragmented: ethical guidelines, technical standards, and clinical workflows have evolved in parallel, creating uncertainty about how to integrate AI safely and meaningfully into psychiatric care. Existing frameworks often address isolated domains (explainability, data protection, or harm prevention [HP]) without providing a coherent structure that connects them to everyday clinical realities. This article introduces a global framework for the responsible integration of AI in psychiatry, built on 4 non-negotiable system capabilities: Explainable AI to ensure transparency and trust; Shared Decision-Making to protect patient autonomy; Electronic Health Record integration to secure continuity and accountability; and HP to embed multilayered safety controls. Together, these pillars define a responsibility-by-design approach that aligns technological development with psychiatry's ethical foundations. The framework offers clinicians, policymakers, and developers a roadmap for aligning innovation with human values and measurable improvements in clinical outcomes. By translating ethical commitments into auditable, non-negotiable system capabilities, it establishes a concrete foundation for regulatory oversight, guideline endorsement, and responsible AI deployment in psychiatry.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.