ReviewFrontiers in psychiatry2023
Out of their minds? Externalist challenges for using AI in forensic psychiatry.
Review in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- When forensic psychiatry clashes with jurisdiction: nemesis and challenges of a patient suffering from glioblastoma.BJPsych open · 2026Article
- AI-driven transformation in forensic medicine education: applications, pedagogical shifts, and future challenges.Frontiers in medicine · 2026Review
- Artificial Intelligence in Healthcare and Psychiatry.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2025Article
- The application of artificial intelligence in forensic pathology: a systematic literature review.Frontiers in medicine · 2025Review
- HYBRIDMINDS-summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses.Frontiers in human neuroscience · 2024Article
- Artificial Intelligence Challenges in the Healthcare Industry: A Systematic Review of Recent Evidence.Healthcare technology lettersReview
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions about future violent behaviour and criminal recidivism pose ethical challenges that require careful deliberation due to their social and legal significance. In this paper, we shed light on these challenges by considering externalist accounts of psychiatric disorders which stress that the presentation and development of psychiatric disorders is intricately entangled with their outward environment and social circumstances. We argue that any use of predictive AI in forensic psychiatry should not be limited to neurobiology alone but must also consider social and environmental factors. This thesis has practical implications for the design of predictive AI systems, especially regarding the collection and processing of training data, the selection of ML methods, and the determination of their explainability requirements.
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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.