newsNature Machine IntelligenceTrust 88 · LabPublished 21d agoLive · 5d ago
Towards AI-augmented decision making in psychiatry
Nature Machine Intelligence, Published online: 12 June 2026; doi:10.1038/s42256-026-01256-2 Psychiatric disorders are heterogeneous, and care depends on interpreting unstructured longitudinal narratives, creating variability that hinders standardization. A study now shows that a psychiatry-specific large language model (LLM) may help clinicians to deliver more consistent, high-quality care.
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paperThe strength of clinical evidence is recoverable from language model representations but not from their stated gradespaperTeam MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline DynamicspaperCLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical ReasoningpaperClinician-Level Agreement Without Clinical Caution: LLM Evaluator Limits in Medical AI BenchmarkingrepoEmo-gml/Awesome-Mental-Health-LLMs
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paperTeam MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline DynamicspaperCLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical ReasoningrepoEmo-gml/Awesome-Mental-Health-LLMspaperClinician-Level Agreement Without Clinical Caution: LLM Evaluator Limits in Medical AI BenchmarkingpaperRSPC: A Benchmark for Modeling Stress and Psychiatric Conditions in Digitally Mediated Relationships using Psychiatrist AnnotationspaperThe strength of clinical evidence is recoverable from language model representations but not from their stated grades