Machine Learning for Precision Psychiatry: Opportunities and Challenges.

By Danilo Bzdok, Andreas Meyer-Lindenberg. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2017

756 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/693006ca666aa1156677ecaa52bd199fb921d1e0

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Citations in the corpus

Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, methodology): “Machine learning (ML) is an AI approach that involves various methods of enabling an algorithm to learn [27, 29 (cid:129) , 31 – 35].”
Automated assessment of psychiatric disorders using speech: A systematic review (2019, background): “34 Therefore, complementing clinical interviews with machine learning models trained on the recordings of these interviews could improve outcomes, save clinicians' time, reduce health care costs, and make treatment planning more efficient.”
ChatGPT outperforms humans in emotional awareness evaluations (2023, background): “2020), improving the accuracy of diagnosis and prognosis (Bzdok and Meyer-Lindenberg, 2018), promoting the understanding of mental illnesses mechanisms (Braun et al.”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, methodology): “AI may further complement clinical intuition by enhancing diagnostic accuracy by supporting the clinical reasoning process (13) and advancing the mechanistic understanding of mental illnesses (14,15).”
Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis (2020, background): “Nervous system Dementia, Ischemic stroke lesions identification [29], late-life dementia [31], degenerative moment disorders [32] SVM, LR, NB, RF, Hierarchical clustering analyses, and DSI 69–80 ML studies in Nervous systems can help to improve the diagnosis of Nerve system conditions AI-based behav”
Individualized prediction of heroin cue-induced craving using task-based EEG functional connectivity (2026, unknown): “Consequently, it effectively rules out the in ﬂ uence of global arousal or non-speci ﬁ c emotional interference 48 .”