AI and mental health: evaluating supervised machine learning models trained on diagnostic classifications

• Published in: AI & society Vol. 40; no. 6; pp. 5077 - 5086
• Main Author: van Oosterzee, Anna
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2025; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Classification; Clinical outcomes; Comorbidity; Computer Science; Control; Engineering Economics; Heterogeneity; Learning algorithms; Logistics; Machine learning; Marketing; Mechatronics; Melanoma; Mental disorders; Mental health; Mental health care; Methodology of the Social Sciences; Oncology; Organization; Patients; Performing Arts; Psychiatry; Psychopathology; Remission; Robotics; Skin cancer; Student Forum; Supervised learning; Validity; Supervised machine learning; Ground truth; Precision psychiatry; DSM
• ISSN: 0951-5666; 1435-5655; 1435-5655
• DOI: 10.1007/s00146-024-02012-z

Machine learning (ML) has emerged as a promising tool in psychiatry, revolutionising diagnostic processes and patient outcomes. In this paper, I argue that while ML studies show promising initial results, their application in mimicking clinician-based judgements presents inherent limitations (Shatte et al. in Psychol Med 49:1426–1448. https://doi.org/10.1017/S0033291719000151 , 2019). Most models still rely on DSM (the Diagnostic and Statistical Manual of Mental Disorders) categories, known for their heterogeneity and low predictive value. DSM's descriptive nature limits the validity of psychiatric diagnoses, which leads to overdiagnosis, comorbidity, and low remission rates. The application in psychiatry highlights the limitations of supervised ML techniques. Supervised ML models inherit the validity issues of their training data set. When the model's outcome is a DSM classification, this can never be more valid or predictive than the clinician’s judgement. Therefore, I argue that these models have little added value to the patient. Moreover, the lack of known underlying causal pathways in psychiatric disorders prevents validating ML models based on such classifications. As such, I argue that high accuracy in these models is misleading when it is understood as validating the classification. In conclusion, these models will not will not offer any real benefit to patient outcomes. I propose a shift in focus, advocating for ML models to prioritise improving the predictability of prognosis, treatment selection, and prevention. Therefore, data selection and outcome variables should be geared towards this transdiagnostic goal. This way, ML can be leveraged to better support clinicians in personalised treatment strategies for mental health patients.