A new review published in Brain Medicine on Mar. 10 calls for psychiatry to move beyond traditional symptom checklists and adopt a more biologically grounded approach to diagnosing mental illness. The review, authored by Dr. Jakub Tomasik, Jihan K. Zaki, and Professor Sabine Bahn from the Cambridge Centre for Neuropsychiatric Research at the University of Cambridge, synthesizes recent advances in biomarker science, digital phenotyping, and artificial intelligence as potential pathways toward improved psychiatric diagnosis.
The topic is significant because psychiatry remains one of the few medical fields that relies primarily on patient interviews and symptom lists rather than laboratory tests or imaging. The authors argue that current diagnostic systems such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) have standardized language but do not reflect underlying biological mechanisms.
"While the DSM and ICD provide an essential framework for psychiatric classification, they fall short of capturing the true nature of mental illness," said Professor Sabine Bahn. "These systems lack a firm biological basis, yield highly heterogeneous and partly overlapping categories, impose arbitrary thresholds, and rely on subjective judgments that vary across clinicians. Perhaps most critically, diagnostic labels often do not predict prognosis or guide effective treatment."
The review examines alternative frameworks including network models, hierarchical taxonomies like HiTOP, research domain criteria (RDoC), and clinical staging approaches. It also surveys molecular research showing genetic overlap among disorders such as schizophrenia, bipolar disorder, and depression but notes that most biomarkers remain limited to research settings.
Digital phenotyping using smartphones or wearables is highlighted as a way to capture real-time behavioral data relevant to mood disorders. However, the authors caution that these technologies require further validation before widespread clinical use. Artificial intelligence models are described as promising adjuncts but face challenges related to data quality and explainability.
"At present, AI models should be regarded as adjunctive decision-support systems that complement clinical judgment rather than standalone diagnostic instruments," said Jihan K. Zaki.
The review concludes by emphasizing practical steps forward: integrating validated biomarkers with digital tools while ensuring transparency and equity in deployment. "By combining scientific and technological advances with clinical expertise, psychiatry can build a diagnostic process that is more consistent, more personalized, and ultimately more effective in improving outcomes for patients," said Bahn.
