aceOptimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability
Bipolar disorder (BD) is a complex and heterogeneous psychiatric condition, characterized by fluctuating clinical courses that affect approximately 1%-2% of the global population in their lifetime. Despite pharmacological advances, treatment response varies significantly among patients, making the identification of individualized treatment strategies a major challenge. Artificial Intelligence (AI), through its classical approaches, has emerged as a powerful tool in precision psychiatry to identify subtle pattern…
Systematic review of 35 studies found classical AI models for bipolar disorder achieved pooled AUC 0.80 for long-term maintenance response and 85%-97% accuracy for safety and dose optimization.
Most classical AI models for bipolar disorder treatment optimization had high risk of bias and lack of external validation, and remain exploratory rather than ready for clinical use.
Most included models had high risk of bias due to data analysis limitations, small sample sizes, and lack of external validation, with 3 studies flagged for small samples with disproportionately high performance.
Evidence
- Peer-reviewedJMIR Mental Health2026-07-21
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Truvace Impact Record TRV-2026-0511, v1: “Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability.” Truvace, 2026-07-22. /record/TRV-2026-0511 (accessed at citation time). sha256 a3e8c623daa69bd3…
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