AI-supported clinical decision systems optimizing multimodal PTSD treatment selection for veterans with co-occurring traumatic brain injury and depression

Post-traumatic stress disorder (PTSD) in military veterans frequently co-occurs with traumatic brain injury (TBI) and major depression, creating a complex clinical profile that challenges traditional treatment-selection approaches. Multimodal care including psychotherapy, pharmacotherapy, neurocognitive rehabilitation, and emerging neuromodulation techniques can improve outcomes, but determining the optimal combination for individual patients remains difficult due to heterogeneous symptom trajectories, overlapping neurobiological signatures, and varied treatment responses. This complexity has led to growing interest in artificial intelligence (AI)–supported clinical decision systems capable of integrating diverse data streams to guide personalized care pathways. AI-enabled platforms can analyze multimodal inputs such as electronic health records, neuroimaging markers, psychometric scales, behavioral data, and longitudinal treatment outcomes to generate predictive models for individualized therapy selection. Machine learning algorithms, particularly ensemble and deep learning architectures, are capable of identifying latent patterns linking neurocognitive impairment, affective dysregulation, trauma exposure profiles, and prior treatment responses to future clinical improvement. For veterans with co-occurring TBI and depression, these systems may help distinguish between PTSD-driven symptoms and TBI-related cognitive deficits, improving the precision of treatment assignments and reducing trial-and-error prescribing. Clinical decision systems also support multimodal treatment optimization by recommending combinations such as cognitive processing therapy with SSRIs, TBI-focused cognitive rehabilitation, or adjunctive neuromodulation based on predicted synergistic effects and individual risk factors. When integrated into clinical workflows, AI-supported tools can enhance early intervention, reduce care variability, and support measurement-based monitoring. By aligning clinical expertise with data-driven insights, AI-supported decision systems offer a scalable pathway for improving PTSD treatment outcomes in veterans with complex comorbidities. Their use has the potential to reduce symptom chronicity, enhance functional recovery, and support more efficient allocation of healthcare resources.