The Clinician in the Loop: How Multimodal AI Affects Clinical Decision-Making in Mental Healthcare

Multimodal artificial intelligence (AI) presents a promising avenue for enhancing precision in psychiatry; however, practical and ethical challenges hinder its clinical adoption. This study surveyed thirty-three mental health professionals to investigate the impact of AI-generated multimodal data on clinical decision-making, perceived data reliability, and barriers to implementation. Results demonstrated that multimodal data significantly altered clinical management. Data suggesting a clear diagnostic shift prompted clinicians to adjust their recommendations, whereas nonspecific data had little effect. The low explainability of multimodal data was a significant factor in clinician responses and actually led to clinical errors. Clinicians identified ethical concerns, patient trust, and knowledge gaps as the primary barriers to implementation, rather than logistical issues. These findings suggest that while clinicians are receptive to using specific, interpretable multimodal data to augment their reasoning, the widespread adoption of AI in psychiatry depends on building trust through education, transparency, and robust clinical validation.