Artificial Intelligence in suicide prevention: promise, peril, and clinical implications for clinical practice

The integration of Artificial Intelligence (AI) into psychiatry is progressing rapidly, with promising yet underexplored applications in suicide prevention. While most existing studies have focused on suicide risk prediction, AI could be applied more broadly across the suicide prevention continuum. This review explores AI's potential in improving the detection of suicidal ideation (SI), enhancing clinician training, and expanding patient access to psychotherapeutic support. Studies suggest that AI models may identify SI more sensitively than clinicians by detecting subtle linguistic or semantic cues. Furthermore, AI-based virtual patients have been shown to improve clinicians' confidence and accuracy in suicide risk assessments, particularly when used alongside traditional training methods. On the patient side, CBT-inspired AI tools offer scalable, on-demand support, but most remain unregulated and clinically unvalidated. Despite their perceived empathy and availability, such tools pose ethical and clinical risks. Many patients use AI tools without professional guidance, a trend clinicians often underestimate. This disconnect calls for urgent regulatory oversight and professional engagement. Without clear clinical validation, AI-based tools may evolve outside medical frameworks, exposing vulnerable individuals to preventable harm. Psychiatry must not only adapt but also lead, ensuring that innovation is harnessed responsibly to serve patients with empathy, safety, and scientific rigor.