"Trying to Do No Harm": exploring clinician concerns towards the use of AI for risk prediction in psychiatry.

BACKGROUND: The application of artificial intelligence (AI) in healthcare is expanding, including in psychiatry. However, its successful adoption depends on clinician acceptance and trust. Despite the growing interest, there remains a knowledge gap in understanding the clinician perspectives and concerns, towards AI in psychiatry. OBJECTIVE: This qualitative, pre-implementation study explored clinician concerns and perceived barriers towards the application of predictive AI for clinical outcomes in a large mental health hospital. METHODS AND ANALYSIS: Four virtual focus groups were conducted with 16 clinicians who provided care at a large mental health hospital in Ontario, Canada. Two focus groups (n=9) included physicians, and two (n=7) included allied clinicians. Participants discussed their awareness and concerns with predictive AI for clinical outcomes. Transcripts were analysed using reflexive thematic analysis. RESULTS: Six themes emerged regarding clinician willingness to use and implement AI for clinical outcome prediction in mental healthcare: AI model performance, quality of data sources, system issues, end-user behaviours, patient outcomes and clinician well-being. Subthemes included the absence of technical infrastructure, quality data to support AI development, the 'black box phenomenon' of AI algorithms, loss of critical thinking, medicolegal concerns and the potential harms from over-intervening. CONCLUSION: To our knowledge, this is the first qualitative study that uses focus groups to explore a full range of clinician attitudes towards machine learning-based prediction tools in mental healthcare settings. It highlights major areas of concern for emerging AI technology. Understanding clinicians' perspectives is critical to identifying barriers to the introduction of AI in psychiatry.