Artificial Intelligence for Psychological Treatment Selection: Predicting Treatment Outcomes and Understanding Clinician and Patient Perspectives

Objectives
Artificial Intelligence (AI) use in clinical decision-making could offer numerous benefits for clinicians, patients, and services. However, AI use in mental healthcare is in its infancy, and little is known about its acceptability to patients and therapists. This qualitative study aimed to better understand perceptions of AI in mental healthcare, and how AI may influence therapy expectations and shared decision-making (SDM).

Methods
Semi-structured interviews were conducted with seven patients and 12 clinicians participating in the TherapyMatch-D (TMD) trial, where patients with depression were randomly allocated to AI-informed psychological treatment selection (TMD) or allocation as usual (AAU). Interviews explored clinical decision-making approaches, experiences using the algorithm, and broader views of AI’s role.

Results
Framework analysis identified nine themes capturing clinicians’ and patients’ experiences of treatment selection. Initial themes outlined factors considered within assessments, usual decision-making approaches, and clinicians’ difficulties. TMD participants felt the AI algorithm improved decision-making quality and confidence in treatment decisions. Clinicians valued the algorithm’s efficiency and usability, whilst patient expectations of therapy appeared improved compared to AAU patients. The algorithm could enhance clinician-patient collaboration, supporting SDM, whilst maintaining patient autonomy. Clinicians noted applicability and integration limitations, recommending better integration with electronic health records and continued human involvement in AI-supported decisions.

Conclusions
AI tools such as the TMD algorithm can enhance the quality, confidence, and collaborative nature of decision-making in mental healthcare, supporting SDM and enhancing positive expectations of therapy. Integrating AI into existing systems and retaining human input could help maximise AI tool effectiveness, efficiency, and acceptability.