The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study - Oo, Chit Thet Lal; Wider, Walton; Pang, Nicholas Tze Ping; Koh, Eugene Boon Yau; Vasanthi, Rajkumar Krishnan; Thet, Khine Zar Zar; Ramalho, Rodrigo; Özdemir, Bilge Nur; Mahboob, Kashmine (Taylor and Francis, 2026)
Purpose: This study explores expert consensus on the benefits and future potential of generative artificial intelligence (GAI) in mental health care, using the Technology Acceptance Model (TAM) to interpret these perceptions. Methods: A two-round Delphi study using a mixed-methods design was conducted with 15 purposively selected experts in psychiatry, clinical psychology, counselling, and digital mental health. Round 1 gathered open-ended responses that were thematically analysed to identify benefit and future-potential dimensions. In Round 2, experts ranked these dimensions, and consensus was assessed using Kendall’s coefficient of concordance. Results: Twenty-eight themes were identified across eight benefit dimensions, and 29 themes across eight future-potential dimensions. Statistically significant consensus was achieved for both benefits (W = 0.145, p = 0.034) and future potential (W = 0.152, p = 0.025). Accessibility and availability ranked as the most important current benefit, while AI as a collaborative and informative tool was prioritised for future application. Discussion: Experts perceived GAI as a transformative adjunct to mental health practice, particularly in expanding access, supporting personalised care, and augmenting professional capacity. Adoption is contingent on usability, transparency, trust, and robust ethical governance to ensure equitable and human-centred integration.