Investigating How Clinicians Form Trust in an AI-Based Mental Health Model: Qualitative Case Study
Clinicians' trust in AI tools is contextually and sequentially constructed, influenced by both model performance and alignment with clinical reasoning. Interpretability features were essential in establishing intrinsic trust, particularly when presented in ways that resonate with clinical norms. For broader acceptance and responsible deployment, trust must be supported by rigorous evaluation data and the inclusion of clinically relevant data types in model design.
Authors
- Pepijn W.J. van de Ven (ORCID: https://orcid.org/0000-0003-3321-450X)
- John E. Williams (ORCID: https://orcid.org/0000-0002-1353-2685)
- Trine Theresa Holmberg Sainte-Marie (ORCID: https://orcid.org/0000-0001-8379-0906)
- Ruth Melia (ORCID: https://orcid.org/0000-0002-1152-5510)
- Nancy Bhardwaj (ORCID: https://orcid.org/0000-0003-1205-9635)
- Anthony Kelly (ORCID: https://orcid.org/0000-0003-1798-1506)
- Kim Mathiasen (ORCID: https://orcid.org/0000-0001-6067-8866)
- Amalie Søgaard Nielsen (ORCID: https://orcid.org/0000-0002-2884-3902)
Institutions
- University of Southern Denmark (DK)
- Aarhus University (DK)
- University of Limerick (IE)
- Quality of Life Research Center (DK)
Publication Details
- Journal
- JMIR Human Factors
- Published
- 2025-11-20
- DOI
- https://doi.org/10.2196/79658
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00