This narrative review synthesises peer-reviewed biomedical literature, grey literature, institutional reports and applied case studies published between 2015 and 2025 to examine how evidence for DHIs is generated in LMICs and argues for a staged, life-cycle-based approach to evidence generation.
Gareth Obery, E. Weicken, Shubhanan Upadhyay et al.· BMJ digital health & AI· 0 citations
This scoping review aims to systematically map the harms which may arise from engaging with AI for mental health support, as formulated in the academic literature, regulatory frameworks, grey literature, and practitioner guidance, to inform the co-production of a harm taxonomy.
X. Hunt, A. G. Mokaya, Sara Zannone et al.· Wellcome Open Research· 0 citations
Alignment plausibility is proposed as a regulatory construct for AI in health: a principled way to argue for, or against, trust that systems are aligned to positive health outcomes, will cause no harm even where capable of doing so, and will ultimately lead to patient benefit.
G. Williams, Sara Zannone, B. Mateen· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
