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
Overall, while LLM-judges show promise, their inability to handle linguistic and cultural context is a critical limitation, underscoring the need for further investment in scalable evaluation solutions.
G. Williams, S. Rutunda, Floris Nzabakira et al.· npj Digital Medicine· 1 citation
A novel benchmark comprising over 9,000 real-world, point-of-care, multilingual, and multimodal clinical question-answer pairs sourced from frontline health workers in Nigeria reveals several critical insights into the suitability of LLMs as clinical decision support systems in low-resource contexts.
Tobi Olatunji, C. Aka, C. Okocha et al.· medRxiv· 0 citations
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