Chandra Avinash, Senthilvel Kaviya, Anjum Rifah, Uchegbu Ijeoma, Smith Laura J, Beaumont Helen, Punjabi Reshma, Begum Samina, Marshall Charles R
Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
J Alzheimers Dis. 2025 Apr;104(3):653-655. doi: 10.1177/13872877251319353. Epub 2025 Feb 16.
Digital health innovations hold diagnostic and therapeutic promise but may be subject to biases for underrepresented groups. We explored perceptions of using artificial intelligence (AI) diagnostics for dementia through a focus group as part of the Automated Brain Image Analysis for Timely and Equitable Dementia Diagnosis (ABATED) study. Qualitative feedback from a diverse public engagement group indicated that cultural variations in trust and acceptability of AI diagnostics may be an unrecognised source of real-world inequity. Efforts focused on the adoption of AI diagnostics in memory clinic pathways should aim to recognise and account for this issue.
数字健康创新具有诊断和治疗前景，但可能存在针对代表性不足群体的偏见。作为“及时、公平的痴呆症诊断自动脑图像分析”（ABATED）研究的一部分，我们通过焦点小组探讨了使用人工智能（AI）诊断痴呆症的看法。来自不同公众参与群体的定性反馈表明，AI诊断在信任和可接受性方面的文化差异可能是现实世界中不平等的一个未被认识到的来源。专注于在记忆诊所途径中采用AI诊断的努力应旨在认识并解决这一问题。
