McCoy Liam G, Bihorac Azra, Celi Leo Anthony, Elmore Matthew, Kewalramani Divya, Kwaga Teddy, Martinez-Martin Nicole, Prôa Renata, Schamroth Joel, Shaffer Jonathan D, Youssef Alaa, Fiske Amelia
Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
BMC Glob Public Health. 2025 May 2;3(1):39. doi: 10.1186/s44263-025-00158-6.
The development of artificial intelligence (AI) applications in healthcare is often positioned as a solution to the greatest challenges facing global health. Advocates propose that AI can bridge gaps in care delivery and access, improving healthcare quality and reducing inequity, including in resource-constrained settings. A broad base of critical scholarship has highlighted important issues with healthcare AI, including algorithmic bias and inequitable and inaccurate model outputs. While such criticisms are valid, there exists a much more fundamental challenge that is often overlooked in global health policy debates: the dangerous mismatch between AI's imagined benefits and the material realities of healthcare systems globally. AI cannot be deployed effectively or ethically in contexts lacking sufficient social and material infrastructure and resources to provide effective healthcare services. Continued investments in AI within unprepared, under-resourced contexts risk misallocating resources and potentially causing more harm than good. The article concludes by providing concrete questions to assess AI systemic capacity and socio-technical readiness in global health.
人工智能（AI）在医疗保健领域的应用发展常常被视为应对全球健康面临的最大挑战的解决方案。倡导者提出，人工智能可以弥合医疗服务提供和可及性方面的差距，提高医疗质量并减少不平等现象，包括在资源有限的环境中。广泛的批判性学术研究突出了医疗保健人工智能的重要问题，包括算法偏见以及不公平和不准确的模型输出。虽然这些批评是合理的，但在全球卫生政策辩论中，存在一个更根本的挑战常常被忽视：人工智能所设想的好处与全球医疗系统的物质现实之间存在危险的不匹配。在缺乏足够的社会和物质基础设施及资源以提供有效医疗服务的情况下，无法有效或合乎道德地部署人工智能。在准备不足、资源匮乏的情况下持续对人工智能进行投资，可能会导致资源分配不当，且可能弊大于利。文章最后提出了具体问题，以评估全球卫生领域中人工智能的系统能力和社会技术准备情况。
