Rajput Yousuf, Tarif Tarek, Wolfe Akira, Dawson Eric, Fox Keolu
Department of Computer Science & Indigenous Futures Institute, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States,
Department of Cognitive Science, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States,
Pac Symp Biocomput. 2025;30:734-747. doi: 10.1142/9789819807024_0055.
This paper examines the integration of artificial intelligence (AI) in point-of-care testing (POCT) to enhance diagnostic speed, accuracy, and accessibility, particularly in underserved regions. AI-driven POCT is shown to optimize clinical decision-making, reduce diagnostic times, and offer personalized healthcare solutions, with applications in genome sequencing and infectious disease management. The paper highlights the environmental challenges of AI, including high energy consumption and electronic waste, and proposes solutions such as energy-efficient algorithms and edge computing. It also addresses ethical concerns, emphasizing the reduction of algorithmic bias and the need for equitable access to AI technologies. While AI in POCT can improve healthcare and promote sustainability, collaboration within the POCT ecosystem-among researchers, healthcare providers, and policymakers-is essential to overcome the ethical, environmental, and technological challenges.
本文探讨了人工智能（AI）在即时检验（POCT）中的整合，以提高诊断速度、准确性和可及性，特别是在医疗服务不足的地区。人工智能驱动的即时检验被证明可以优化临床决策、减少诊断时间，并提供个性化医疗解决方案，应用于基因组测序和传染病管理。本文强调了人工智能面临的环境挑战，包括高能耗和电子垃圾，并提出了节能算法和边缘计算等解决方案。它还讨论了伦理问题，强调减少算法偏差以及公平获取人工智能技术的必要性。虽然即时检验中的人工智能可以改善医疗保健并促进可持续发展，但即时检验生态系统内研究人员、医疗服务提供者和政策制定者之间的合作对于克服伦理、环境和技术挑战至关重要。
