Kamitani Emiko, Koenig Linda J, Sullivan Patrick
Division of HIV Prevention, the US Centers for Disease Control and Prevention.
Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
AIDS. 2025 Aug 1;39(10):1311-1321. doi: 10.1097/QAD.0000000000004220. Epub 2025 Jul 10.
Artificial intelligence (AI) holds significant potential to transform HIV prevention and treatment through the application of advanced technologies such as machine learning (ML), deep learning (DL), and generative AI (Gen AI). These technologies can enhance the monitoring, management, and analysis of vast and complex HIV-related datasets, enabling more timely predictions of potential risks and improving HIV care strategies. AI is poised to streamline HIV prevention interventions by increasing workforce efficiency, supporting expanded accessibility and sustainability of preexposure prophylaxis (PrEP) care in nontraditional settings, and supporting clinical decision-making. Additionally, when utilized within HIV care systems, AI can help close gaps in diagnosis, treatment, and continuous care engagement. However, to optimize AI's potential in HIV prevention, careful implementation is crucial. Challenges such as reducing bias, ensuring ethical standards (including health privacy standards) are maintained, and mitigating risks like AI hallucinations must be addressed. Thoughtful integration, community consultation, and continuous evaluation will be critical to ensuring that AI plays a beneficial role in HIV prevention and drives innovations that lead to more equitable health outcomes. This editorial review explores AI's transformative potential, focusing on the US CDC's key public health strategies for HIV prevention. When aligning with public health strategies - particularly in countries supported by initiatives like President's Emergency Plan for AIDS Relief (PEPFAR) - AI can contribute significantly to global efforts to end the HIV epidemic. It offers a vision for AI's future application in HIV prevention, emphasizing the need for a holistic and syndemic approach to improving HIV prevention worldwide.
人工智能（AI）通过应用机器学习（ML）、深度学习（DL）和生成式人工智能（Gen AI）等先进技术，在改变艾滋病毒预防和治疗方面具有巨大潜力。这些技术可以加强对大量复杂的艾滋病毒相关数据集的监测、管理和分析，从而更及时地预测潜在风险并改进艾滋病毒护理策略。人工智能有望通过提高工作人员效率、支持在非传统环境中扩大暴露前预防（PrEP）护理的可及性和可持续性以及支持临床决策，来简化艾滋病毒预防干预措施。此外，在艾滋病毒护理系统中使用时，人工智能可以帮助缩小诊断、治疗和持续护理参与方面的差距。然而，为了优化人工智能在艾滋病毒预防方面的潜力，谨慎实施至关重要。必须解决诸如减少偏差、确保维持道德标准（包括健康隐私标准）以及减轻人工智能幻觉等风险等挑战。深思熟虑的整合、社区咨询和持续评估对于确保人工智能在艾滋病毒预防中发挥有益作用并推动创新以实现更公平的健康结果至关重要。这篇编辑评论探讨了人工智能的变革潜力，重点关注美国疾病控制与预防中心（CDC）预防艾滋病毒的关键公共卫生策略。当与公共卫生策略保持一致时——特别是在由总统艾滋病紧急救援计划（PEPFAR）等倡议支持的国家——人工智能可以为全球终结艾滋病毒流行的努力做出重大贡献。它为人工智能未来在艾滋病毒预防中的应用提供了愿景，强调需要采取整体和综合疾病方法来改善全球的艾滋病毒预防工作。
