McClean Mairi, Panciu Traian Constantin, Lange Christoph, Duarte Raquel, Theis Fabian
Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany.
Breathe (Sheff). 2024 Dec 10;20(3):240056. doi: 10.1183/20734735.0056-2024. eCollection 2024 Oct.
The challenges to effective tuberculosis (TB) disease control are considerable, and the current global targets for reductions in disease burden seem unattainable. The combination of complex pathophysiology and technical limitations results in difficulties in achieving consistent, reliable diagnoses, and long treatment regimens imply serious physiological and socioeconomic consequences for patients. Artificial intelligence (AI) applications in healthcare have significantly improved patient care regarding diagnostics, treatment and basic research. However, their success relies on infrastructures prioritising comprehensive data generation and collaborative research environments to foster stakeholder engagement. This viewpoint article briefly outlines the current and potential applications of advanced AI models in global TB control and the considerations and implications of adopting these tools within the public health community.
有效控制结核病面临诸多挑战，当前全球减轻疾病负担的目标似乎难以实现。复杂的病理生理学与技术限制相结合，导致难以实现持续、可靠的诊断，而漫长的治疗方案对患者意味着严重的生理和社会经济后果。人工智能在医疗保健领域的应用在诊断、治疗和基础研究方面显著改善了患者护理。然而，其成功依赖于优先考虑全面数据生成的基础设施以及促进利益相关者参与的协作研究环境。这篇观点文章简要概述了先进人工智能模型在全球结核病控制中的当前及潜在应用，以及在公共卫生领域采用这些工具的考虑因素和影响。
