Zhang Xiaoyu, Zhang Deng, Zhang Xifan, Zhang Xin
First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
Department of Infectious Diseases, The First Affiliated Hospital of Xiamen University, Xiamen, China.
Front Microbiol. 2024 Aug 6;15:1449844. doi: 10.3389/fmicb.2024.1449844. eCollection 2024.
The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.
21世纪，医学和公共卫生领域中细菌感染的诊断与治疗依旧面临重大挑战。人工智能（AI）已成为诊断和治疗细菌感染的一项强大新工具。人工智能正在迅速变革传染病流行病学研究，为疫情提供有效的早期预警、预防和控制。机器学习模型提供了一种高度灵活的方式来模拟和预测病原体与宿主相互作用的复杂机制，这对于全面理解疾病本质至关重要。基于机器学习的病原体鉴定技术和抗菌药物敏感性测试突破了传统方法的局限，显著缩短了从样本采集到结果判定的时间，极大提高了实验室检测的速度和准确性。此外，人工智能技术在治疗细菌感染中的应用，特别是在药物和疫苗研发以及噬菌体等创新疗法的应用方面，为改善治疗和遏制细菌耐药性提供了新策略。尽管人工智能在细菌感染诊断和治疗方面具有广阔的应用前景，但在数据质量和数量以及模型可解释性、临床整合和患者隐私保护方面仍存在重大挑战。为克服这些挑战并在临床实践中实现广泛应用，跨学科合作、技术创新和政策支持是共同努力的关键要素。总之，随着人工智能技术的不断进步和深入应用，人工智能将使医生能够更有效地应对细菌感染挑战，推动医疗实践朝着精准、高效和个性化发展；为患者优化最佳护理和治疗方案；并为公共卫生安全提供有力支持。
