Antimicrobial resistance(AMR) has become one of the greatest public health crises of the 21st century, responsible for an estimated five million lives annually and escalating healthcare costs worldwide. The excessive use of antibiotics in human medicine, agriculture, and animal husbandry continues to accelerate the development of resistance, especially in low- and middle-income countries. While traditional diagnostic methods remain indispensable, they are often too slow and fragmented to respond to rapidly evolving pathogens. Meanwhile, healthcare systems face increasing difficulty in integrating vast amounts of genomic, clinical, and epidemiological data. Faced with these mounting challenges, researchers are exploring AI-driven tools to predict resistance patterns, optimize antibiotic use, and strengthen early detection and intervention strategies.
A research team from Peking Union Medical College Hospital and Xiangya Third Hospital of Central South University has published (DOI: 10.12290/xhyxzz.2025-0655) a comprehensive review in the Medical Journal of Peking Union Medical College Hospital (September 2025), shedding light on how AI is revolutionizing the prevention and control of AMR. The article illustrates how machine learning and deep learning are transforming surveillance, diagnosis, treatment optimization, and drug discovery—offering a timely blueprint for integrating intelligent systems into global infection management.
The review details how AI technologies are being applied across four major fronts of AMR prevention. First, in epidemiological surveillance and early warning, AI algorithms such as XGBoost analyze hospital resistance records and antibiotic consumption data to forecast future outbreaks, helping health agencies act before crises escalate. Natural language processing systems can even scan electronic records and social media to detect resistance “hotspots” in real time. Second, in resistance detection and prediction, AI-powered models trained on MALDI-TOF mass spectrometry and genomic data can identify resistant bacteria within hours—far faster than traditional culture tests. Models trained on more than 300,000 bacterial samples achieved high predictive accuracy for Staphylococcus aureus and Klebsiella pneumoniae, demonstrating clinical readiness. Third, in clinical decision-making, AI-based systems reduce mismatched antibiotic prescriptions by up to half and promote rational drug use in hospitals. Finally, in drug discovery, deep learning models such as those that identified halicin and abaucin reveal entirely new classes of antibiotics with unique mechanisms. Together, these AI advances are redefining how humanity detects, treats, and prevents resistance on a global scale.
“AI is transforming our fight against antimicrobial resistance from reactive to predictive,” said corresponding author Dr. Li Zhang. “By integrating genomic, clinical, and environmental data, AI systems can uncover hidden transmission patterns and recommend tailored treatments faster than ever before. Yet to achieve full impact, we must also enhance data quality, ensure algorithmic transparency, and strengthen ethical oversight. Through cross-disciplinary collaboration, AI can bridge the gap between innovation and implementation—transforming smart technologies into lifesaving public health tools.”
The convergence of AI and infectious disease science signals a paradigm shift in global health defense. In hospitals, AI-driven diagnostic and decision-support tools enable clinicians to deliver faster, more targeted therapies, reducing antibiotic misuse and improving patient outcomes. On a broader scale, predictive analytics guide surveillance and resource allocation, facilitating early containment of resistant pathogens. In pharmaceutical research, AI accelerates drug discovery by exploring chemical spaces beyond human intuition. As the technology continues to evolve, standardizing data, building interpretable models, and fostering global collaboration will be essential. AI is poised to become a cornerstone of precision infection control and sustainable healthcare.
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References
DOI
10.12290/xhyxzz.2025-0655
Original Source URL
https://xhyxzz.pumch.cn/article/doi/10.12290/xhyxzz.2025-0655
Funding Information
National Natural Science Foundation of China (No. 82472341); Public Competitive Program of the Chinese Medical Foundation (No. 23-520); Medical and Health Science and Technology Innovation Engineering Project of the Chinese Academy of Medical Sciences (No. 2021-I2M-1-044).
About Medical Journal of Peking Union Medical College Hospital
Medical Journal of Peking Union Medical College Hospital is a leading clinical medicine publication, supported by the multidisciplinary expertise of Peking Union Medical College Hospital. It features the latest research, advancements, and academic trends in clinical and translational medicine, pharmacy, and related interdisciplinary fields, catering to clinicians and medical students across China. The journal aims to promote the exchange of medical knowledge and serve as a high-quality platform for leading academic discussions and fostering scholarly debate in clinical medicine. The journal is listed in China's Core Journals of Science and Technology (CSTPCD), Chinese Science Citation Database (CSCD), A Guide to the Core Journals of China, and the Chinese Biomedical Literature Database (CMCC). Full-text content is accessible on platforms such as Wanfang Data, CNKI, and Chongqing VIP Database. It is indexed in Scopus (Netherlands), the Directory of Open Access Journals (DOAJ) in Sweden, and the Japan Science and Technology Agency Database (JST).