{"title":"Beyond risk scores: artificial intelligence representations of physiological resilience in surgical patients.","authors":"John Whittle, Evangelos Mazomenos","doi":"10.1016/j.bja.2026.08.037","DOIUrl":null,"url":null,"abstract":"<p><p>Current perioperative risk assessment relies largely on static models that estimate the probability of adverse outcomes using indirect, population-level proxies of physiological reserve. We argue that physiological resilience is better understood as an emergent, dynamic property of interacting biological systems, expressed within the broader context of surgical stress, anaesthetic care, and perioperative management. Modern artificial intelligence and machine learning architectures offer a paradigm shift, moving perioperative medicine beyond static risk calculators toward dynamic, multimodal representations of physiological state over time. Such representations provide the substrate for prescriptive analytics, actionable clinical decision support, and mechanism-informed perioperative care.</p>","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":" ","pages":""},"PeriodicalIF":10.3000,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British journal of anaesthesia","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.bja.2026.08.037","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Current perioperative risk assessment relies largely on static models that estimate the probability of adverse outcomes using indirect, population-level proxies of physiological reserve. We argue that physiological resilience is better understood as an emergent, dynamic property of interacting biological systems, expressed within the broader context of surgical stress, anaesthetic care, and perioperative management. Modern artificial intelligence and machine learning architectures offer a paradigm shift, moving perioperative medicine beyond static risk calculators toward dynamic, multimodal representations of physiological state over time. Such representations provide the substrate for prescriptive analytics, actionable clinical decision support, and mechanism-informed perioperative care.
期刊介绍：
The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience.
The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence.
Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.
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