Key result
Modern artificial intelligence and machine learning architectures could shift perioperative medicine beyond static risk calculators toward dynamic representations of physiological state over time.
Modern artificial intelligence and machine learning architectures may shift perioperative risk assessment from static risk calculators to dynamic, multimodal representations of physiological state.
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.
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Whittle et al. (2026) conducted a review in Surgical patients. Artificial intelligence and machine learning architectures vs. Static models was evaluated. Modern artificial intelligence and machine learning architectures could shift perioperative medicine beyond static risk calculators toward dynamic representations of physiological state over time.
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