Entropy in Clinical Decision-Making: A Narrative Review Through the Lens of Decision Theory

Navigating uncertainty is fundamental to sound clinical decision-making. With the advent of artificial intelligence, mathematical approximations of disease states-expressed as entropy-offer a novel approach to quantify and communicate uncertainty. Although entropy is well established in fields like physics and computer science, its technical complexity has delayed its routine adoption in clinical reasoning. In this narrative review, we adhere to Shannon's definition of entropy from information processing theory and examine how it has been used in clinical decision-making over the last 15 years. Grounding our analysis in decision theory-which frames decisions in terms of states, acts, consequences, and preferences-we evaluated 20 studies that employed entropy. Our findings reveal that entropy is predominantly used to quantify uncertainty rather than directly guiding clinical actions. High-stakes fields such as oncology and radiology have led the way, using entropy to improve diagnostic accuracy and support risk assessment, while applications in neurology and hematology remain largely exploratory. Notably, no study has yet translated entropy into an operational, evidence-based decision-support framework. These results point to entropy's value as a quantitative tool in clinical reasoning, while also highlighting the need for prospective validation and the development of integrated clinical tools.