Reducing medical errors from health care AI: lessons from Claude Shannon and Max Planck on precision in medicine

In 1948, Claude Shannon revolutionized the world of communication with his theory of information, showing that precision and efficiency could emerge from chaos. Roughly 40 years earlier, Max Planck had done something similar in physics by discovering the rules of quantum mechanics, reducing uncertainty in an unpredictable universe. These two minds, though working in entirely different fields, shared a common vision: to bring order out of entropy. Today, their legacies hold surprising relevance in one of the most advanced frontiers of modern medicine, artificial intelligence (AI) in health care.

AI has become an essential tool in diagnosing diseases, predicting patient outcomes, and guiding complex treatments. Yet, despite the promise of precision, AI systems in health care remain susceptible to a dangerous form of entropy—a creeping disorder that can lead to systemic errors, missed diagnoses, and faulty recommendations. As more hospitals and medical facilities rely on these technologies, the stakes are as high as ever. The dream of reducing medical error through AI has, in some cases, transformed into a new breed of error, one rooted in the uncertainty of the machine’s algorithms.

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