Artificial intelligence to revolutionize surgical decision-making is still just around the corner

Medical artificial intelligence, especially large language models, has engendered both excitement and unease across the medical community, promising improved surgical diagnostic accuracy, perioperative decision-making, and patient safety amidst concerns of considerable bias and its implications on the future of human expertise. The evolution of artificial intelligence clinical decision support originated in the surgical field through early systems like AAPHelp, which leveraged Bayesian reasoning to diagnose acute abdominal pain. Despite the initial promise of artificial intelligence clinical decision support, attempts to develop more robust diagnostic tools largely fell short in the 1980s, with instruments unable to adequately diagnose and manage complex presentations, a lack of transparency in their decision-making processes, and limitations in scope. It was not until the 2010s that clinical decision support began to show renewed potential as a meaningful adjunct in surgical practice, largely driven by advancements in machine learning techniques and wider access to large clinical data sets. New artificial intelligence models, particularly those utilizing large language models, now demonstrate impressive capabilities, from predicting the risk of postoperative complications for individual patients to streamlining clinical documentation and beyond. However, challenges persist regarding transparency, bias, reliability, and integration into clinical workflows. Despite these hurdles, artificial intelligence-based tools like large language models represent an exciting new chapter in surgical decision-making. Built on a long history of clinical decision support systems in surgery, these technologies hold great promise to meaningfully augment surgical practice and improve care for patients.