The review searched four databases for machine learning models predicting clinical outcomes after osteoporotic hip fracture, identifying 40 eligible studies published between 2010 and 2026. Death was the most frequently modelled outcome, with delirium, complications, resource use, rehabilitation and surgical failure examined less consistently. Models generally showed moderate to strong discrimination, but external validation was inconsistent, calibration was rarely reported and uncertainty estimates were incomplete.
Why it is interesting: Hip fracture mortality is among the most-studied prediction tasks in medicine, and this finds the literature still short of the checks that would make a model usable.