Artificial intelligence (AI) tools are increasingly being developed to assist in healthcare, and one recent application is in predicting a patient’s risk of dying after a hip fracture, a serious health issue affecting many Americans each year. Researchers from the University of Pennsylvania have employed machine learning algorithms to analyze a decade’s worth of data from 3,751 hip fracture patients. The models generated by these algorithms can provide a “mortality risk score” by considering various factors, such as age and blood glucose levels, to predict the risk of death at 1, 5, and 10 years after a hip fracture.
The significance of this AI-powered tool lies in its potential to identify high-risk patients, enabling healthcare professionals to offer more intensive follow-up care or counseling to patients and their families. Hip fractures are a significant public health concern, with a substantial percentage of patients dying within a year, and a substantial portion of survivors losing their independence.
While AI tools like these can be valuable in healthcare, experts emphasize the importance of preventive approaches such as diet and exercise to address the broader issue of hip fractures and related health problems. AI can complement traditional healthcare practices and provide valuable insights and support, but it should not replace fundamental lifestyle changes that contribute to overall health and well-being.