AI and deep learning are believed to change the scope of Medicine, as the introduction of smartphones changed our day-to-day life. Indeed, deep-learning algorithms show promising results as valuable diagnostic tools to assist clinicians in many respective specialties.

In Orthopaedic Trauma, data on application of AI technology is scarce but exciting: AI performs on a human level in recognising fractures on plain radiographs taken in the Emergency Department of patients with wrist-, hand-, and ankle-injuries with 83% accuracy. Moreover, AI has shown promising diagnostic performance characteristics not only to detect- (96% accuracy), but even to classify proximal humerus fractures (65–86%).

Associate professor Schwab’s Surgical Oncology Research Group (www.sorg-ai.com) from the Harvard Medical School affiliated Massachusetts General Hospital, is on the frontier of developing decision-making tools in Orthopaedics: in particular, on deploying their machine learning prediction models as online tools on clinically very relevant questions in the field of Orthopaedic Oncology and Spine. These valuable prediction tools created in their studies encourage us to broaden the AI scope to Orthopaedic Trauma and collaborate with the SORG team in order to aid patients and Orthopaedic Trauma surgeons in decision-making. 

Our mission is to use this platform to provide uniform clinical decision-making tools to guide both surgeon and patient to the best evidence-based treatment available: for a specific fracture in a unique patient. In conclusion, AI and Machine Learning allow us to move to the next level of evidence-based medicine: personalised patient care.