Ismail Dergaa, Helmi Ben Saad, Abdelfatteh El Omri, Jordan M Glenn, Cain C T Clark, Jad Adrian Washif, Noomen Guelmami, Omar Hammouda, Ramzi A Al-Horani, Luis Felipe Reynoso-Sánchez, Mohamed Romdhani, Laisa Liane Paineiras-Domingos, Rodrigo L Vancini, Morteza Taheri, Leonardo Jose Mataruna-Dos-Santos, Khaled Trabelsi, Hamdi Chtourou, Makram Zghibi, Özgür Eken, Sarya Swed, Mohamed Ben Aissa, Hossam H Shawki, Hesham R El-Seedi, Iñigo Mujika, Stephen Seiler, Piotr Zmijewski, David B Pyne, Beat Knechtle, Irfan M Asif, Jonathan A Drezner, Øyvind Sandbakk, Karim Chamari
The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals...
March 2024: Biology of Sport