Federated AI for Surgical Robotics: Enhancing Precision, Privacy, and Real-Time Decision-Making in Smart Healthcare

Modern healthcare now uses surgical robotics to help perform surgeries that cause little tissue damage and are very accurate. Still, connecting federated artificial intelligence (AI) to surgical robotics may lead to better precision, preserve patient privacy and allow real-time decisions in healthcare settings. The paper explores if federated learning (FL) can enhance surgical robotic systems by allowing hospitals and healthcare institutions to safely and cooperatively improve their models. This means private details of patients aren’t ever transferred to one area, so both privacy and teamwork can be maintained. We analyse the designs of AI systems for surgery using robots which involve selecting models, training them and optimizing the parameters, as they affect procedural accuracy and decision-making skills. Also, we cover how FL must perform in real-time systems, where being responsive and reliable is critical. The discussion at the end of the paper covers what federated AI could mean for surgical robotics, privacyfocused healthcare and precision medicine.