Explainable Artificial Intelligence in Medical Robotics: A Review of Surgical Decision Support, Robotic Precision, and Clinical Trustworthiness

There is potential to enhance surgical decision support, robotic accuracy, and clinical dependability through the use of XAI into medical robots. It is difficult to comprehend suggestions, oversee robotic activities, and guarantee patient safety using traditional AI systems due to their lack of transparency. Trustworthy medical robotic architectures, precision-oriented robotic control, AI-enabled surgical intelligence, and XAI approaches are all included in this study. Surgical decision-making and prediction interpretation using methods like Gradient-weighted Class Activation Mapping (Grad-CAM), attention processes, Shapley Additive explanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME) are covered. In addition, the paper delves into topics such as AI-based safety monitoring, microsurgical aid, adaptive trajectory planning, real-time imaging, deep learning, and reinforcement learning. The areas of risk prediction, human-robot interaction, automated mistake detection, and training based on simulations receive special emphasis. Lifecycle governance, interpretability, fairness, cybersecurity, privacy protection, layered architectures, and runtime safety assurance are all ways to check if something is trustworthy. The review concludes by stressing the significance of human oversight, open communication, and clinical validation for responsible implementation. This review organises the field of explainable medical robotics and highlights important factors to keep in mind while designing trustworthy, understandable, and clinically acceptable robots.