Personalized Surgical Planning with AI: A Machine Learning Framework

Abstract

Personalized surgical planning has been accomplished through the manual interpretation of medical imaging and the knowledge of the surgeon. This process can be time-consuming and is prone to experiencing fluctuation. A framework for machine learning that was developed with the intention of improving surgical decision-making in terms of precision, efficiency, and patient-specificity.  The approach that has been developed incorporates multimodal medical data, such as CT scans, MRIs, and electronic health records, in order to construct predictive models for surgical risk assessment, anatomical segmentation, and intraoperative guiding.  Through the evaluation of important performance parameters like precision, recall, Dice coefficient, and area under the receiver operating curve (AUC-ROC), advanced benchmarking approaches verify the trustworthiness of models.  Approaches that are driven by artificial intelligence offer solutions that are both data-driven and adaptive, thereby reducing the number of errors that are caused by humans while simultaneously improving surgical workflows.  It is possible to perform real-time predictive analytics with this framework, which also enables dynamic adjustments to be made throughout procedures.  It is anticipated that future developments, such as the incorporation of augmented reality and robotic-assisted surgery, would further refine tailored treatment options.  The use of such intelligent systems has the potential to greatly enhance patient outcomes by lowering the risk of surgical complications, improving the distribution of resources, and shortening the amount of time needed for recovery.  The transformative potential of machine learning in contemporary healthcare, paving the way for surgical planning that is safer, more efficient, and highly tailored.