Operational framework and training standard requirements for AI‐empowered robotic surgery

Abstract

Background: For autonomous robot-delivered surgeries to ever become a feasible option, we recommend the combination of human-centered artificial intelligence (AI) and transparent machine learning (ML), with integrated Gross anatomy models. This can be supplemented with medical imaging data of cadavers for performance evaluation. Methods: We reviewed technological advances and state-of-the-art documented developments. We undertook a literature search on surgical robotics and skills, tracing agent studies, relevant frameworks, and standards for AI. This embraced transparency aspects of AI. Conclusion: We recommend "a procedure/skill template" for teaching AI that can be used by a surgeon. Similar existing methodologies show that when such a metricbased approach is used for training surgeons, cardiologists, and anesthetists, it results in a >40% error reduction in objectively assessed intraoperative procedures. The integration of Explainable AI and ML, and novel tissue characterization sensorics to tele

Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy

“…New communication channels between surgeons and robotics have been tried, for example, eye tracking and voice inputs. There are known frameworks for evaluating training AI [25]. Since testing robotic surgery systems in the real world is not feasible, testing frameworks are required to evaluate the surgical techniques for robotic surgeons.…”

Dentomaksi̇llofasi̇yal Radyoloji̇de Yapay Zeka Uygulamalarinin Rolü: Bölüm 1

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Radiomics and Machine Learning in Oral Healthcare

“…Consequently, automatic methods based on deep neural networks have been tested for several purposes, which are as follows: classification, image registration, segmentation, lesion detection, image retrieval, image guided therapy, image generation, and enhancement . Most recently, radiomics and AI research have been advancing in the dental field, revealing the potential of these technologies to substantially improve clinical care …”