Year: 2019
Type: review
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 metric-based 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-operated robotic-assisted procedures with medical imaged cadavers, provides robotic guidance and refines tissue classifications at a molecular level.
DOI: 10.1002/rcs.2020
Institutions: Universidade de São Paulo, Johns Hopkins University, Medical University of Graz, University of Pittsburgh, Ludwig-Maximilians-Universität München, Université d'Artois, Leiden University Medical Center, Wirral University Teaching Hospital NHS Foundation Trust, Otto-von-Guericke-Universität Magdeburg, Imperial College London, University of Würzburg, University Medical Center Hamburg-Eppendorf, University of Ulster, ORSI Academy, Ulster University, Derry-Londonderry Campus
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2947546589