Operational framework and training standard requirements for AI-empowered robotic surgery
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Operational framework and training standard requirements for AI-empowered robotic surgery. / O'Sullivan, Shane; Leonard, Simon; Holzinger, Andreas; Allen, Colin; Battaglia, Fiorella; Nevejans, Nathalie; van Leeuwen, Fijs W. B.; Sajid, Mohammed Imran; Friebe, Michael; Ashrafian, Hutan; Heinsen, Helmut; Wichmann, Dominic; Hartnett, Margaret; Gallagher, Anthony G.
In: The international journal of medical robotics + computer assisted surgery : MRCAS, Vol. 16, No. 5, 10.2020, p. 1-13.Research output: SCORING: Contribution to journal › SCORING: Review article › Research
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TY - JOUR
T1 - Operational framework and training standard requirements for AI-empowered robotic surgery
AU - O'Sullivan, Shane
AU - Leonard, Simon
AU - Holzinger, Andreas
AU - Allen, Colin
AU - Battaglia, Fiorella
AU - Nevejans, Nathalie
AU - van Leeuwen, Fijs W. B.
AU - Sajid, Mohammed Imran
AU - Friebe, Michael
AU - Ashrafian, Hutan
AU - Heinsen, Helmut
AU - Wichmann, Dominic
AU - Hartnett, Margaret
AU - Gallagher, Anthony G.
N1 - © 2020 John Wiley & Sons, Ltd.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - surgical skills
KW - dexterity
KW - autonomous robotic surgery
KW - supervised autonomy
KW - explainable artificial intelligence xai
KW - surgical navigation
U2 - 10.1002/rcs.2020
DO - 10.1002/rcs.2020
M3 - SCORING: Review article
C2 - 31144777
VL - 16
SP - 1
EP - 13
JO - INT J MED ROBOT COMP
JF - INT J MED ROBOT COMP
SN - 1478-5951
IS - 5
ER -
