Sebastian Bodenstedt1,2, Martin Wagner3, Beat Peter Müller-Stich3, Jürgen Weitz4,2, Stefanie Speidel1,2. 1. Division of Translational Surgical Oncology, National Center for Tumor Diseases Dresden, Dresden, Germany. 2. Centre for Tactile Internet with Human-in-the-Loop (CeTI), TU Dresden, Dresden, Germany. 3. Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany. 4. Department for Visceral, Thoracic and Vascular Surgery, University Hospital Carl-Gustav-Carus, TU Dresden, Dresden, Germany.
Abstract
BACKGROUND: Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field. SUMMARY: This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery. KEY MESSAGES: AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.
BACKGROUND: Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field. SUMMARY: This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery. KEY MESSAGES: AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.
Authors: Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun Journal: Nature Date: 2017-01-25 Impact factor: 49.962
Authors: Marije Wijnberge; Bart F Geerts; Liselotte Hol; Nikki Lemmers; Marijn P Mulder; Patrick Berge; Jimmy Schenk; Lotte E Terwindt; Markus W Hollmann; Alexander P Vlaar; Denise P Veelo Journal: JAMA Date: 2020-03-17 Impact factor: 56.272
Authors: Stephanie L Hyland; Martin Faltys; Matthias Hüser; Xinrui Lyu; Thomas Gumbsch; Cristóbal Esteban; Christian Bock; Max Horn; Michael Moor; Bastian Rieck; Marc Zimmermann; Dean Bodenham; Karsten Borgwardt; Gunnar Rätsch; Tobias M Merz Journal: Nat Med Date: 2020-03-09 Impact factor: 53.440
Authors: Ryan Poplin; Avinash V Varadarajan; Katy Blumer; Yun Liu; Michael V McConnell; Greg S Corrado; Lily Peng; Dale R Webster Journal: Nat Biomed Eng Date: 2018-02-19 Impact factor: 25.671
Authors: Lorenzo Moja; Hernan Polo Friz; Matteo Capobussi; Koren Kwag; Rita Banzi; Francesca Ruggiero; Marien González-Lorenzo; Elisa G Liberati; Massimo Mangia; Peter Nyberg; Ilkka Kunnamo; Claudio Cimminiello; Giuseppe Vighi; Jeremy M Grimshaw; Giovanni Delgrossi; Stefanos Bonovas Journal: JAMA Netw Open Date: 2019-12-02