Literature DB >> 34815097

Executive summary of the artificial intelligence in surgery series.

Tyler J Loftus1, Alexander P J Vlaar2, Andrew J Hung3, Azra Bihorac4, Bradley M Dennis5, Catherine Juillard6, Daniel A Hashimoto7, Haytham M A Kaafarani8, Patrick J Tighe9, Paul C Kuo10, Shuhei Miyashita11, Steven D Wexner12, Kevin E Behrns12.   

Abstract

As opportunities for artificial intelligence to augment surgical care expand, the accompanying surge in published literature has generated both substantial enthusiasm and grave concern regarding the safety and efficacy of artificial intelligence in surgery. For surgeons and surgical data scientists, it is increasingly important to understand the state-of-the-art, recognize knowledge and technology gaps, and critically evaluate the deluge of literature accordingly. This article summarizes the experiences and perspectives of a global, multi-disciplinary group of experts who have faced development and implementation challenges, overcome them, and produced incipient evidence thereof. Collectively, evidence suggests that artificial intelligence has the potential to augment surgeons via decision-support, technical skill assessment, and the semi-autonomous performance of tasks ranging from resource allocation to patching foregut defects. Most applications remain in preclinical phases. As technologies and their implementations improve and positive evidence accumulates, surgeons will face professional imperatives to lead the safe, effective clinical implementation of artificial intelligence in surgery. Substantial challenges remain; recent progress in using artificial intelligence to achieve performance advantages in surgery suggests that remaining challenges can and will be overcome.
Copyright © 2021 Elsevier Inc. All rights reserved.

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Year:  2021        PMID: 34815097      PMCID: PMC9379376          DOI: 10.1016/j.surg.2021.10.047

Source DB:  PubMed          Journal:  Surgery        ISSN: 0039-6060            Impact factor:   4.348


  28 in total

1.  Surgical skill and complication rates after bariatric surgery.

Authors:  John D Birkmeyer; Jonathan F Finks; Amanda O'Reilly; Mary Oerline; Arthur M Carlin; Andre R Nunn; Justin Dimick; Mousumi Banerjee; Nancy J O Birkmeyer
Journal:  N Engl J Med       Date:  2013-10-10       Impact factor: 91.245

2.  Burnout and medical errors among American surgeons.

Authors:  Tait D Shanafelt; Charles M Balch; Gerald Bechamps; Tom Russell; Lotte Dyrbye; Daniel Satele; Paul Collicott; Paul J Novotny; Jeff Sloan; Julie Freischlag
Journal:  Ann Surg       Date:  2010-06       Impact factor: 12.969

3.  Milestones for autonomous in vivo microrobots in medical applications.

Authors:  Bo Sun; Georgina Wood; Shuhei Miyashita
Journal:  Surgery       Date:  2020-12-11       Impact factor: 3.982

4.  Leveraging interpretable machine learning algorithms to predict postoperative patient outcomes on mobile devices.

Authors:  Majed W El Hechi; Samer A Nour Eddine; Lydia R Maurer; Haytham M A Kaafarani
Journal:  Surgery       Date:  2020-09-09       Impact factor: 3.982

5.  The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care.

Authors:  Matthieu Komorowski; Leo A Celi; Omar Badawi; Anthony C Gordon; A Aldo Faisal
Journal:  Nat Med       Date:  2018-10-22       Impact factor: 53.440

6.  Intelligent, Autonomous Machines in Surgery.

Authors:  Tyler J Loftus; Amanda C Filiberto; Jeremy Balch; Alexander L Ayzengart; Patrick J Tighe; Parisa Rashidi; Azra Bihorac; Gilbert R Upchurch
Journal:  J Surg Res       Date:  2020-04-24       Impact factor: 2.192

7.  Machine learning analyses of automated performance metrics during granular sub-stitch phases predict surgeon experience.

Authors:  Andrew B Chen; Siqi Liang; Jessica H Nguyen; Yan Liu; Andrew J Hung
Journal:  Surgery       Date:  2020-11-05       Impact factor: 3.982

8.  Computer Vision in the Operating Room: Opportunities and Caveats.

Authors:  Lauren R Kennedy-Metz; Pietro Mascagni; Antonio Torralba; Roger D Dias; Pietro Perona; Julie A Shah; Nicolas Padoy; Marco A Zenati
Journal:  IEEE Trans Med Robot Bionics       Date:  2020-11-24

9.  Validation of the AI-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

Authors:  Lydia R Maurer; Prahan Chetlur; Daisy Zhuo; Majed El Hechi; George C Velmahos; Jack Dunn; Dimitris Bertsimas; Haytham M A Kaafarani
Journal:  Ann Surg       Date:  2020-12-23       Impact factor: 13.787

10.  Association Between Surgeon Technical Skills and Patient Outcomes.

Authors:  Jonah J Stulberg; Reiping Huang; Lindsey Kreutzer; Kristen Ban; Bradley J Champagne; Scott R Steele; Julie K Johnson; Jane L Holl; Caprice C Greenberg; Karl Y Bilimoria
Journal:  JAMA Surg       Date:  2020-10-01       Impact factor: 14.766

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  2 in total

1.  Diabetes management in spinal surgery.

Authors:  Michelot Michel; Brandon Lucke-Wold
Journal:  J Clin Images Med Case Rep       Date:  2022-06-22

2.  Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult.

Authors:  Krzysztof Laudanski
Journal:  J Clin Med       Date:  2022-02-21       Impact factor: 4.241

  2 in total

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