Literature DB >> 31767194

The digital surgeon: How big data, automation, and artificial intelligence will change surgical practice.

James Wall1, Thomas Krummel2.   

Abstract

Exponential growth in computing power, data storage, and sensing technology has led to a world in which we can both capture and analyze incredible amounts of data. The evolution of machine learning has further advanced the ability of computers to develop insights from massive data sets that are beyond the capacity of human analysis. The convergence of computational power, data storage, connectivity, and Artificial Intelligence (AI) has led to health technologies that, to date, have focused on diagnostic areas such as radiology and pathology. The question remains how the digital revolution will translate in the realm of surgery. There are three main areas where the authors believe that AI could impact surgery in the near future: enhancement of training modalities, cognitive enhancement of the surgeon, and procedural automation. While the promise of Big Data, AI, and Automation is high, there have been unanticipated missteps in the use of such technologies that are worth considering as we evaluate how such technologies could/should be adopted in surgical practice. Surgeons must be prepared to adopt smarter training modalities, supervise the learning of machines that can enhance cognitive function, and ultimately oversee autonomous surgery without allowing for a decay in the surgeon's operating skills.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Automation and artificial intelligence in pediatric surgery; Future pediatric surgery

Mesh:

Year:  2019        PMID: 31767194     DOI: 10.1016/j.jpedsurg.2019.09.008

Source DB:  PubMed          Journal:  J Pediatr Surg        ISSN: 0022-3468            Impact factor:   2.545


  3 in total

Review 1.  Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review.

Authors:  Zubair Ahmad; Shabina Rahim; Maha Zubair; Jamshid Abdul-Ghafar
Journal:  Diagn Pathol       Date:  2021-03-17       Impact factor: 2.644

2.  Robotics and AI for Teleoperation, Tele-Assessment, and Tele-Training for Surgery in the Era of COVID-19: Existing Challenges, and Future Vision.

Authors:  Navid Feizi; Mahdi Tavakoli; Rajni V Patel; S Farokh Atashzar
Journal:  Front Robot AI       Date:  2021-04-14

Review 3.  Using Machine Learning for Pharmacovigilance: A Systematic Review.

Authors:  Patrick Pilipiec; Marcus Liwicki; András Bota
Journal:  Pharmaceutics       Date:  2022-01-23       Impact factor: 6.321

  3 in total

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