Literature DB >> 31748800

Promises and Perils of Artificial Intelligence in Neurosurgery.

Sandip S Panesar1, Michel Kliot2, Rob Parrish1, Juan Fernandez-Miranda2, Yvonne Cagle3, Gavin W Britz1.   

Abstract

Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing "automation revolutions," namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective.
Copyright © 2019 by the Congress of Neurological Surgeons.

Entities:  

Keywords:  Artificial intelligence; Automation; Deep learning; Diagnostics; Machine learning; Prognostication; Surgical adjuncts

Mesh:

Year:  2020        PMID: 31748800     DOI: 10.1093/neuros/nyz471

Source DB:  PubMed          Journal:  Neurosurgery        ISSN: 0148-396X            Impact factor:   4.654


  7 in total

Review 1.  A Brief Insight on Magnetic Resonance Conditional Neurosurgery Robots.

Authors:  Z I Bibi Farouk; Shan Jiang; Zhiyong Yang; Abubakar Umar
Journal:  Ann Biomed Eng       Date:  2022-01-06       Impact factor: 3.934

2.  Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.

Authors:  Quinlan D Buchlak; Nazanin Esmaili; Christine Bennett; Farrokh Farrokhi
Journal:  Acta Neurochir Suppl       Date:  2022

Review 3.  Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm.

Authors:  Simon Williams; Hugo Layard Horsfall; Jonathan P Funnell; John G Hanrahan; Danyal Z Khan; William Muirhead; Danail Stoyanov; Hani J Marcus
Journal:  Cancers (Basel)       Date:  2021-10-07       Impact factor: 6.639

4.  Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium.

Authors:  Tolga Turan Dundar; Ismail Yurtsever; Meltem Kurt Pehlivanoglu; Ugur Yildiz; Aysegul Eker; Mehmet Ali Demir; Ahmet Serdar Mutluer; Recep Tektaş; Mevlude Sila Kazan; Serkan Kitis; Abdulkerim Gokoglu; Ihsan Dogan; Nevcihan Duru
Journal:  Front Surg       Date:  2022-04-29

Review 5.  Robotics in neurosurgery: Current prevalence and future directions.

Authors:  Rohin Singh; Kendra Wang; Muhammad Bilal Qureshi; India C Rangel; Nolan J Brown; Shane Shahrestani; Oren N Gottfried; Naresh P Patel; Mohamad Bydon
Journal:  Surg Neurol Int       Date:  2022-08-19

6.  DeepNavNet: Automated Landmark Localization for Neuronavigation.

Authors:  Christine A Edwards; Abhinav Goyal; Aaron E Rusheen; Abbas Z Kouzani; Kendall H Lee
Journal:  Front Neurosci       Date:  2021-06-17       Impact factor: 4.677

7.  Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage.

Authors:  Wei Chen; Xiangkui Li; Lu Ma; Dong Li
Journal:  Front Neurol       Date:  2022-01-03       Impact factor: 4.003

  7 in total

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