Literature DB >> 35550419

Meaningless Applications and Misguided Methodologies in Artificial Intelligence-Related Orthopaedic Research Propagates Hype Over Hope.

Prem N Ramkumar1, Michael Pang2, Teja Polisetty2, J Matthew Helm2, Jaret M Karnuta3.   

Abstract

There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular ("AI/machine learning"), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the "black box phenomenon" by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable.
Copyright © 2022 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

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Year:  2022        PMID: 35550419     DOI: 10.1016/j.arthro.2022.04.014

Source DB:  PubMed          Journal:  Arthroscopy        ISSN: 0749-8063            Impact factor:   5.973


  2 in total

Review 1.  Supervised machine learning and associated algorithms: applications in orthopedic surgery.

Authors:  James A Pruneski; Ayoosh Pareek; Kyle N Kunze; R Kyle Martin; Jón Karlsson; Jacob F Oeding; Ata M Kiapour; Benedict U Nwachukwu; Riley J Williams
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2022-10-12       Impact factor: 4.114

2.  The development and deployment of machine learning models.

Authors:  James A Pruneski; Riley J Williams; Benedict U Nwachukwu; Prem N Ramkumar; Ata M Kiapour; R Kyle Martin; Jón Karlsson; Ayoosh Pareek
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2022-09-09       Impact factor: 4.114

  2 in total

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