Literature DB >> 36083354

The development and deployment of machine learning models.

James A Pruneski1, Riley J Williams2, Benedict U Nwachukwu2, Prem N Ramkumar2, Ata M Kiapour1, R Kyle Martin3, Jón Karlsson4, Ayoosh Pareek5.   

Abstract

Applications of artificial intelligence, specifically machine learning, are becoming increasingly popular in Orthopaedic Surgery, and medicine as a whole. This growing interest is shared by data scientists and physicians alike. However, there is an asymmetry of understanding of the developmental process and potential applications of machine learning. As new technology will undoubtedly affect clinical practice in the coming years, it is important for physicians to understand how these processes work. The purpose of this paper is to provide clarity and a general framework for building and assessing machine learning models.
© 2022. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).

Entities:  

Year:  2022        PMID: 36083354     DOI: 10.1007/s00167-022-07155-4

Source DB:  PubMed          Journal:  Knee Surg Sports Traumatol Arthrosc        ISSN: 0942-2056            Impact factor:   4.114


  3 in total

1.  Finding the missing link for big biomedical data.

Authors:  Griffin M Weber; Kenneth D Mandl; Isaac S Kohane
Journal:  JAMA       Date:  2014-06-25       Impact factor: 56.272

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

Authors:  Prem N Ramkumar; Michael Pang; Teja Polisetty; J Matthew Helm; Jaret M Karnuta
Journal:  Arthroscopy       Date:  2022-05-10       Impact factor: 5.973

3.  Defining & assessing the quality, usability, and utilization of immunization data.

Authors:  Peter Bloland; Adam MacNeil
Journal:  BMC Public Health       Date:  2019-04-04       Impact factor: 3.295

  3 in total

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