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

Ramkumar, Prem N.; Pang, Michael; Polisetty, Teja S.; Helm, J. Matthew; Karnuta, Jaret M.

doi:10.1016/j.arthro.2022.04.014

Cited by 18 publications

(8 citation statements)

References 39 publications

(44 reference statements)

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“…Over time, competence and confidence gradually improve, progressing along the "Slope of Enlightenment" until one reaches the "Plateau of Sustainability," indicating true mastery of a subject. The hype-fueled cycle described by Ramkumar et al 1 has turned out a growing number of ML-related studies replete with errors in basic fundamentals, raising concern that we may be collectively sitting at the peak of the Dunning-Kruger curve, unaware of the true complexities associated with ML and ultimately AI in general.…”

Section: See Related Article On Page 2761mentioning

confidence: 99%

“…However, as the gaps in knowledge become apparent, we will begin to fill them, producing more meaningful and clinically relevant research. As Ramkumar et al 1 point out, there is a strong trend to publish commentaries and reviews on ML in orthopaedics. Although the irony that this very editorial is a commentary on a University of Connecticut (I.J.W.).…”

Section: See Related Article On Page 2761mentioning

confidence: 99%

See 1 more Smart Citation

Editorial Commentary: Machine Learning in Orthopaedics: Venturing Into the Valley of Despair

Wellington¹,

Cote²

2022

Arthroscopy: The Journal of Arthroscopic & Related Surgery

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Section: See Related Article On Page 2761mentioning

confidence: 99%

Section: See Related Article On Page 2761mentioning

confidence: 99%

Editorial Commentary: Machine Learning in Orthopaedics: Venturing Into the Valley of Despair

Wellington¹,

Cote²

2022

Arthroscopy: The Journal of Arthroscopic & Related Surgery

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“…There is a well‐documented increase in machine learning studies within the orthopedic literature [26]. Given this recent increase in machine learning applications within the field, it is important for musculoskeletal health professionals to be familiar with the various models they may encounter in the literature and how best to translate these novel tools into their clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Supervised machine learning and associated algorithms: applications in orthopedic surgery

Pruneski

Pareek

Kunze

et al. 2022

Knee Surg Sports Traumatol Arthrosc

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Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on “big data” develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

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“…Recent studies have demonstrated the ability of complex algorithms to compliment, and even outperform physicians in some diagnostic tasks [11, 21]. The drastic increase in machine learning applications in orthopaedics has been well described, with almost 200 studies related to artificial intelligence in orthopaedics published between 2018 and 2021 [19].…”

Section: Introductionmentioning

confidence: 99%

The development and deployment of machine learning models

Pruneski

Williams

Nwachukwu

et al. 2022

Knee Surg Sports Traumatol Arthrosc

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Applications of artiicial intelligence, speciically 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 afect 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.

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

Cited by 18 publications

References 39 publications

Editorial Commentary: Machine Learning in Orthopaedics: Venturing Into the Valley of Despair

Editorial Commentary: Machine Learning in Orthopaedics: Venturing Into the Valley of Despair

Supervised machine learning and associated algorithms: applications in orthopedic surgery

The development and deployment of machine learning models

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