Review

. 2020 Oct 26;5(10):593-603.

doi: 10.1302/2058-5241.5.190092. eCollection 2020 Oct.

Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle

Jacobien H F Oosterhoff^{1

2}, Job N Doornberg^{1

3}; Machine Learning Consortium

Collaborators, Affiliations

Collaborators

Machine Learning Consortium:
Paul Algra, Michel van den Bekerom, Mohit Bhandari, Michiel Bongers, Charles Court-Brown, Anne-Eva Bulstra, Geert Buijze, Sofia Bzovsky, Neil Chen, Job Doornberg, Andrew Duckworth, J. Carel Goslings, Benjamin Gravesteijn, Olivier Groot, Gordon Guyatt, Laurent Hendrickx, Dirk-Jan Hofstee, Frank IJpma, Ruurd Jaarsma, Stein Janssen, Paul Jutte, Aditya Karhade, Lucien Keijser, Gino Kerkhoffs, David Langerhuizen, Jonathan Lans, Wouter Mallee, Matthew Moran, Margaret McQueen, Marjolein Mulders, Miryam Obdeijn, Tarandeep Oberai, Jacobien H.F. Oosterhoff, Rudolf Poolman, David Ring, Paul Tornetta III, Joseph Schwab, Emil H. Schemitsch, Niels Schep, Inger Schipper, Bram Schoolmeesters, Marc Swiontkowski, David Sanders, Sheila Sprague, Ewout Steyerberg, Stephen D. Walter, Monique Walenkamp

Affiliations

¹ Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, the Netherlands.
² Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
³ Department of Orthopaedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia.

PMID: 33204501
PMCID: PMC7608572
DOI: 10.1302/2058-5241.5.190092

Review

Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle

Jacobien H F Oosterhoff et al. EFORT Open Rev. 2020.

. 2020 Oct 26;5(10):593-603.

doi: 10.1302/2058-5241.5.190092. eCollection 2020 Oct.

Authors

Jacobien H F Oosterhoff^{1

2}, Job N Doornberg^{1

3}; Machine Learning Consortium

Collaborators

Machine Learning Consortium:
Paul Algra, Michel van den Bekerom, Mohit Bhandari, Michiel Bongers, Charles Court-Brown, Anne-Eva Bulstra, Geert Buijze, Sofia Bzovsky, Neil Chen, Job Doornberg, Andrew Duckworth, J. Carel Goslings, Benjamin Gravesteijn, Olivier Groot, Gordon Guyatt, Laurent Hendrickx, Dirk-Jan Hofstee, Frank IJpma, Ruurd Jaarsma, Stein Janssen, Paul Jutte, Aditya Karhade, Lucien Keijser, Gino Kerkhoffs, David Langerhuizen, Jonathan Lans, Wouter Mallee, Matthew Moran, Margaret McQueen, Marjolein Mulders, Miryam Obdeijn, Tarandeep Oberai, Jacobien H.F. Oosterhoff, Rudolf Poolman, David Ring, Paul Tornetta III, Joseph Schwab, Emil H. Schemitsch, Niels Schep, Inger Schipper, Bram Schoolmeesters, Marc Swiontkowski, David Sanders, Sheila Sprague, Ewout Steyerberg, Stephen D. Walter, Monique Walenkamp

Affiliations

¹ Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, the Netherlands.
² Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
³ Department of Orthopaedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia.

PMID: 33204501
PMCID: PMC7608572
DOI: 10.1302/2058-5241.5.190092

Abstract

Artificial Intelligence (AI) in general, and Machine Learning (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery.The greatest benefit of ML is in its ability to learn from real-world clinical use and experience, and thereby its capability to improve its own performance.Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients' care and doctors' workflows.The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment.Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks.In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. 'If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine'.⁷⁶ Cite this article: EFORT Open Rev 2020;5:593-603. DOI: 10.1302/2058-5241.5.190092.

Keywords: artificial intelligence; computer vision; data-driven medicine; machine learning; orthopaedic surgery; orthopaedic trauma; personalized medicine; prediction tools.

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Conflict of interest statement

ICMJE Conflict of interest statement: JD reports receipt of a grant from Marti-Keuning Eckhardt Foundation for the submitted work. The other authors declare no conflict of interest relevant to this work.

Figures

**Fig. 1**
Artificial Intelligence Hype Cycle, Machine learning, Natural Language Processing and Computer Vision on its way down – Adapted from Gartner Hype Cycle for Artificial Intelligence, 2019 gartner.com/smarterwithgartner.

**Fig. 2**
AI is very likely to assist orthopaedic surgeons: ‘If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine’ (Courtesy: Marcello Lavallen).

**Fig. 3**
Workflow for patients clinically suspected for a distal radius fracture. *Note*. ED, emergency department.

**Fig. 4**
Flowsheet from clinical problem to implementation.

**Fig. 5**
Classification algorithms. (Courtesy: B.Y. Gravesteijn)

See this image and copyright information in PMC

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Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle

Collaborators

Affiliations

Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle

Authors

Collaborators

Affiliations

Abstract

Conflict of interest statement

Figures

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References

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