Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 Feb;13(1):69-76.
doi: 10.1007/s12178-020-09600-8.

Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions

J Matthew Helm  1 , Andrew M Swiergosz  1 , Heather S Haeberle  2 , Jaret M Karnuta  1 , Jonathan L Schaffer  1 , Viktor E Krebs  1 , Andrew I Spitzer  3 , Prem N Ramkumar  4
Affiliations
Review

Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions

J Matthew Helm et al. Curr Rev Musculoskelet Med. 2020 Feb.

Abstract

Purpose of review: With the unprecedented advancement of data aggregation and deep learning algorithms, artificial intelligence (AI) and machine learning (ML) are poised to transform the practice of medicine. The field of orthopedics, in particular, is uniquely suited to harness the power of big data, and in doing so provide critical insight into elevating the many facets of care provided by orthopedic surgeons. The purpose of this review is to critically evaluate the recent and novel literature regarding ML in the field of orthopedics and to address its potential impact on the future of musculoskeletal care.

Recent findings: Recent literature demonstrates that the incorporation of ML into orthopedics has the potential to elevate patient care through alternative patient-specific payment models, rapidly analyze imaging modalities, and remotely monitor patients. Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, we report evidence that demonstrates these emerging applications of AI warrant ownership, leverage, and application by the orthopedic surgeon to better serve their patients and deliver optimal, value-based care.

Keywords: Artificial intelligence; Big data; Machine learning; Patient-specific payment models; Remote patient monitoring systems; Value-based care.

PubMed Disclaimer

Conflict of interest statement

J. Matthew Helm, Andrew M. Sweirgosz, Heather S. Haeberle, and Jaret M. Karnuta report no conflicts of interest.

Viktor E. Krebs reports royalties and consultancy fees from Stryker outside the submitted work.

Prem N. Ramkumar reports royalties and consultancy fees from Focus Ventures outside the submitted work.

Jonathan L. Schaffer reports royalties and consultancy fees from Zimmer Biomet, outside the submitted work.

Andrew I. Spitzer reports consultancy fees from Flexion Therapeutics Inc., Medical Device Business Services Inc., FIDIA Pharma USA Inc., and Sanofi-Aventis USA LLC, outside the submitted work.

Figures

Fig. 1
Fig. 1
Example of input, hidden, and output layers used to predict value-based metrics prior to elective primary total hip or knee arthroplasty from Ramkumar et al. [••]. Dr. Ramkumar retains the rights to this figure
See this image and copyright information in PMC

References

    1. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplast. 2018;33(8):2358–2361. doi: 10.1016/j.arth.2018.02.067. - DOI - PubMed
    1. Maxmen JS. The post-physician era : medicine in the twenty-first century. Hoboken: Wiley; 1976.
    1. Naylor CD. On the prospects for a (deep) learning health care system. JAMA. 2018;320(11):1099–1100. doi: 10.1001/jama.2018.11103. - DOI - PubMed
    1. Topol Eric J. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019;25(1):44–56. doi: 10.1038/s41591-018-0300-7. - DOI - PubMed
    1. Haeberle Heather S., Helm James M., Navarro Sergio M., Karnuta Jaret M., Schaffer Jonathan L., Callaghan John J., Mont Michael A., Kamath Atul F., Krebs Viktor E., Ramkumar Prem N. Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review. The Journal of Arthroplasty. 2019;34(10):2201–2203. doi: 10.1016/j.arth.2019.05.055. - DOI - PubMed