Highlights
- •Highlight 1: AI and ML are becoming cornerstones in the healthcare-research and are integral in our continued capitalization of robust patient EMR data.
- •Highlight 2: Considerations for the use and application of ML in healthcare settings include transparency, interpretability, and ethics.
- •Highlight 3: The future of AI and ML in healthcare research is exciting and expansive.
Abstract
Artificial intelligence (AI) is a broad term referring to the application of computational
algorithms that can analyze large data sets to classify, predict, or gain useful conclusions.
Under the umbrella of AI is machine learning (ML). ML is the process of building or
learning statistical models using previously observed real world data to predict outcomes,
or categorize observations based on ‘training’ provided by humans. These predictions
are then applied to future data, all the while folding in the new data into its perpetually
improving and calibrated statistical model. The future of AI and ML in healthcare
research is exciting and expansive. AI and ML are becoming cornerstones in the medical
and healthcare-research domains and are integral in our continued processing and capitalization
of robust patient EMR data. Considerations for the use and application of ML in healthcare
settings include assessing the quality of data inputs and decision-making that serve
as the foundations of the ML model, ensuring the end-product is interpretable, transparent,
and ethical concerns are considered throughout the development process. The current
and future applications of ML include improving the quality and quantity of data collected
from EMRs to improve registry data, utilizing these robust datasets to improve and
standardized research protocols and outcomes, clinical decision-making applications,
natural language processing and improving the fundamentals of value-based care, to
name only a few.
Keywords
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References
- Long-Term trends in health care: the post-physician Era reconsidered BT - indicators and trends in health and health care.in: Schwefel D Springer Berlin Heidelberg, Berlin, Heidelberg1987: 109-115
- The post-physician Era: medicine in the 21st century.JAMA. 1977; 237 (10.1001/jama.1977.03270480076033): 2336-2337
- The inevitable application of big data to health care.JAMA. 2013; 309 (10.1001/jama.2013.393): 1351-1352
- How to read articles that use machine learning: Users’ guides to the medical literature.JAMA. 2019; 322 (10.1001/jama.2019.16489): 1806-1816
Callahan A, Shah NH. Chapter 19 - machine learning in healthcare. In: Sheikh A, Cresswell KM, Wright A, Bates dwbt-ka in ci, editors., Academic Press; 2017, p. 279–91. doi:https://doi.org/10.1016/B978-0-12-809523-2.00019-4.
- Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management.J Big Data. 2020; 7 (10.1186/s40537-020-00302-z): 26
- The impact of machine learning on patient care: a systematic review.Artif Intell Med. 2020; 103 (https://doi.org/10.1016/j.artmed.2019.101785)101785
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.J Am Med Informat Assoc. 2017; 24 (10.1093/jamia/ocw042): 198-208
Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. 2021.
- Deep learning for healthcare: review, opportunities and challenges.Brief Bioinform. 2018; 19 (10.1093/bib/bbx044): 1236-1246
- Systematic poisoning attacks on and defenses for machine learning in healthcare.IEEE J Biomed Heal Informat. 2015; 19 (10.1109/JBHI.2014.2344095): 1893-1905
- Interpretable machine learning in healthcare.Proc. 2018 ACM Int. Conf. bioinformatics, Comput. Biol. Heal. Informatics. Association for Computing Machinery, New York, NY, USA2018: 559-560 (10.1145/3233547.3233667)
- Automated machine learning: review of the state-of-the-art and opportunities for healthcare.Artif Intell Med. 2020; 104 (https://doi.org/10.1016/j.artmed.2020.101822)101822
- High-performance medicine: the convergence of human and artificial intelligence.Nat Med. 2019; (10.1038/s41591-018-0300-7)
- Do no harm: a roadmap for responsible machine learning for health care.Nat Med. 2019; 25 (10.1038/s41591-019-0548-6): 1337-1340
- A review of challenges and opportunities in machine learning for health.AMIA Jt Summits Transl Sci Proc AMIA Jt Summits Transl Sci. 2020; 2020: 191-200
- Practical guidance on artificial intelligence for health-care data.Lancet Digit Heal. 2019; 1 (10.1016/S2589-7500(19)30084-6): e157-e159
- Can AI help reduce disparities in general medical and mental health care?.AMA J Ethics. 2019; 21 (10.1001/amajethics.2019.167): E167-E179
- Ethical machine learning in healthcare.Annu Rev Biomed Data Sci. 2021; 4 (10.1146/annurev-biodatasci-092820-114757): 123-144
- Identifying ethical considerations for machine learning healthcare applications.Am J Bioeth. 2020; 20 (10.1080/15265161.2020.1819469): 7-17
- Algorithmic decision-making based on machine learning from big data: can transparency restore accountability?.Philos Technol. 2018; 31 (10.1007/s13347-017-0293-z): 525-541
- A systematic review of mixed methods research on human factors and ergonomics in health care.Appl Ergon. 2015; 51 (10.1016/j.apergo.2015.06.001): 291-321
- Can we open the black box of AI?.Nature. 2016; 538 (10.1038/538020a): 20-23
Abdollahi B, Nasraoui O. Transparency in fair machine learning: the case of explainable recommender systems BT - human and machine learning: Visible, explainable, trustworthy and transparent. In: Zhou J, Chen F, editors., Cham: Springer International Publishing; 2018, p. 21–35. doi:10.1007/978-3-319-90403-0_2.
- A machine learning-based diagnostic model associated with knee osteoarthritis severity.Sci Rep. 2020; 10 (10.1038/s41598-020-72941-4): 15743
- A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: data from the OsteoArthritis Initiative.Comput Med Imag Graph. 2019; 73 (https://doi.org/10.1016/j.compmedimag.2019.01.007): 11-18
- Machine learning algorithms predict functional improvement after hip arthroscopy for femoroacetabular impingement syndrome in athletes.JBJS. 2021; : 103
- Machine learning algorithms identify optimal sagittal component position in total knee arthroplasty.J Arthroplasty. 2021; 36 (https://doi.org/10.1016/j.arth.2021.02.063): S242-S249
- Digital twins, artificial intelligence, and machine learning technology to identify a real personalized motion axis of the tibiotalar joint for robotics in total ankle arthroplasty.Int Orthop. 2021; 45 (10.1007/s00264-021-05175-2): 2209-2217
- The application of machine learning to balance a total knee arthroplasty.Bone Jt Open. 2020; 1 (10.1302/2633-1462.16.BJO-2020-0056.R1): 236-244
- Machine learning–based identification of hip arthroplasty designs.J Orthop Transl. 2020; 21 (https://doi.org/10.1016/j.jot.2019.11.004): 13-17
- Prediction of post-operative implanted knee function using machine learning in clinical big data.2016 Int. Conf. Mach. Learn. Cybern. 2016; 1 (vol10.1109/ICMLC.2016.7860900): 195-200
- Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry: Presented at the 2019 AANS/CNS Section on Disorders of the Spine and Perip.J Neurosurg Spine SPI. 2019; 31 (10.3171/2019.3.SPINE181367): 568-578
- Can machine-learning algorithms predict early revision TKA in the Danish Knee arthroplasty registry?.Clin Orthop Relat Res. 2020; 478
- Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States.Cardiovasc Revascularizat Med. 2021; 22 (https://doi.org/10.1016/j.carrev.2020.06.017): 22-28
- Use of natural language processing algorithms to identify common data elements in operative notes for total hip arthroplasty.J Bone Joint Surg Am. 2019; 101 (10.2106/JBJS.19.00071): 1931-1938
- Use of natural language processing tools to identify and classify periprosthetic femur fractures.J Arthroplasty. 2019; 34 (https://doi.org/10.1016/j.arth.2019.07.025): 2216-2219
- Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: a case study of detecting total hip replacement dislocation.Comput Biol Med. 2021; 129 (https://doi.org/10.1016/j.compbiomed.2020.104140)104140
- Automated detection of periprosthetic joint infections and data elements using natural language processing.J Arthroplasty. 2021; 36 (https://doi.org/10.1016/j.arth.2020.07.076): 688-692
- Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients.Bone Joint J. 2020; (102-B10.1302/0301-620X.102B7.BJJ-2019-1574.R1): 99-104
- Artificial intelligence to identify arthroplasty implants from radiographs of the Hip.J Arthroplasty. 2021; 36 (e110.1016/j.arth.2020.11.015): S290-S294
- Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets.Med Image Anal. 2010; 14 (10.1016/j.media.2010.02.008): 265-275
- Who goes to inpatient rehabilitation or skilled nursing facilities unexpectedly following total knee arthroplasty?.J Arthroplasty. 2018; 33 (e110.1016/j.arth.2017.12.015): 1348-1351
- Risk adjustment is necessary in medicare bundled payment models for total hip and knee arthroplasty.J Arthroplasty. 2018; 33 (10.1016/j.arth.2018.02.095): 2368-2375
- Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model.J Arthroplasty. 2018; 33 (10.1016/j.arth.2018.08.028): 3617-3623
- Risk adjustment for medicare total knee arthroplasty bundled payments.Orthopedics. 2016; 39 (10.3928/01477447-20160623-04): e911-e916
- The ethics of bundled payments in total joint replacement: “Cherry Picking” and “Lemon Dropping.J Clin Ethics. 2018; 29: 62-68
Article info
Publication history
Published online: January 31, 2022
Accepted:
January 29,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
✰All authors have made substantial contributions to all of the following: (1) the conception and design of the paper, (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the version to be submitted. The manuscript, including related data, figures and tables has not been previously published and is not under consideration elsewhere.
✰✰None of the authors have any conflict of interests to declare.
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