Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?
Abstract
This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI has shown immense promise in transforming healthcare through the application of advanced technologies like machine learning and deep learning.
“Artificial intelligence (AI) has shown immense promise in transforming healthcare through the application of advanced technologies like machine learning (ML) and deep learning (DL) [1], [2].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [1]
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Deep Learning for Identifying Metastatic Breast CancerPreprint
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016) · arXiv.org · doi:10.48550/arxiv.1606.05718 · Cited by 1,028 · see citing works
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00248 · Cited by 610 · see citing works
Cruz-Roa, Á., Gilmore, H. L., Basavanhally, A. N., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2017) · Scientific Reports · doi:10.1038/srep46450 · Cited by 530 · see citing works
Hopfield, J. J. (1988) · IEEE Circuits and Devices Magazine · doi:10.1109/101.8118 · Cited by 505 · see citing works
Ahmed, M. N., Toor, A. S., O’Neil, K., et al. (2017) · IEEE Pulse · doi:10.1109/mpul.2017.2678098 · Cited by 108 · see citing works
The law of accelerating returns
Buchanan, M. (2008) · Nature Physics · doi:10.1038/nphys1010 · Cited by 87 · see citing works
Somashekhar, S. P., Sepulveda, M., Norden, A. D., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.35.15_suppl.8527 · Cited by 51 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178 · see citing works
Chang, C., Hwang, G., & Gau, M. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13158 · Cited by 294 · see citing works
Can nurses remain relevant in a technologically advanced future?
Pepito, J. A., & Locsin, R. (2019) · International Journal of Nursing Sciences · doi:10.1016/j.ijnss.2018.09.013 · Cited by 221 · see citing works
Shao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158 · see citing works
Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance
Guan, J. (2019) · Chinese Medical Sciences Journal · doi:10.24920/003611 · Cited by 170 · see citing works
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works
Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120 · see citing works
Artificial intelligence and clinical anatomical education: Promises and perils
Lazarus, M. D., Truong, M., Douglas, P., & Selwyn, N. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2221 · Cited by 116 · see citing works
Taskiran, N. (2023) · Nurse Educator · doi:10.1097/nne.0000000000001446 · Cited by 75 · see citing works
Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64 · see citing works
Vogan, A. A., Alnajjar, F., Gochoo, M., & Khalid, S. (2020) · IEEE Access · doi:10.1109/access.2020.2966819 · Cited by 63 · see citing works
Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption
Morrison, K. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2020-0258 · Cited by 43 · see citing works
Artificial intelligence-driven serious games in health care: scoping review
Abd-alrazaq, A., Abuelezz, I., Hassan, A., AlSammarraie, A., Alhuwail, D., Irshaidat, S., Abu Serhan, H., Ahmed, A., Alabed Alrazak, S., & Househ, M. (2022) · JMIR Serious Games · doi:10.2196/39840 · Cited by 39 · see citing works
Aksoy, S., Demircioglu, P., & Bogrekci, I. (2024) · Dermato · doi:10.3390/dermato4040015 · Cited by 20 · see citing works
Jaigirdar, F. T., Rudolph, C., Oliver, G., Watts, D., & Bain, C. (2020) · 2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic50333.2020.00030 · Cited by 18 · see citing works
Artificial intelligence in writing and research: ethical implications and best practices
AlSamhori, A. F., & Alnaimat, F. (2024) · Central Asian Journal of Medical Hypotheses and Ethics · doi:10.47316/cajmhe.2024.5.4.02 · Cited by 15 · see citing works
- Authors
- Bini, S. A.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Bini, Stefano A.
- ORCID
- Bini, Stefano A.
- Venue
- The Journal of Arthroplasty
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 33
- Issue
- 8
- Pages
- 2358-2361
- ISSN
- 0883-5403 (ISSN-L)
- OpenAlex
- W2797763839
- Cited by
- 670
- References
- 10
- Citations by year
- 2026: 47 · 2025: 105 · 2024: 113 · 2023: 114 · 2022: 107 · 2021: 97 · 2020: 51 · 2019: 32 · 2018: 3
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Arthroplasty (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, machine learning, deep learning, artificial neural networks, cognitive computing, biomimetics, clinical decision support, hip and knee arthroplasty, health technology
- MeSH terms
- Machine Learning, Deep Learning, Algorithms, Artificial Intelligence, Humans, Orthopedics, Physicians, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
