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The Integration of Artificial Intelligence into Clinical Practice
Open accessAbstract
The purpose of this literature review is to provide a fundamental synopsis of current research pertaining to artificial intelligence (AI) within the domain of clinical practice. Artificial intelligence has revolutionized the field of medicine and healthcare by providing innovative solutions to complex problems. One of the most important benefits of AI in clinical practice is its ability to investigate extensive volumes of data with efficiency and precision. This has led to the development of various applications that have improved patient outcomes and reduced the workload of healthcare professionals. AI can support doctors in making more accurate diagnoses and developing personalized treatment plans. Successful examples of AI applications are outlined for a series of medical specialties like cardiology, surgery, gastroenterology, pneumology, nephrology, urology, dermatology, orthopedics, neurology, gynecology, ophthalmology, pediatrics, hematology, and critically ill patients, as well as diagnostic methods. Special reference is made to legal and ethical considerations like accuracy, informed consent, privacy issues, data security, regulatory framework, product liability, explainability, and transparency. Finally, this review closes by critically appraising AI use in clinical practice and its future perspectives. However, it is also important to approach its development and implementation cautiously to ensure ethical considerations are met.
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“AI applications have proven effective in various clinical domains, such as reducing documentation burdens for clinicians, improving image interpretation in radiology, supporting intraoperative surgical guidance, and facilitating public health risk stratification2,3.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [3]
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“Few structured programs thoroughly address both the practical and ethical dimensions of AI in medicine3–10.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [3]
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2 retracted
Bodaghi, A., Fattahi, N., & Ramazani, A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13323 · Cited by 371
RetractedWu, L., Wei, D., Yang, N., Lei, H., & Wang, Y. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/4022312 · Cited by 9
RetractedSung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Siegel, R. L., Miller, K. D., & Jemal, A. (2018) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21442 · Cited by 16,632
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella‐Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1545-1 · Cited by 15,703
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827e83af · Cited by 9,694
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Widrow, B. C., & Lehr, M. A. (1990) · Proceedings of the IEEE · doi:10.1109/5.58323 · Cited by 2,270
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- Russell, S., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.].
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Metadata
- Authors
- Karalis, V. D.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- Corresponding author
- Karalis, Vangelis D.
- ORCID
- Karalis, Vangelis D.
- Venue
- Applied Biosciences
- Publisher
- MDPI AG
- Published
- 2024-01-01
- Volume
- 3
- Issue
- 1
- Pages
- 14-44
- ISSN
- 2813-0464 (ISSN-L)
- OpenAlex
- W4390509775
- Cited by
- 255
- References
- 188
- Citations by year
- 2026: 64 · 2025: 135 · 2024: 55
- FWCI
- 24.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Biosciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, clinical practice, diagnosis, personalized treatment, legal and ethical considerations, data privacy, data security, regulatory framework, explainability, transparency, informed consent, product liability
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