The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
How the FDA Regulates AI
Abstract
Recent years have seen digital technologies increasingly leveraged to multiply conventional imaging modalities' diagnostic power. Artificial intelligence (AI) is most prominent among these in the radiology space, touted as the "stethoscope of the 21st century" for its potential to revolutionize diagnostic precision, provider workflow, and healthcare expenditure. Partially owing to AI's unique characteristics, and partially due to its novelty, existing regulatory paradigms are not well suited to balancing patient safety with furthering the growth of this new sector. The current review examines the historic, current, and proposed regulatory treatment of AI-empowered medical devices by the US Food and Drug Administration (FDA). An innovative framework proposed by the FDA seeks to address these issues by looking to current good manufacturing practices (cGMP) and adopting a total product lifecycle (TPLC) approach. If brought into force, this may reduce the regulatory burden incumbent on developers, while holding them to rigorous quality standards, maximizing safety, and permitting the field to mature.
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“The vast majority of AI devices thus far, including those in oncology, fall under Class II, for which the FDA 510(k) pathway applies and requires the device to be”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [187]
In the citing paper
“The FDA regulates AI applications under the umbrella of Software as a Medical Device (SaMD) with similar regulatory processes as other SaMD devices and non-AI algorithms (187).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [187]
Citations
Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Artificial intelligence in radiology
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Big data and black-box medical algorithms
Price, W. N. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aao5333 · Cited by 254
Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI?
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.160 · Cited by 188
Artificial Intelligence: Threat or Boon to Radiologists?
Recht, M., & Bryan, R. N. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.07.007 · Cited by 172
Abramoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2019) · Yearbook of pediatric endocrinology · doi:10.1530/ey.16.12.1 · Cited by 92
Kohli, A., Mahajan, V., Seals, K. F., Kohli, A., & Jha, S. K. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20410 · Cited by 32
Price, & Nicholson, W. T. (2015) · Harvard journal of law & technology · Cited by 25
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Editorial: Autonomous magnetic resonance imaging
Geethanath, S., Nunes, R. G., & Nielsen, J. (2023) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2023.1277580
Tempering Expectations on the Medical AI Revolution: the medical trainee viewpoint (Preprint)Preprint
Hu, Z., Hu, R., Yau, O., Teng, M. Y., Wang, P., Hu, G., & Singla, R. (2021) · doi:10.2196/preprints.34304
Pfisterer, K. J., Saha, S., Fossat, Y., Grossman, M. R., Wong, A. M., Yadollahi, A., Wang, B., Garg, A., Dinu, L. M., Kale, D., et al. (2023) · doi:10.2196/preprints.54559
Fluid Intelligence: AI’s Role in Accurate Measurement of Ascites
Aisen, A. M., & Rodrigues, P. S. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.240377
The role of artificial intelligence and machine learning in clinical trials
Dri, D. A., Massella, M., Carafa, M., & Marianecci, C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91819-0.00008-7
Tantray, J., Tantray, M. A., Roy, R., Nikumbh, P., & Prajapati, B. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-43823-3.00019-4
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Matsuoka, Y., Sato, T., Ikeguchi, K., Koizumi, N., Anand, A. R., Saravana, S., Sudhir, R. R., Lakshmipathy, M., & Okumura, N. (2026) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2026.100321
Mahak, Singh, S., Sankhayadhar, N., Khan, I. J., & Tripathi, A. (2026) · De Gruyter eBooks · doi:10.1515/9783112226223-001
Legal, Regulatory, and Quality Considerations
Gowda, V., & Harvey, H. B. (2026) · Springer eBooks · doi:10.1007/978-3-032-24568-7_9
Self-citationAydoğan, H. C., Köksal, A., Aygün, A., Teke, H. Y., Catalbas, F., & Çaltekin, İ. (2026) · Tomography · doi:10.3390/tomography12080113
Bakir, M., Masri, M. K. A., Mushtaha, Y., Shaikh, N., Raddaoui, M., & Mohammad, K. S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1960963
Metadata
- Authors
- Harvey, H. B., & Gowda, V.
- Affiliations
- Harvard University (US)
- Corresponding author
- Harvey, H. Benjamin
- Venue
- Academic Radiology
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 27
- Issue
- 1
- Pages
- 58-61
- ISSN
- 1076-6332 (ISSN-L)
- OpenAlex
- W2992147743
- Cited by
- 112
- References
- 12
- Citations by year
- 2026: 12 · 2025: 28 · 2024: 22 · 2023: 15 · 2022: 16 · 2021: 12 · 2020: 6
- FWCI
- 0.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Radiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Regulation
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- AI regulation, FDA, current good manufacturing practice, patient safety, medical device regulation, artificial intelligence in radiology, regulatory burden, quality standards
- MeSH terms
- Artificial Intelligence, Humans, Radiology, United States, United States Food and Drug Administration, Workflow, Patient Safety
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