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
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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
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Assessment of Ventilated Athletic Uniforms for Improved Thermal Comfort
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The current and future state of AI interpretation of medical images
Rajpurkar, P., & Lungren, M. P. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2301725 · Cited by 443
Palaniappan, K., Lin, E. Y. T., & Vogel, S. (2024) · Healthcare · doi:10.3390/healthcare12050562 · Cited by 301
Čartolovni, A., Tomičić, A., & Lazić Mosler, E. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104738 · Cited by 267
Xu, H., & Shuttleworth, K. M. J. (2024) · Intelligent Medicine · doi:10.1016/j.imed.2023.08.001 · Cited by 196
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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