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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Most AI devices, including oncology devices, are Class II, for which the FDA 510(k) pathway applies and requires substantial equivalence to already-cleared tools.
“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]
The FDA regulates AI applications under Software as a Medical Device with processes similar to other SaMD and non-AI algorithms.
“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]
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 · see citing works
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638 · see citing works
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479 · see citing works
Big data and black-box medical algorithms
Price, W. N. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aao5333 · Cited by 254 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 33 · see citing works
Price, & Nicholson, W. T. (2015) · Harvard journal of law & technology · Cited by 25 · see citing works
Price, W. N., II (2014) · bepress Legal Repository · Cited by 22 · see citing works
Assessment of Ventilated Athletic Uniforms for Improved Thermal Comfort
McQuerry, M., & Hogans, K. (2018) · AATCC Journal of Research · doi:10.14504/ajr.5.5.1 · Cited by 7 · see citing works
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Boucher, E. M., Harake, N. R., Ward, H. E., Stoeckl, S. E., Vargas, J., Minkel, J., Parks, A. C., & Zilca, R. (2021) · Expert Review of Medical Devices · doi:10.1080/17434440.2021.2013200 · Cited by 440 · see citing works
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Rajpurkar, P., & Lungren, M. P. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2301725 · Cited by 443 · see citing works
Palaniappan, K., Lin, E. Y. T., & Vogel, S. (2024) · Healthcare · doi:10.3390/healthcare12050562 · Cited by 301 · see citing works
Čartolovni, A., Tomičić, A., & Lazić Mosler, E. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104738 · Cited by 267 · see citing works
Xu, H., & Shuttleworth, K. M. J. (2024) · Intelligent Medicine · doi:10.1016/j.imed.2023.08.001 · Cited by 196 · see citing works
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Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Collaboration between clinicians and vision–language models in radiology report generation
Tanno, R., Barrett, D. G. T., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Lau, C., Tu, T., Azizi, S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03302-1 · Cited by 186 · see citing works
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)
Omoumi, P., Ducarouge, A., Tournier, A., Harvey, H., Kahn, C. E., Verchère, F. L.-D., dos Santos, D. P., Kober, T., & Richiardi, J. (2021) · European Radiology · doi:10.1007/s00330-020-07684-x · Cited by 171 · see citing works
Self-citationImplementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
Daye, D., Wiggins, W. F., Lungren, M. P., Alkasab, T. K., Kottler, N. E., Allen, B., Roth, C. J., Bizzo, B. C., Durniak, K., Brink, J. A., et al. (2022) · Radiology · doi:10.1148/radiol.212151 · Cited by 140 · see citing works
Regulatory Aspects of Artificial Intelligence and Machine Learning
Pantanowitz, L., Hanna, M. G., Pantanowitz, J., Lennerz, J. K., Henricks, W. H., Shen, P. U. F., Quinn, B., Bennet, S., & Rashidi, H. H. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100609 · Cited by 128 · see citing works
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Mello-Thoms, C. R., & Mello, C. A. B. (2023) · British Journal of Radiology · doi:10.1259/bjr.20221031 · Cited by 131 · see citing works
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning
McCradden, M. D., Anderson, J. A., Stephenson, E. A., Drysdale, E., Erdman, L., Goldenberg, A., & Shaul, R. Z. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2021.2013977 · Cited by 117 · see citing works
AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals
Gazquez-Garcia, J., Sánchez-Bocanegra, C. L., & Sevillano, J. L. (2025) · JMIR Medical Education · doi:10.2196/58161 · Cited by 94 · see citing works
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Baydoun, A., Jia, A. Y., Zaorsky, N. G., Kashani, R., Rao, S. K., Shoag, J. E., Vince, R., Bittencourt, L. K., Zuhour, R. J., Price, A. T., et al. (2023) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-023-00684-0 · Cited by 84 · see citing works
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Wichmann, J. L., Willemink, M. J., & Cecco, C. N. D. (2020) · Investigative Radiology · doi:10.1097/rli.0000000000000673 · Cited by 79 · see citing works
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Tejani, A. S., Elhalawani, H. M., Moy, L. P., Kohli, M. D., & Kahn, C. E. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220084 · Cited by 80 · see citing works
Farah, L., Murris, J., Borget, I., Guilloux, A., Martelli, N., & Katsahian, S. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.02.004 · Cited by 75 · see citing works
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Bhandari, A. (2024) · Cureus · doi:10.7759/cureus.72646 · Cited by 85 · see citing works
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Saini, J. P. S., Thakur, A., & Yadav, D. (2025) · RSC Pharmaceutics · doi:10.1039/d4pm00323c · Cited by 76 · see citing works
The first use of artificial intelligence (AI) in the ER: triage not diagnosis
Weisberg, E. M., Chu, L. C. H., & Fishman, E. K. (2020) · Emergency Radiology · doi:10.1007/s10140-020-01773-6 · Cited by 64 · see citing works
Aldhafeeri, F. M. (2025) · Diagnostics · doi:10.3390/diagnostics15182300 · Cited by 61 · see citing works
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 57 · see citing works
Ma, C., Zhang, H., Rao, Y., Jiang, X., Liu, B., Sun, Z., Song, Z., Gao, Y., Cui, Y., Liu, X., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02198-6 · Cited by 52 · see citing works
Olaye, I. M., & Seixas, A. A. (2023) · Journal of Medical Internet Research · doi:10.2196/32962 · Cited by 42 · see citing works
Giansanti, D. (2022) · Healthcare · doi:10.3390/healthcare10101824 · Cited by 41 · see citing works
Farah, L., Davaze‐Schneider, J., Martin, T., Nguyen, P., Borget, I., & Martelli, N. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102547 · Cited by 40 · see citing works
Linguraru, M. G., Bakas, S., Aboian, M., Chang, P., Flanders, A. E., Kalpathy–Cramer, J., Kitamura, F., Lungren, M. P., Mongan, J., Prevedello, L. M. S., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.240225 · Cited by 39 · see citing works
Mähler, M., Auza, C., Albesa, R., Melus, C., & Wu, J. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820239-5.00010-3 · Cited by 38 · see citing works
Reporting Guidelines for Artificial Intelligence in Medical Research
Campbell, J. P., Lee, A., Abràmoff, M. D., Keane, P. A., Ting, D. S. W., Lum, F., & Chiang, M. F. (2020) · Ophthalmology · doi:10.1016/j.ophtha.2020.09.009 · Cited by 36 · see citing works
Benning, L., Peintner, A., & Peintner, L. (2022) · Cancers · doi:10.3390/cancers14030623 · Cited by 32 · see citing works
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Liu, J. Z., & Li, X. (2024) · The Journal of Legal Analysis · doi:10.1093/jla/laae009 · Cited by 30 · see citing works
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Aggarwal, K., Jimeno, M. M., Ravi, K. S., González, R. G., & Geethanath, S. (2023) · NMR in Biomedicine · doi:10.1002/nbm.5014 · Cited by 31 · see citing works
Dang, J., Lal, A., Montgomery, A., Flurin, L., Litell, J. M., Gajic, O., Rabinstein, A. A., Cervantes-Arslanian, A. M., Marcellino, C., Robinson, C., et al. (2023) · BMC Neurology · doi:10.1186/s12883-023-03192-9 · Cited by 29 · see citing works
Clinical applications of AI in MSK imaging: a liability perspective
Harvey, H. B., & Gowda, V. (2022) · Skeletal Radiology · doi:10.1007/s00256-021-03782-z · Cited by 28 · see citing works
Self-citationA foundation model for human-AI collaboration in medical literature mining
Wang, Z., Cao, L., Jin, Q., Chan, J., Wan, N., Afzali, B., Cho, H.-J., Choi, C.-I., Emamverdi, M., Gill, M. K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62058-5 · Cited by 35 · see citing works
Zinchenko, V. V., Arzamasov, K. M., Четвериков, С. Ф., Maltsev, A. V., Петрович, Н. В., Akhmad, E. S., Е., Ш. Д., Андрейченко, А. Е., Vladzymyrskyy, A. V., & Morozov, S. P. (2022) · Современные технологии в медицине · doi:10.17691/stm2022.14.5.02 · Cited by 28 · see citing works
Evaluating artificial intelligence algorithms for use in veterinary radiology
Joslyn, S. K., & Alexander, K. (2022) · Veterinary Radiology & Ultrasound · doi:10.1111/vru.13159 · Cited by 28 · see citing works
Interpretation of Artificial Intelligence Models in Healthcare
Ardakani, A. A., Airom, O., Khorshidi, H., Bureau, N. J., Salvi, M., Molinari, F., & Acharya, U. R. (2024) · Journal of Ultrasound in Medicine · doi:10.1002/jum.16524 · Cited by 29 · see citing works
Dreizin, D., Staziaki, P. V., Khatri, G. D., Beckmann, N. M., Feng, Z., Liang, Y., DelProposto, Z., Klug, M., Spann, J. S., Sarkar, N., et al. (2023) · Emergency Radiology · doi:10.1007/s10140-023-02120-1 · Cited by 28 · see citing works
Evaluation and Regulation of Artificial Intelligence Medical Devices for Clinical Decision Support
Weissman, G. E. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095824 · Cited by 23 · see citing works
A Justifiable Investment in AI for Healthcare: Aligning Ambition with Reality
Karpathakis, K., Morley, J., & Floridi, L. (2024) · Minds and Machines · doi:10.1007/s11023-024-09692-y · Cited by 24 · see citing works
Tavakoli, N., Abad, Z. S. H., Gowda, V., Samsel, K., Bedayat, A., Ghasemiesfe, A., Bağcı, U., Hsiao, A., Leiner, T., Carr, J., et al. (2025) · Radiology · doi:10.1148/radiol.242961 · Cited by 26 · see citing works
Self-citationWinkler, P. W., Zsidai, B., Senorski, E. H., Pruneski, J. A., Hirschmann, M. T., Ley, C., Tischer, T., Herbst, E., Pareek, A., Musahl, V., et al. (2025) · Journal of Experimental Orthopaedics · doi:10.1002/jeo2.70247 · Cited by 20 · see citing works
Ahmad, M. A., Overman, S., Allen, C. M. B., Kumar, V., Teredesai, A. M., & Eckert, C. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3470823 · Cited by 20 · see citing works
Bhatia, N. K., Trivedi, H., Safdar, N. M., & Heilbrun, M. E. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.08.002 · Cited by 19 · see citing works
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Bendre, S., Shinde, K., Kale, N., & Gilda, S. (2022) · Asian Journal of Pharmacy and Technology · doi:10.52711/2231-5713.2022.00040 · Cited by 19 · see citing works
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Tikhomirov, L., Semmler, C., Prizant, N., Bhasin, S., Kenyon, G., van der Vegt, A., Erdman, L., Kurian, N. C., Thompson, H., Palmer, L. J., et al. (2026) · Nature Health · doi:10.1038/s44360-025-00048-z · Cited by 17 · see citing works
Alis, D. C., Tanyel, T., Meltem, E., Şeker, M. E., Şeker, D., Karakaş, H. M., Karaarslan, E., & Öksüz, İ. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.232658 · Cited by 16 · see citing works
Regulatory challenges in ai-based diagnostics: Legal implications of ai use in medical diagnostics
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Lui, Y. W., Geras, K. J., Block, K. T., Parente, M., Hood, J., & Recht, M. P. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.09.039 · Cited by 15 · see citing works
Hu, Y., Liu, J., & Jiang, W. (2025) · Renal Failure · doi:10.1080/0886022x.2025.2555686 · Cited by 14 · see citing works
Aritra, S., Shikha, C. B., & Indu, S. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.001 · Cited by 17 · see citing works
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Self-citationChallenges of Machine Learning and AI (What Is Next?), Responsible and Ethical AI
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Jha, S. K., & Cook, T. S. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.11.003 · Cited by 13 · see citing works
Mocking, T. R., van de Loosdrecht, A. A., Cloos, J. J., & Bachas, C. (2025) · HemaSphere · doi:10.1002/hem3.70138 · Cited by 12 · see citing works
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Sumner, C., Kietzman, A. T., Kadom, N., Frigini, L. A., Makary, M. S., Martin, A., McKnight, C. D., Retrouvey, M., Spieler, B. M., & Griffith, B. D. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.08.015 · Cited by 13 · see citing works
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Murphy, T. I., Armitage, J. A., van Wijngaarden, P., Abel, L. A., & Douglass, A. G. (2023) · Clinical and Experimental Optometry · doi:10.1080/08164622.2023.2197578 · Cited by 7 · see citing works
Automation: A revolutionary vision of artificial intelligence in theranostics
Panesar, G., Batra, S., & Sharma, S. (2022) · Bulletin du Cancer · doi:10.1016/j.bulcan.2022.10.009 · Cited by 6 · see citing works
Hu, Z., Hu, R., Yau, O., Teng, M. Y., Wang, P., Hu, G., & Singla, R. (2022) · JMIR Medical Informatics · doi:10.2196/34304 · Cited by 7 · see citing works
Fajkis-Zajączkowska, N., Zawada, A., Bojko, M., & Kolasa, K. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110750 · Cited by 5 · see citing works
Patient safety in AI-powered diagnostic pathology
Rugge, M., Fraschini, M., Orvieto, E., Didaci, L., Sandonà, L., D’Amuri, A., Saba, L., & Faa, G. (2025) · Journal of Clinical Pathology · doi:10.1136/jcp-2025-210231 · Cited by 5 · see citing works
Singh, S., Kumar, S., Quadir, S. S., Bhandari, S., Baniya, B., Joshi, G., Jain, C. P., & Choudhary, D. K. (2025) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-025-09940-3 · Cited by 5 · see citing works
Boyer, P., Donia, J., Whyne, C. M., Burns, D., & Shaw, J. (2022) · Health Policy and Technology · doi:10.1016/j.hlpt.2022.100648 · Cited by 5 · see citing works
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Penteado, B. E., Fornazin, M., Castro, L., & Rachid, R. (2022) · ACM SIGCPR Conference on Computer Personnel Research (SIGCPR) · doi:10.1145/3510606.3551898 · Cited by 5 · see citing works
Youssef, A., Fronk, D., Grimes, J. N., Cheuy, L. Y., & Larson, D. B. (2024) · medRxiv · doi:10.1101/2024.07.12.24309602 · Cited by 5 · see citing works
Zhang, P.-F., Ting, H.-N., & Chang, S. (2025) · IEEE Access · doi:10.1109/access.2025.3584401 · Cited by 4 · see citing works
Gowda, V., Bizzo, B. C., & Dreyer, K. J. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.05.032 · Cited by 4 · see citing works
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Mustafa, S. M. N. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-7051-3.ch012 · Cited by 5 · see citing works
Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?
Mutasa, S., & Yi, P. H. (2021) · Skeletal Radiology · doi:10.1007/s00256-021-03850-4 · Cited by 4 · see citing works
AI in Healthcare: Ethical and Regulatory Challenges
Abdallah, M. M. A., Abdallah, Q., Abukhalil, A., & Barqawi, L. (2025) · International Conference on New Trends in Computing Sciences (ICTCS) · doi:10.1109/ictcs65341.2025.10989298 · Cited by 3 · see citing works
Artificial Intelligence Across the Drug Development Lifecycle
Demyashkin, G. A., Алексеевич, П. М., Zyryanov, S., Yavorskiy, A., Shegai, P., & Kaprin, A. (2026) · Medical Sciences · doi:10.3390/medsci14020248 · Cited by 4 · see citing works
Raz, A. E., Inbar, Y., Avnoon, N., Lifshitz-Milwidsky, L. B., Hofman, B., Honig, A., & Paz, Z. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1651934 · Cited by 4 · see citing works
Rana, S., Ghahremani, J. S., & Navarro, R. A. (2024) · Journal of Shoulder and Elbow Surgery · doi:10.1016/j.jse.2024.08.008 · Cited by 3 · see citing works
Lee, H., Kim, G., & Bae, S. W. (2024) · Journal of Yeungnam Medical Science · doi:10.12701/jyms.2025.42.19 · Cited by 3 · see citing works
Penteado, B. E., Fornazin, M., Castro, L., Rachid, R., Gonçalves, L. H. D. N., & Falcão, M. Z. (2025) · Health and Technology · doi:10.1007/s12553-025-01015-0 · Cited by 2 · see citing works
Yan, Y., Zeng, J.-Q., & Ding, X. (2026) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v7.i1.112357 · Cited by 2 · see citing works
Advances in artificial intelligence in prostate cancer pathology
Chan, G. J., & Ding, C. C. (2026) · Seminars in Diagnostic Pathology · doi:10.1016/j.semdp.2026.150995 · Cited by 2 · see citing works
Lin, Y. J., & Komperda, I. (2026) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-026-02330-x · Cited by 3 · see citing works
Vrdoljak, A. (2022) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2022.11.020 · Cited by 2 · see citing works
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features
Valentin, R., Katz, S. M., Lee, J., Walker, D., Sorgenfrei, M., & Kochenderfer, M. J. (2024) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc62030.2024.10748707 · Cited by 2 · see citing works
AI in the Detection of Breast Cancer and Related Ethical and Legal Issues
Mukhopadhyay, K. D., Guha, T., & Basu, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8457-8_9 · Cited by 2 · see citing works
Potential and role of artificial intelligence in current medical healthcare
Hung, C.-M., Shi, H., Lee, P., Chang, C., Rau, K., Lee, H.-M., Tseng, C.-H., Pei, S., Tsai, K.-J., & Chiu, C. (2022) · WArtificial Intelligence in Cancer · doi:10.35713/aic.v3.i1.1 · Cited by 2 · see citing works
Establishing a policy statement on the use of artificial intelligence in neurosurgery
Mohamed, A. A., Das, N., Still, M. E., Sharma, A. M., Lucke-Wold, B. P., Schirmer, C. M., Pirris, S. M., Adogwa, O., Prevedello, D. M., & Vessell, M. (2025) · Neurosurgical Review · doi:10.1007/s10143-025-03745-1 · Cited by 3 · see citing works
AI: A Use Case for Global Health
Srivastava, D. (2024) · LSE Public Policy Review · doi:10.31389/lseppr.109 · Cited by 1 · see citing works
Gap Analysis on Regulatory Frameworks for AI-embedded Medical Devices
Balogun, E., & Luetge, C. (2025) · IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx) · doi:10.1109/citrexcompanion65208.2025.10981493 · Cited by 1 · see citing works
Policy brief: AI-first Medicaid: how CMS can build a smarter safety net with Precision Benefits
Favini, N., Batlivala, N., Mijic, M., & Daneshjou, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02195-9 · Cited by 1 · see citing works
Zaman, S., Sasankan, P., Amyar, A., & Tsao, C. W. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1637962 · Cited by 1 · see citing works
Algorithmic opacity in opioid risk scoring and the need for transparent AI regulation
Sherry Yun, W. A. N. G., Stofer, R., Yu, Q., Li, A., Mai, G., & Wang, H. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02491-y · Cited by 1 · see citing works
Navigating the sociotechnical labyrinth: dynamic certification for responsible embodied AI
Bakirtzis, G., Tubella, A. A., Theodorou, A. A., Danks, D., & Topcu, U. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-340553-2.00019-8 · Cited by 1 · see citing works
Ethical and Legal Risks of Artificial Intelligence in Radiology
D’Antonoli, T. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-99680-2_12
- 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 · 2019: 1
- 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 › Health Professions › Health Information Management › 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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