Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations
Open accessAbstract
Integration of AI-enabled algorithms into the radiology workflow presents a complex array of challenges that span operational, technical, clinical, and regulatory domains. Successfully overcoming these hurdles requires a multifaceted approach, including strategic planning, educational initiatives, and careful consideration of the practical implications for radiologists' workloads. Institutions must navigate these challenges with a clear understanding of the potential benefits and limitations of both vended and in-house developed AI tools.
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As of 2024, the FDA had cleared or approved approximately 700 AI/ML-enabled medical devices, 76% of which are related to radiology.
“As of 2024, the FDA had cleared or approved approximately 700 AI/ML-enabled medical devices, 76% of which are related to radiology.”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [29]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215 · see citing works
Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.0265 · Cited by 409 · see citing works
The limits of fair medical imaging AI in real-world generalization
Yang, Y., Zhang, H., Gichoya, J. W., Katabi, D., & Ghassemi, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03113-4 · Cited by 285 · see citing works
Allen, B., Seltzer, S. E., Langlotz, C. P., Dreyer, K., Summers, R. M., Petrick, N., Marinac‐Dabic, D., Cruz, M. L., Alkasab, T. K., Hanisch, R. J., et al. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.04.014 · Cited by 128 · see citing works
Artificial Intelligence and Machine Learning in Radiology
Wichmann, J. L., Willemink, M. J., & Cecco, C. N. D. (2020) · Investigative Radiology · doi:10.1097/rli.0000000000000673 · Cited by 79 · see citing works
Understanding and Appreciating Burnout in Radiologists
Bailey, C. R., Bailey, A. M., McKenney, A. S., & Weiss, C. R. (2022) · Radiographics · doi:10.1148/rg.220037 · Cited by 77 · see citing works
Translating AI to Clinical Practice: Overcoming Data Shift with Explainability
Choi, Y., Yu, W., Nagarajan, M. B., Teng, P., Goldin, J. G., Raman, S. S., Enzmann, D. R., Kim, G. H. J., & Brown, M. S. (2023) · RadioGraphics · doi:10.1148/rg.220105 · Cited by 65 · see citing works
Artificial Intelligence in Radiology: Opportunities and Challenges
Flory, M. N., Napel, S., & Tsai, E. B. (2024) · Seminars in Ultrasound, CT and MRI · doi:10.1053/j.sult.2024.02.004 · Cited by 29 · see citing works
DEWEY: The DICOM-Enabled Workflow Engine System
Erickson, B. J., Langer, S. G., Blezek, D. J., Ryan, W. J., & French, T. L. (2014) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-013-9661-0 · Cited by 15 · see citing works
Self-citationFailures Hiding in Success for Artificial Intelligence in Radiology
Purkayastha, S., Trivedi, H., & Gichoya, J. W. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.11.008 · Cited by 10 · see citing works
Translation of Artificial Intelligence Into Practice: The Radiologist as a Vendor
Rothenberg, S., Gupta, S. L., Boonn, W. W., & Kim, W. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.06.021 · Cited by 4 · see citing works
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Research works printed but not linked: 1
- [14]Artificial intelligence and machine learning (AI/ML)-enabled medical devices 2023. FDA https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices PMC11791695 39680415
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Suleman, M. U., Mursaleen, M., Khalil, U., Saboor, A., Bilal, M., Khan, S. A., Subhani, M. A., Hussnain, M. A., Tabassum, S. N., & Tahir, M. R. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000004166 · Cited by 69 · see citing works
Costea, D., Dobrin, N., Tătaru, C.-I., Toader, C., Șerban, M., Covache-Busuioc, R.-A., Munteanu, O., & Diaconescu, B. I. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110546 · Cited by 27 · see citing works
Artificial Intelligence in Microsurgical Planning: A Five-Year Leap in Clinical Translation
Shadid, O. H., Seth, I., Cuomo, R., Rozen, W. M., & Marcaccini, G. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14134574 · Cited by 23 · see citing works
Wong, A. V.-K., Roslan, N. L., McDonald, R., Noor, J. M., Hutchings, S., D’costa, P., Via, G., & Corradi, F. (2025) · The Ultrasound Journal · doi:10.1186/s13089-025-00436-2 · Cited by 15 · see citing works
Huo, T., Wu, W., Chen, X., Xue, M., Liu, P., Zhang, J., Xie, Y., Wang, H., Zhou, H., Yan, Z., et al. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.06.005 · Cited by 14 · see citing works
Rickard, D., Kabir, M. A., & Homaira, N. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108802 · Cited by 14 · see citing works
Panayides, A. S., Chen, H., Filipović, N., Geroski, T., Hou, J., Lekadir, K., Kostas Marias, A., Matsopoulos, G. K., Papanastasiou, G., Sarder, P., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3649496 · Cited by 13 · see citing works
Sorin, V., Korfiatis, P. D., Bratt, A. K., Leiner, T., Wald, C., Butler, C. S., Cook, C. J., Kline, T. L., & Collins, J. D. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.06.036 · Cited by 9 · see citing works
Self-citationHuman-AI Complementarity in Diagnostic Radiology: The Case of Double Reading
Wagner, I. A., & Chakradeo, K. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00886-5 · Cited by 8 · see citing works
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
Chang, Y.-C., Toro, C. D., Gjolaj, J. P., Braga, T. A., & Subhawong, T. K. (2025) · North American Spine Society Journal (NASSJ) · doi:10.1016/j.xnsj.2025.100621 · Cited by 5 · see citing works
Kumar, Y., Cardan, R., Chang, H. H., Heinzman, K. A., Gultekin, K., Goss, A. M., McDonald, A. M., Murdaugh, D. L., McConathy, J., Rothenberg, S., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15070803 · Cited by 5 · see citing works
Pucciarelli, F., Silveri, G. G., Zerunian, M., Santis, D. D., Polici, M., Gaudio, A. D., Masci, B., Polidori, T., Tremamunno, G., Persechino, R., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11090310 · Cited by 4 · see citing works
Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions
Hartsock, I., Koutsoubis, N., Ahmed, S., Parker, N. H., Schabath, M. B., Araujo, C. R., Qayyum, A., Lam, C. A., Gatenby, R. A., & Rasool, G. (2026) · Cancers · doi:10.3390/cancers18060942 · Cited by 4 · see citing works
Hsu, C., Yang, S., Lee, Y.-T., Yao, K.-H., Hsu, T.-H., Chung, P., Chu, Y.-C., Kuo, C.-T., & Lien, C.-Y. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01425-6 · Cited by 3 · see citing works
A modern radiologist’s guide to artificial intelligence
Kapur, J., Kelly, B. S., & Vega, R. (2026) · Pediatric Radiology · doi:10.1007/s00247-026-06542-0 · Cited by 3 · see citing works
Coombs, L. P., Brink, L., Kim, W., Wald, C., Schmidt, K., Tilkin, M., Dreyer, K., Cook, T. S., & Bizzo, B. C. (2026) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2026.04.024 · Cited by 3 · see citing works
AI for Lesion Detection in Musculoskeletal Radiology
Kim, S. J., Lee, H. S., Hwang, S., & Yoon, Y. (2025) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2025.0081 · Cited by 2 · see citing works
EcoRad: sustainable radiology and the ecology of economics
Northrup, B. E., Hanneman, K. A., & Omary, R. A. (2024) · BJR|Open · doi:10.1093/bjro/tzaf027 · Cited by 2 · see citing works
Glinkowski, W., Kamiński, P., & Obuchowicz, R. (2026) · Diagnostics · doi:10.3390/diagnostics16101420 · Cited by 2 · see citing works
Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology
Doo, F. X., Davis, M., Poff, J. A., Lui, Y. W., Haines, K., & Towbin, A. J. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.250982 · Cited by 2 · see citing works
Kurisaki, K., Kobayashi, S., Akashi, T., Nakao, Y., Fukumoto, M., Tasaki, K., Adachi, T., Eguchi, S., & Kanetaka, K. (2026) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v18.i1.111357 · Cited by 1 · see citing works
Neal, J. T., Hoffmann, R. M., Chiang, E. L., & Gravel, C. A. (2026) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000001555 · Cited by 1 · see citing works
A Native Strategy for Integrating Deep-Learning Models for Segmentation into a Radiological Viewer
Xiberta, P., Ruiz, M., Vila, M., Juanola, A. J. I., Puig, J., Vilanova, J. C., & Boada, I. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01801-2 · Cited by 1 · see citing works
Antonissen, N. J. C., Schalekamp, S., Hahn, H., van Leeuwen, K. G., & Jacobs, C. (2026) · European Radiology · doi:10.1007/s00330-026-12580-x · Cited by 1 · see citing works
Kunze, K. N., & Barad, A. V. (2026) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1002/arj.70231 · Cited by 1 · see citing works
Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review
Rai, M. C. E., Far, A. B., Darweesh, M., Dhou, S., Aburaed, N., El, S., Elkhazendar, M., & Ellahham, S. (2026) · Diagnostics · doi:10.3390/diagnostics16101485 · Cited by 1 · see citing works
Deep Learning in Dental Imaging: Advances, Challenges, and Future
AYDIN, E. C., & Can, Z. (2026) · Oral Radiology · doi:10.1007/s11282-026-00939-1 · Cited by 1 · see citing works
Saliba, T., Rotzinger, D. C., & Fahrni, G. (2026) · F1000Research · doi:10.12688/f1000research.182574.1 · Cited by 1 · see citing works
Raab, F., & Strotzer, Q. (2025) · European Radiology · doi:10.1007/s00330-025-12260-2 · Cited by 1 · see citing works
HOT-FIT-Based Evaluation of the Outpatient Registration System at RSUD Komodo
Pandur, F. D. N., & Widiyanto, W. W. (2025) · International Journal of Health and Social Behavior · doi:10.62951/ijhsb.v2i2.387
Chervenkov, L., Miteva, D., & Velikova, T. V. (2025) · PubMed · doi:10.5492/wjccm.v14.i3.102808
(2025) · Issues in Information Systems · doi:10.48009/2_iis_132
Implementation Science: Definition, Importance, and Application in Neuroradiology
Rashidi, A., & Sadigh, G. (2025) · American Journal of Neuroradiology · doi:10.3174/ajnr.a9104
Marjanović, M., & Latinović, L. (2026) · Serbian Journal of Engineering Management · doi:10.5937/sjem2600076m
da Silva, M. C. B., de Andrade, P. B., Lee, H. M. H., Silva, P. V. A., Ferreira, A. C., Kuss, C. P., Rodrigues, M., Ribeiro, G., de Camargo, T. F. O., Fan, W. Y. C., et al. (2026) · JMIR Research Protocols · doi:10.2196/88626
Yemeshev, Y., Nurmashev, B., & Koçyiğit, B. F. (2026) · Central Asian Journal of Medical Hypotheses and Ethics · doi:10.47316/cajmhe.2026.7.1.06
Istiqomah, H., Fatmasari, D., Hariri, A., Masrochah, S., & Wibowo, G. M. (2026) · INTERNATIONAL JOURNAL OF CLINICAL SCIENCE AND MEDICAL RESEARCH · doi:10.55677/ijcsmr/v6i4-04/2026
Istiqomah, H., Fatmasari, D., Hariri, A., Masrochah, S., & Wibowo, G. M. (2026) · Jurnal Ners · doi:10.31004/jn.v10i3.57519
Liu, S., & Liang, R. (2026) · Digital Health · doi:10.1177/20552076251404503
Features of Artificial Intelligence Driven Pioneer Hospitals: Insights from Radiology
Goodarzi, M., Torabi, M., Safdari, R., Ahmadi, M., Elmi, S., Golmahi, F., Mortezaie, S., Nezari, P., Souraki, M. A., Shantia, S., et al. (2026) · Iranian Journal of Radiology · doi:10.5812/iranjradiol-166577
Inteligencia artificial en radiología: beneficios, desafíos y aplicaciones actuales
Ayala, L. A. L., Rojas, S. A. R., & Ramirez, O. L. A. (2026) · Biociencias (UNAD) · doi:10.22490/26194759.11940
Ayyala, R. S., Lee, A., Smith, R., Towbin, A. J., & Coley, B. D. (2026) · American Journal of Roentgenology · doi:10.2214/ajr.26.35394
Huang, J., Ong, W., Zheng, A. L. W., Tan, T. S. E., Zheng, G. T. C., Teo, E. C., Tan, J. H., Kumar, N., & Hallinan, J. T. P. D. (2026) · Cancers · doi:10.3390/cancers18172794
Yi, W., Chen, Y., Feng, X., & Xia, T. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1927284
A Prototyping Platform for AI-Assisted Decision-Making in Clinical Radiology
Fang, N., Pluyter, J. R., Chen, L.-L., & Funk, M. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3821402.3830228
Li, Z., Tang, C., Feng, P., Wang, X., Zhang, H., Lan, Y., & Wang, N. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1916223
Hu, Z., Lin, H. M., Chen, Y. A., Lozano, C. S., Patel, M. D., Emami, A., Sejdić, E., & Colak, E. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001786
Malik, P. K. (2026) · Annales Academiae Medicae Silesiensis · doi:10.18794/aams/241936
- Authors
- Korfiatis, P., Kline, T. L., Meyer, H. M., Khalid, S., Leiner, T., Loufek, B. T., Blezek, D., Vidal, D. E., Hartman, R. P., Joppa, L. J., Missert, A. D., Potretzke, T. A., Taubel, J. P., Tjelta, J. A., Callstrom, M. R., & Williamson, E. E.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Williamson, Eric E.
- ORCID
- Venue
- Mayo Clinic Proceedings Digital Health
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 3
- Issue
- 1
- Pages
- 100188
- ISSN
- 2949-7612 (ISSN-L)
- PMC
- PMC11975811
- OpenAlex
- W4405526581
- Cited by
- 46
- References
- 13
- Citations by year
- 2026: 26 · 2025: 19 · 2024: 1
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Mayo Clinic Proceedings Digital Health (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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 › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in radiology, radiology workflow, AI implementation challenges, radiologist workload, clinical implementation of AI, AI regulation, radiologist education
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