Artificial Intelligence in Imaging: The Radiologist’s Role
Open accessAbstract
Rapid technological advancements in artificial intelligence (AI) methods have fueled explosive growth in decision tools being marketed by a rapidly growing number of companies. AI developments are being driven largely by computer scientists, informaticians, engineers, and businesspeople, with much less direct participation by radiologists. Participation by radiologists in AI is largely restricted to educational efforts to familiarize them with the tools and promising results, but techniques to help them decide which AI tools should be used in their practices and to how to quantify their value are not being addressed. This article focuses on the role of radiologists in imaging AI and suggests specific ways they can be engaged by (1) considering the clinical need for AI tools in specific clinical use cases, (2) undertaking formal evaluation of AI tools they are considering adopting in their practices, and (3) maintaining their expertise and guarding against the pitfalls of overreliance on technology.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Over-reliance or under-reliance on ai is wrong when decoupled from considerations on the relative informativeness of the ai.
“over-reliance or under-reliance on ai, regard a general attitude towards support systems, which is wrong when decoupled from considerations on the relative informativeness of the ai39,41,42.”
From Experimental evidence of effective human–AI collaboration in medical decision-making (2022) · cites this paper as [42]
In “Maintaining radiologist expertise” (page not provided), Rubin warns that blindly accepting AI output can lead radiologists astray, particularly when an algorithm performs poorly on unusual cases. “Evaluation of AI tools” also urges testing performance on local data. The paper does not address under-reliance or frame the issue as a general attitude that must be judged against AI’s informativeness relative to the user’s. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSome technical understanding is imperative for quality and safety assessment and therefore creates trust in the AI application's reliability.
“However, some technical understanding is imperative for quality and safety assessment and therefore create trust in the AI application’s reliability [8, 21, 30].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works
Artificial intelligence in cancer imaging: Clinical challenges and applications
Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846 · see citing works
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · see citing works
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449 · see citing works
Measures of Diagnostic Accuracy: Basic Definitions.
Šimundić, A.-M. (2009) · PubMed Central · Cited by 1,266 · see citing works
Measures of diagnostic accuracy: basic definitions
Šimundić, A.-M. (2009) · Medical and Biological Sciences · Cited by 1,144 · see citing works
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31645-3 · Cited by 1,020 · see citing works
Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works
Deep learning in medical imaging and radiation therapy
Sahiner, B., Pezeshk, A. X., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13264 · Cited by 739 · see citing works
Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M., Tan, T., Mertelmeier, T., et al. (2019) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djy222 · Cited by 692 · see citing works
Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection
Lehman, C. D., Wellman, R. D., Buist, D. S. M., Kerlikowske, K., Tosteson, A. N. A., & Miglioretti, D. L. (2015) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.5231 · Cited by 678 · see citing works
Influence of Computer-Aided Detection on Performance of Screening Mammography
Fenton, J. J., Taplin, S. H., Carney, P. A., Abraham, L., Sickles, E. A., D’Orsi, C. J., Berns, E. A., Cutter, G., Hendrick, R. E., Barlow, W. E., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa066099 · Cited by 619 · see citing works
Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
Rodríguez-Ruiz, A., Krupinski, E., Mordang, J.-J., Schilling, K., Heywang-Köbrunner, S. H., Sechopoulos, I., & Mann, R. M. (2019) · Radiology · doi:10.1148/radiol.2018181371 · Cited by 602 · see citing works
Kim, D. W., Jang, H. Y., Kim, K. W., Shin, Y., & Park, S. H. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0025 · Cited by 459 · see citing works
Sejnowski, T. J. (2018) · doi:10.7551/mitpress/11474.001.0001 · Cited by 438 · see citing works
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., Lee, M. J., & Asadi, H. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.20224 · Cited by 410 · see citing works
Super‐resolution musculoskeletal <scp>MRI</scp> using deep learning
Chaudhari, A. S., Fang, Z., Kogan, F., Wood, J., Stevens, K. J., Gibbons, E. K., Lee, J. H., Gold, G. E., & Hargreaves, B. A. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27178 · Cited by 406 · see citing works
Lin, W.-M., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., Guo, G., Xiao, M., Du, M., Qu, X., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00777 · Cited by 391 · see citing works
Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT
Betancur, J., Commandeur, F., Motlagh, M. E., Sharir, T., Einstein, A. J., Bokhari, S., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., et al. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.01.020 · Cited by 359 · see citing works
Accuracy of Screening Mammography Interpretation by Characteristics of Radiologists
Barlow, W. E., Chi, C., Carney, P. A., et al. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh333 · Cited by 218 · see citing works
Highly accurate model for prediction of lung nodule malignancy with CT scans
Causey, J., Zhang, J., Ma, S., Jiang, B., Qualls, J., Politte, D. G., Prior, F., Zhang, S., & Huang, X. (2018) · Scientific Reports · doi:10.1038/s41598-018-27569-w · Cited by 214 · see citing works
Nie, D., Lu, J., Zhang, H., Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., & Shen, D. (2019) · Scientific Reports · doi:10.1038/s41598-018-37387-9 · Cited by 213 · see citing works
Tong, N., Gou, S., Yang, S., Ruan, D., & Sheng, K. (2018) · Medical Physics · doi:10.1002/mp.13147 · Cited by 208 · see citing works
Improvement of image quality at CT and MRI using deep learning
Higaki, T., Nakamura, Y., Tatsugami, F., Nakaura, T., & Awai, K. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0796-2 · Cited by 208 · see citing works
Li, W., Cao, P., Zhao, D., & Wang, J. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/6215085 · Cited by 199 · see citing works
Addressing Burnout in Radiologists
Chetlen, A. L., Chan, T. L., Ballard, D. H., Frigini, L. A., Hildebrand, A. D., Kim, S., Brian, J. M., Krupinski, E. A., & Ganeshan, D. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.07.001 · Cited by 188 · see citing works
Burt, J. R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F., Hussein, S., & Bağcı, U. (2018) · British Journal of Radiology · doi:10.1259/bjr.20170545 · Cited by 171 · see citing works
Nishio, M., Sugiyama, O., Yakami, M., Ueno, S., Kubo, T., Kuroda, T., & Togashi, K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200721 · Cited by 170 · see citing works
A review on lung boundary detection in chest X-rays
Candemir, S., & Antani, S. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01917-1 · Cited by 170 · see citing works
Effectiveness of Computer-Aided Detection in Community Mammography Practice
Fenton, J. J., Abraham, L., Taplin, S. H., Geller, B. M., Carney, P. A., D’Orsi, C. J., Elmore, J. G., & Barlow, W. E. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr206 · Cited by 168 · see citing works
Le, M. H., Chen, J., Wang, L., Wang, Z., Liu, W., Cheng, K.-T. T., & Yang, X. Y. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa7731 · Cited by 161 · see citing works
Haan, M., Ongena, Y. P., Hommes, S., Kwee, T. C., & Yakar, D. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.12.043 · Cited by 141 · see citing works
Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas
Liang, S., Zhang, R., Liang, D., Song, T., Ai, T., Chen, X., Xia, L., & Wang, Y. (2018) · Genes · doi:10.3390/genes9080382 · Cited by 133 · see citing works
Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography
Alberdi, E., Povyakalo, A., Strigini, L., & Ayton, P. (2004) · Academic Radiology · doi:10.1016/j.acra.2004.05.012 · Cited by 124 · see citing works
Qiu, Y., Yan, S., Gundreddy, R. R., Wang, Y., Cheng, S. S. M., Liu, H., & Zheng, B. (2017) · Journal of X-Ray Science and Technology · doi:10.3233/xst-16226 · Cited by 107 · see citing works
Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience
Yoo, Y. J., Ha, E. J., Cho, Y. J., Kim, H. L., Han, M., & Kang, S. Y. (2018) · Korean Journal of Radiology · doi:10.3348/kjr.2018.19.4.665 · Cited by 90 · see citing works
Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features
Shi, B., Grimm, L. J., Mazurowski, M. A., Baker, J. A., Marks, J. R., King, L. M., Maley, C. C., Hwang, E. S., & Lo, J. Y.-C. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.11.036 · Cited by 88 · see citing works
Winkel, D. J., Heye, T. J., Weikert, T., Boll, D. T., & Stieltjes, B. (2018) · Investigative Radiology · doi:10.1097/rli.0000000000000509 · Cited by 83 · see citing works
U‐Net based deep learning bladder segmentation in CT urography
Ma, X., Hadjiiski, L. M., Wei, J., Chan, H., Cha, K. H., Cohan, R. H., Caoili, E. M., Samala, R. K., Zhou, C., & Lu, Y. (2019) · Medical Physics · doi:10.1002/mp.13438 · Cited by 72 · see citing works
Short-Term Outcomes of Screening Mammography Using Computer-Aided Detection
Fenton, J. J., Xing, G., Elmore, J. G., Bang, H., Chen, S. L., Lindfors, K. K., & Baldwin, L. M. (2013) · Annals of Internal Medicine · doi:10.7326/0003-4819-158-8-201304160-00002 · Cited by 63 · see citing works
Pan, I., Agarwal, S., & Merck, D. L. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00180-9 · Cited by 63 · see citing works
Cha, K. H., Hadjiiski, L. M., Cohan, R. H., Chan, H., Caoili, E. M., Davenport, M. S., Samala, R. K., Weizer, A. Z., Alva, A. S., Kirova, G., et al. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.10.010 · Cited by 62 · see citing works
Interpretation Time of Computer-aided Detection at Screening Mammography
Tchou, P. M., Haygood, T. M., Atkinson, E. N., Stephens, T. W., Davis, P., Arribas, E. M., Geiser, W. R., & Whitman, G. J. (2010) · Radiology · doi:10.1148/radiol.10092170 · Cited by 49 · see citing works
Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging
Bharti, P., Mittal, D., & Ananthasivan, R. (2016) · Ultrasonic Imaging · doi:10.1177/0161734616639875 · Cited by 42 · see citing works
UNDERSTANDING HUMAN OVER-RELIANCE ON TECHNOLOGY.
Grissinger, M. C. (2017) · PubMed Central · Cited by 42 · see citing works
Sadda, P., İmamoğlu, M., Dombrowski, M., et al. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1886-4 · Cited by 32 · see citing works
Shaish, H. A., Mutasa, S., Makkar, J. S., Chang, P., Schwartz, L. H., & Ahmed, F. S. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.20094 · Cited by 19 · see citing works
Introduction to Statistical Methods in Pathology
Momeni, A., Pincus, M. R., & Libien, J. M. (2017) · Springer eBooks · doi:10.1007/978-3-319-60543-2 · Cited by 18 · see citing works
Order Entry Protocols Are an Amenable Target for Workflow Automation
Tudor, J., Klochko, C. L., Patel, M., & Siegal, D. S. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.02.003 · Cited by 11 · see citing works
Momeni, A., Pincus, M. R., & Libien, J. M. (2017) · Springer eBooks · doi:10.1007/978-3-319-60543-2_2 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [1]Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks In: Advances in Neural Information Processing Systems 25: Curran Associates, Inc., 2012; 1097– 1105.
- [5]Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going Deeper with Convolutions. 2015 Ieee Conference on Computer Vision and Pattern Recognition (Cvpr) 2015: 1– 9.
- [6]He KM, Zhang XY, Ren SQ, Sun J. Deep Residual Learning for Image Recognition. 2016 Ieee Conference on Computer Vision and Pattern Recognition (Cvpr) 2016: 770– 778.
Cited by
Sorted by citations
Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00611-y · Cited by 423 · see citing works
Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P. C., & Moors, E. H. M. (2020) · European Radiology · doi:10.1007/s00330-020-06946-y · Cited by 329 · see citing works
Experimental evidence of effective human–AI collaboration in medical decision-making
Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., GI Genius CADx Study Group, Antonelli, G., Awadie, H., Bernhofer, S., Carballal, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-18751-2 · Cited by 235 · see citing works
- Authors
- Rubin, D. L.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Rubin, Daniel L.
- ORCID
- Rubin, Daniel L.
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 16
- Issue
- 9
- Pages
- 1309-1317
- ISSN
- 1546-1440 (ISSN-L)
- PMC
- PMC6733578
- OpenAlex
- W2971356145
- Cited by
- 110
- References
- 53
- Citations by year
- 2026: 9 · 2025: 24 · 2024: 19 · 2023: 11 · 2022: 20 · 2021: 14 · 2020: 12 · 2019: 1
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American College of Radiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute
- National Institutes of Health (1U01CA187947, U01CA142555, 1U01CA190214)
- 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, radiologist role, artificial intelligence adoption, technology dependence, radiologist expertise, AI tool selection
- MeSH terms
- Radiologists, Deep Learning, Algorithms, Artificial Intelligence, Humans, Image Processing, Computer-Assisted, Practice Patterns, Physicians', Radiology, ROC Curve, Role, Sensitivity and Specificity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
