The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.
Artificial intelligence in radiology
Open accessAbstract
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For instance, recent advances in AI-powered tools have shown similar or better sensitivity for detecting minuscule deviations from normal anatomy, compared to a human assessor (20, 38–40).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [40]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Especially in medical imaging there is a large potential as deep learning can successfully be used for identifying and segmenting objects within the 3-dimensional (3D) image space13–16.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [13]
In the citing paper
“Many AI applications in health care have demonstrated performance that matches or even surpasses that of human physicians in medical diagnosis, execution of treatment and surgery.1–3”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [3]
In the citing paper
“AI systems also make errors in reading, classifying, and reconstructing radiological images, but the error rate is decreasing, and AI systems will soon outperform humans in terms of speed and accuracy of image reading [12, 17, 103, 228].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [103]
In the citing paper
“Studies have shown that an average radiologist now needs to interpret one image every 3–4 s to keep up with clinical workloads3–5.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [4]
In the citing paper
“Consider radiology, where AI assists radiologists in analyzing vast volumes of medical images where algorithms can quickly identify anomalies in x-rays or MRIs, flagging potential issues and requiring closer inspection [116].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [116]
In the citing paper
“In chatbot counselling, another use of artificial intelligence is that the dependence on mental healthcare chatbots does not have the reciprocity and transparency of treatment that should occur in the partnership between the mental healthcare patients and the healthcare professional”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Hosny et al. 2018
In the citing paper
“Computerized tomography (CT) and MRI scanning are researching AI in radiology to identify and treat illness in patients”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Hosny et al. 2018
In the citing paper
“While the possibility of AI replacing radiologists, and thereby threatening their professional identity, was extensively mentioned in many recent radiology publications [5, 9, 16, 44], none of the interviewees in this research identified with this threat.”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [16]
In the citing paper
“Literature has mainly blamed this on low technical performance of these early applications [5, 14–16], while other potential barriers to successful implementation, such as organizational or social aspects, have been largely ignored [17].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [16]
In the citing paper
“For instance, AI has been reported to improve the assessment of medical imaging to detect cases such as malignancy and diabetic retinopathy.30,31”
From Artificial intelligence: opportunities and implications for the health workforce (2020) · cites this paper as [30]
In the citing paper
“Deep learning algorithms have been widely applied in the field of image diagnosis and prediction owing to their advantages of being fast, accurate, and reproducible [15].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Self-citationShin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Research works printed but not linked: 10
- Long, J., Shelhamer, E. & Darrell, T. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3431–3440 (Boston, MA, USA, 2015).
- Sohn, K., Shang, W. & Lee, H. in Advances in Neural Information Processing Systems 27 (NIPS 2014) (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q.) 2141–2149 (Montreal, Canada, 2014).
- Pipatsrisawat, T., Gacic, A., Franchetti, F., Puschel, M. & Moura, J. M. F. in Proceedings. (ICASSP ‘05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 v/153–v/156 (Philadelphia, PA, USA, 2005).
- Ngiam, J. et al. in Proceedings of the 28th International Conference on Machine Learning 689–696 (Bellevue, WA, USA, 2011).
- Shin, H.-C. et al. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2497–2506 (Las Vegas, NV, USA, 2016).
- Goodfellow, I. et al. in Advances in Neural Information Processing Systems 27 (NIPS 2014) (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q.) 2672–2680 (Montreal, Canada, 2014).
- Zhang, P., Wang, F. & Zheng, Y. in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 578–582 (Melbourne, Australia, 2017).
- Ford, R. A., Price, W. & Nicholson, I. I. Privacy and accountability in black-box medicine. Mich. Telecomm. Tech. L. Rev. 23, 1 (2016).
- McMahan, H. B., Moore, E., Ramage, D., Hampson, S. & y Arcas, B. A. in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 1273–1282 (Fort Lauderdale, FL, USA, 2017).
- Bejnordi, B. E. et al. in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 929–932 (Melbourne, Australia, 2017).
Cited by
Sorted by citations
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
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
Self-citationAhmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019) · British Journal of Management · doi:10.1111/1467-8551.12343 · Cited by 835
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777
Harnessing multimodal data integration to advance precision oncology
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579
Self-citationIntegrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine
Adir, O., Poley, M., Chen, G., Froim, S., Krinsky, N., Shklover, J., Shainsky‐Roitman, J., Lammers, T., & Schroeder, A. (2020) · Advanced Materials · doi:10.1002/adma.201901989 · Cited by 496
Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479
Bhayana, R., Krishna, S., & Bleakney, R. R. (2023) · Radiology · doi:10.1148/radiol.230582 · Cited by 460
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446
Interpretability of machine learning-based prediction models in healthcare
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1379 · Cited by 541
Artificial Intelligence as Augmenting Automation: Implications for Employment
Tschang, F. T., & Almirall, E. (2021) · Academy of Management Perspectives · doi:10.5465/amp.2019.0062 · Cited by 409
Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning
Zhou, L.-Q., Wu, X.-L., Huang, S.-Y., Wu, G.-G., Ye, H.-R., Wei, Q., Bao, L.-Y., Deng, Y.-B., Li, X.-R., Cui, X.-W., et al. (2020) · Radiology · doi:10.1148/radiol.2019190372 · Cited by 384
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
Joshi, G., Jain, A., Araveeti, S. R., Adhikari, S., Garg, H., & Bhandari, M. (2024) · Electronics · doi:10.3390/electronics13030498 · Cited by 294
Schachner, T., Keller, R., & v Wangenheim, F. (2020) · Journal of Medical Internet Research · doi:10.2196/20701 · Cited by 267
Towards a new generation of artificial intelligence in China
Wu, F., Lu, C., Zhu, M., Chen, H., Zhu, J., Yu, K., Li, L., Li, M., Chen, Q., Li, X., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0183-4 · Cited by 287
High-throughput microbial culturomics using automation and machine learning
Huang, Y., Sheth, R. U., Zhao, S., Cohen, L. A., Dabaghi, K., Moody, T., Sun, Y., Ricaurte, D., Richardson, M., Velez-Cortes, F., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01674-2 · Cited by 283
Blockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281
Artificial intelligence in surgery
Varghese, C., Harrison, E. M., O’Grady, G., & Topol, E. J. (2024) · Nature Medicine · doi:10.1038/s41591-024-02970-3 · Cited by 287
A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
Mehmood, A., Maqsood, M., Bashir, M., & Shuyuan, Y. (2020) · Brain Sciences · doi:10.3390/brainsci10020084 · Cited by 264
Human–machine partnership with artificial intelligence for chest radiograph diagnosis
Patel, B. N., Rosenberg, L., Willcox, G., Baltaxe, D., Lyons, M., Irvin, J., Rajpurkar, P., Amrhein, T., Gupta, R., Halabi, S., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0189-7 · Cited by 254
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., & Gratacós, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-67076-5 · Cited by 240
Deep convolutional neural networks to predict cardiovascular risk from computed tomography
Zeleznik, R., Foldyna, B., Eslami, P., Weiss, J., Alexander, I., Taron, J., Parmar, C., Alvi, R. M., Banerji, D., Uno, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-20966-2 · Cited by 240
Self-citationBisdas, S., Topriceanu, C.-C., Zakrzewska, Z., Irimia, A.-V., Shakallis, L., Subhash, J., Casapu, M.-M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.795284 · Cited by 232
Understanding artificial intelligence based radiology studies: What is overfitting?
Mutasa, S., Sun, S., & Ha, R. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.04.025 · Cited by 216
Ahmed, Z., Bhinder, K. K., Tariq, A., Tahir, M. J., Mehmood, Q., Tabassum, M. S., Malik, M., Aslam, S., Asghar, M. S., & Yousaf, Z. (2022) · Annals of Medicine & Surgery · doi:10.1016/j.amsu.2022.103493 · Cited by 208
Cestonaro, C., Delicati, A., Marcante, B., Caenazzo, L., & Tozzo, P. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1305756 · Cited by 231
Artificial intelligence: opportunities and implications for the health workforce
Hazarika, I. (2020) · International Health · doi:10.1093/inthealth/ihaa007 · Cited by 203
Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100148 · Cited by 201
Resnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228
“AI systems also make errors in reading, classifying, and reconstructing radiological images, but the error rate is decreasing, and AI systems will soon outperform humans in terms of speed and accuracy of image reading [12, 17, 103, 228].”
Artificial intelligence in medical education - perception among medical students
Jackson, P., Ponath Sukumaran, G., Babu, C., Tony, M. C., Jack, D. S., Reshma, V. R., Davis, D., Kurian, N., & John, A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05760-0 · Cited by 185
Artificial intelligence in oncology: path to implementation
Chua, I. S., Gaziel‐Yablowitz, M., Korach, Z. T., Kehl, K. L., Levitan, N. A., Arriaga, Y. E., Jackson, G. P., Bates, D. W., & Hassett, M. (2021) · Cancer Medicine · doi:10.1002/cam4.3935 · Cited by 179
Gao, Y., Zeng, S., Xu, X., Li, H., Yao, S., Song, K., Li, X., Chen, L., Tang, J., Xing, H., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00278-8 · Cited by 185
Martín Noguerol, T., Paulano-Godino, F., Martín-Valdivia, M. T., Menias, C. O., & Luna, A. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.047 · Cited by 175
Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal
Xie, H. N., Wang, N., He, M., Zhang, L. H., Cai, H. M., Xian, J. B., Lin, M. F., Zheng, J., & Yang, Y. Z. (2020) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.21967 · Cited by 176
Interpretation and visualization techniques for deep learning models in medical imaging
Huff, D. T., Weisman, A. J., & Jeraj, R. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/abcd17 · Cited by 172
Ongena, Y. P., Haan, M., Yakar, D., & Kwee, T. C. (2020) · European Radiology · doi:10.1007/s00330-019-06486-0 · Cited by 161
Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024) · JMIR Medical Education · doi:10.2196/54793 · Cited by 165
Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review
Kenner, B., Chari, S. T., Kelsen, D., Klimstra, D. S., Pandol, S. J., Rosenthal, M., Rustgi, A. K., Taylor, J. A., Yala, A., Abul-Husn, N., et al. (2021) · Pancreas · doi:10.1097/mpa.0000000000001762 · Cited by 158
Self-citationEmerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19
Tsikala Vafea, M., Atalla, E., Georgakas, J., Shehadeh, F., Mylona, E. K., Kalligeros, M., & Mylonakis, E. (2020) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-020-00629-w · Cited by 153
Lennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N., & Pinto dos Santos, D. (2021) · Journal of Medical Internet Research · doi:10.2196/24221 · Cited by 153
Humar, P., Asaad, M., Bengur, F. B., & Nguyen, V. (2023) · Aesthetic Surgery Journal · doi:10.1093/asj/sjad130 · Cited by 155
AI in Medical Education: Global situation, effects and challenges
Zhang, W., Cai, M., Lee, H. J., Evans, R., Zhu, C., & Ming, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12009-8 · Cited by 148
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152
Jha, N., Shankar, P. R., Al-Betar, M. A., Mukhia, R., Hada, K., & Palaian, S. (2022) · Advances in Medical Education and Practice · doi:10.2147/amep.s368519 · Cited by 143
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
A bird's-eye view of deep learning in bioimage analysis
Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126
Jussupow, E., Spohrer, K., & Heinzl, A. (2022) · JMIR Formative Research · doi:10.2196/28750 · Cited by 138
Zhu, S., Gilbert, M., Chetty, I., & Siddiqui, F. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104828 · Cited by 131
An MRI deep learning model predicts outcome in rectal cancer
Jiang, X., Zhao, H., Saldanha, O. L., Nebelung, S., Kuhl, C., Amygdalos, I., Lang, S. A., Wu, X., Meng, X., Truhn, D., et al. (2023) · Radiology · doi:10.1148/radiol.222223 · Cited by 127
Abuzaid, M. M., Elshami, W., Tekin, H., & Issa, B. (2022) · Academic Radiology · doi:10.1016/j.acra.2020.09.014 · Cited by 120
McCoy, L. G., Brenna, C. T. A., Chen, S. S., Vold, K., & Das, S. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.11.001 · Cited by 118
Higgins, D., & Madai, V. I. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000052 · Cited by 118
Harvey, H. B., & Gowda, V. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.09.017 · Cited by 112
Hickman, S. E., Baxter, G. C., & Gilbert, F. J. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01333-w · Cited by 110
Luz, C. F., Vollmer, M., Decruyenaere, J., Nijsten, M. W., Glasner, C., & Sinha, B. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2020.02.003 · Cited by 106
Artificial Intelligence in Screening Mammography: A Population Survey of Women’s Preferences
Ongena, Y. P., Yakar, D., Haan, M., & Kwee, T. C. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.09.042 · Cited by 105
AI-RADS: an artificial intelligence curriculum for residents
Lindqwister, A. L., Hassanpour, S., Lewis, P. J., & Sin, J. M. (2021) · Academic Radiology · doi:10.1016/j.acra.2020.09.017 · Cited by 103
Lindvall, C., Deng, C.-Y., Moseley, E., Agaronnik, N., El-Jawahri, A., Paasche-Orlow, M. K., Lakin, J. R., Volandes, A., & Tulsky, T. A.-P. I. (2022) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2021.06.025 · Cited by 91
Macnamara, B. N., Berber, I., Çavuşoğlu, M. C., Krupinski, E. A., Nallapareddy, N., Nelson, N. E., Smith, P. J., Wilson-Delfosse, A. L., & Ray, S. (2024) · Cognitive Research: Principles and Implications · doi:10.1186/s41235-024-00572-8 · Cited by 97
Next-generation business models for artificial intelligence start-ups in the healthcare industry
Kulkov, I. (2023) · International Journal of Entrepreneurial Behavior & Research · doi:10.1108/ijebr-04-2021-0304 · Cited by 97
Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques
Kalra, N., Verma, P., & Verma, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108917 · Cited by 95
Patient perspectives on informed consent for medical AI: A web-based experiment
Park, H. J. (2024) · DIGITAL HEALTH · doi:10.1177/20552076241247938 · Cited by 95
Doumat, G., Daher, D., Ghanem, N.-N., & Khater, B. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1015418 · Cited by 91
Explainable AI and Multi-Modal Causability in Medicine
Holzinger, A. (2021) · i-com · doi:10.1515/icom-2020-0024 · Cited by 87
Chen, Y., Stavropoulou, C., Narasinkan, R., Baker, A., & Scarbrough, H. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-06861-y · Cited by 87
Cresswell, K., Callaghan, M., Khan, S., Sheikh, Z., Mozaffar, H., & Sheikh, A. (2020) · Health Informatics Journal · doi:10.1177/1460458219900452 · Cited by 87
Fraile Navarro, D., Ijaz, K., Rezazadegan, D., Rahimi-Ardabili, H., Dras, M., Coiera, E., & Berkovsky, S. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105122 · Cited by 82
Data leakage in deep learning studies of translational EEG
Brookshire, G., Kasper, J., Blauch, N. M., Wu, Y. C., Glatt, R., Merrill, D. A., Gerrol, S., Yoder, K. J., Quirk, C., & Lucero, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1373515 · Cited by 103
“Our Alzheimer’s CNN classifier appeared to have an accuracy of above 99% when using segment-based holdout, but its true performance on previously-unseen subjects was indistinguishable from chance.”
InfluentialLiu, M., Wu, J., Wang, N., Zhang, X., Bai, Y., Guo, J., Zhang, L., Liu, S., & Tao, K. (2023) · PLOS ONE · doi:10.1371/journal.pone.0273445 · Cited by 84
Ethical considerations for artificial intelligence: an overview of the current radiology landscape.
Department of Radiology and Nuclear Medicine, U. H. B., & Akinci D'Antonoli, T. (2020) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2020.19279 · Cited by 80
Gutierrez, G., Stephenson, C., Eadie, J., Asadpour, K., & Alavi, N. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1356773 · Cited by 71
Understanding Biases and Disparities in Radiology AI Datasets: A Review
Tripathi, S., Gabriel, K., Dheer, S., Parajuli, A., Augustin, A. I., Elahi, A., Awan, O., & Dako, F. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.06.015 · Cited by 75
Iacucci, M., Santacroce, G., Zammarchi, I., Maeda, Y., Del Amor, R., Meseguer, P., Kolawole, B. B., Chaudhari, U., Di Sabatino, A., Danese, S., et al. (2024) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(24)00053-0 · Cited by 73
Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review
Lawrence, R., Dodsworth, E., Massou, E., Sherlaw-Johnson, C., Ramsay, A. I. G., Walton, H., O'Regan, T., Gleeson, F., Crellin, N., Herbert, K., et al. (2025) · eClinicalMedicine · doi:10.1016/j.eclinm.2025.103228 · Cited by 69
Application of artificial intelligence in medical technologies: a systematic review of main trends.
Bitkina, O. V., Park, J., & Kim, H. K. (2023) · Digital Health · doi:10.1177/20552076231189331 · Cited by 66
Hanneman, K., Playford, D., Dey, D., van Assen, M., Mastrodicasa, D., Cook, T. S., Gichoya, J. W., Williamson, E. E., Rubin, G. D., & on behalf of the American Heart Association Council on Cardiovascular Radiology and Intervention; and Council on Lifelong Congenital Heart Disease and Heart Health in the Young (2024) · Circulation · doi:10.1161/cir.0000000000001202 · Cited by 72
Yeo, M., Tahayori, B., Kok, H. K., Maingard, J., Kutaiba, N., Russell, J., Thijs, V., Jhamb, A., Chandra, R. V., Brooks, M., et al. (2021) · Journal of NeuroInterventional Surgery · doi:10.1136/neurintsurg-2020-017099 · Cited by 65
Laupichler, M. C., Hadizadeh, D. R., Wintergerst, M. W. M., von der Emde, L., Paech, D., Dick, E. A., & Raupach, T. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03866-x · Cited by 62
A review on the recent applications of deep learning in predictive drug toxicological studies
Sinha, K., Ghosh, N., & Sil, P. C. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00375 · Cited by 64
Artificial Intelligence in Prenatal Ultrasound Diagnosis
He, F., Wang, Y., Xiu, Y., Zhang, Y., & Chen, L. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.729978 · Cited by 62
Regulatory Issues and Challenges to Artificial Intelligence Adoption
Harvey, H. B., & Gowda, V. (2021) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2021.07.007 · Cited by 59
Huang, H., Zheng, S., Yang, Z., Wu, Y., Li, Y., Qiu, J., Cheng, Y., Lin, P., Lin, Y., Guan, J., et al. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhac099 · Cited by 57
O'Reilly, D., McGrath, J., & Martin-Loeches, I. (2024) · Journal of Intensive Medicine · doi:10.1016/j.jointm.2023.10.001 · Cited by 55
Grøvik, E., Yi, D., Iv, M., Tong, E., Nilsen, L. B., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00398-4 · Cited by 51
Is it time to get rid of black boxes and cultivate trust in AI?
Gastounioti, A., & Kontos, D. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200088 · Cited by 47
Xiang, Y., Huang, C., He, Y., & Zhang, Q. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.644150 · Cited by 46
The Artificial intelligence large language models and neuropsychiatry practice and research ethic
Zhong, Y., Chen, Y.-J., Zhou, Y., Lyu, Y.-A.-H., Yin, J.-J., & Gao, Y.-J. (2023) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2023.103577 · Cited by 44
Wu, X., Zhang, S., Zhang, Z., He, Z., Xu, Z., Wang, W., Jin, Z., You, J., Guo, Y., Zhang, L., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00670-2 · Cited by 43
Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption
Morrison, K. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2020-0258 · Cited by 43
Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Vellvé, K., Eixarch, E., Crispi, F., Bonet-Carne, E., Bennasar, M., & Gratacos, E. (2021) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2021.100462 · Cited by 39
Huo, T., Li, L., Chen, X., Wang, Z., Zhang, X., Liu, S., Huang, J., Zhang, J., Yang, Q., Wu, W., et al. (2023) · Scientific Reports · doi:10.1038/s41598-022-26771-1 · Cited by 38
How to Design AI-Driven Clinical Trials in Nuclear Medicine
Delso, G., Cirillo, D., Kaggie, J. D., Valencia, A., Metser, U., & Veit-Haibach, P. (2021) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2020.09.003 · Cited by 36
Wenderott, K., Krups, J., Luetkens, J. A., & Weigl, M. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104243 · Cited by 33
Pfob, A., Sidey-Gibbons, C., Barr, R. G., Duda, V., Alwafai, Z., Balleyguier, C., Clevert, D.-A., Fastner, S., Gomez, C., Goncalo, M., et al. (2022) · European Radiology · doi:10.1007/s00330-021-08519-z · Cited by 29
Sharma, V., Saini, U., Pareek, V., Sharma, L., & Kumar, S. (2023) · Scripta Medica · doi:10.5937/scriptamed54-46267 · Cited by 27
Metadata
- Authors
- Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L.
- Affiliations
- Harvard University (US)
- Dana-Farber Cancer Institute (US)
- NewYork–Presbyterian Hospital (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Aerts, Hugo J. W. L.
- Venue
- Nature Reviews Cancer
- Published
- 2018-05-17
- Volume
- 18
- Issue
- 8
- Pages
- 500-510
- ISSN
- 1474-175X (ISSN-L)
- 1474-1768
- PMC
- PMC6268174
- OpenAlex
- W2803760365
- Cited by
- 3,803
- References
- 117
- Citations by year
- 2026: 454 · 2025: 677 · 2024: 693 · 2023: 602 · 2022: 483 · 2021: 449 · 2020: 303 · 2019: 127 · 2018: 15
- FWCI
- 26.15
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature reviews. Cancer (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (U01CA190234, U01CA151118, U24CA194354)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in radiology, deep learning, medical image analysis, convolutional neural networks, variational autoencoder, image recognition, cancer detection, computer-aided diagnosis, clinical implementation barriers
- MeSH terms
- Machine Learning, Deep Learning, Algorithms, Artificial Intelligence, Humans, Image Processing, Computer-Assisted, Neoplasms, Pattern Recognition, Automated, Radiology
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
