Collaboration between clinicians and vision–language models in radiology report generation
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Abstract
Abstract Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment. The worldwide shortage of radiologists, however, restricts access to expert care and imposes heavy workloads, contributing to avoidable errors in report delivery. While recent progress in automated report generation with vision-language models offers clear potential to ameliorate this situation, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of AI-generated reports. In this study, we build a state-of-the-art report generation system for chest radiographs, Flamingo-CXR, by fine-tuning a well-known vision-language foundation model on radiology data. To measure the quality of the AI-generated reports, we perform an expert evaluation, that is largest in scale and diversity to date, by engaging a group of 27 certified radiologists in the United States and India to provide detailed assessment of AI-generated and human written reports from an intensive care setting as well as an inpatient setting. We observe a wide distribution of preferences across the panel, ranging from full consensus to dissensus, across clinical settings and regions, with 55.4% of Flamingo-CXR intensive care reports evaluated to be preferable or equivalent to clinician reports, by half or more of the panel, rising to 77.7% for outpatient x-rays overall and to 94% for the subset of cases with no pertinent abnormal findings. For reports that contain errors we develop an assistive setting, the first demonstration of clinician-AI collaboration for radiology report composition, and we observe a synergistic improvement across all clinical settings. Altogether, these nuanced evaluations reveal disparities between the AI system and radiologists, identify areas for potential clinical utility and pave the way toward a collaborative system that enhances clinical accuracy of radiology reporting.
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Small multimodal models still have significant performance gaps compared to larger models.
“However, while small language models have shown success in text domains14–16, small multimodal models (SMMs) still have significant performance gaps compared to larger models6,10.”
From A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings (2025) · cites this paper as [10]
Cites
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works
(2004) · Cited by 9,718 · see citing works
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,281 · see citing works
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · see citing works
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293 · see citing works
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
Self-citationMIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0322-0 · Cited by 1,816 · see citing works
Toward expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734 · see citing works
Self-citationFlamingo: a Visual Language Model for Few-Shot Learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.14198 · Cited by 1,555 · see citing works
On Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.173 · Cited by 1,185 · see citing works
Generating Radiology Reports via Memory-driven Transformer
Chen, Z., Song, Y., Chang, T.-H., & Wan, X. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.112 · Cited by 693 · see citing works
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2306.00890 · Cited by 590 · cited 2× · see citing works
“Although we have witnessed initial signs of such possibilities in the recent work on interactive, multimodal medical AI 26,33,38 , there remains a considerable amount of progress to be made toward building a clinically useful writing assistant for radiology.”
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · neural information processing systems · doi:10.52202/075280-1240 · Cited by 590 · see citing works
Deep Multimodal Representation Learning: A Survey
Guo, W., Wang, J., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2916887 · Cited by 535 · see citing works
Towards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.14334 · Cited by 483 · see citing works
Self-citationTowards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI · doi:10.1056/aioa2300138 · Cited by 483 · see citing works
Self-citationThe 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 · cited 2× · see citing works
“Manual inspection unveils some examples in which nuanced clinical errors were detected in the human reports, but not in the corresponding AI-generated reports and vice versa (Meth-ods and Extended Data Tables 3 and 4).”
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works
Perceiver IO: A General Architecture for Structured Inputs & OutputsPreprint
Jaegle, A., Borgeaud, S., Alayrac, J.-B., Doersch, C., Ionescu, C., Ding, X. D., Koppula, S., Zoran, D., Brock, A., Shelhamer, E., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.14795 · Cited by 357 · see citing works
Towards Expert-Level Medical Question Answering with Large Language Models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.09617 · Cited by 338 · see citing works
Self-citationToward Best Practices in Radiology Reporting
Kahn, C. E., Langlotz, C. P., Burnside, E. S., Carrino, J. A., Channin, D. S., Hovsepian, D. M., & Rubin, D. L. (2009) · Radiology · doi:10.1148/radiol.2523081992 · Cited by 245 · see citing works
Seah, J. C. Y., Tang, C. H. M., Buchlak, Q. D., Holt, X., Wardman, J. B., Aimoldin, A., Esmaili, N., Ahmad, H. K., Pham, H. N., Lambert, J. F., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00106-0 · Cited by 233 · see citing works
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M., Castro, D. C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01442 · Cited by 227 · see citing works
Multimodal Foundation Models: From Specialists to General-Purpose Assistants
Li, C., Gan, Z., Yang, Z., Yang, J., Li, L., Wang, L., & Gao, J. (2024) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000110 · Cited by 225 · see citing works
Interactive and Explainable Region-guided Radiology Report Generation
Tanida, T., Müller, P., Kaissis, G. A., & Rueckert, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00718 · Cited by 219 · see citing works
Clinically Accurate Chest X-Ray Report GenerationPreprint
Liu, G., Hsu, T.-M. H., McDermott, M. B. A., Boag, W., Weng, W., Peter Szolovits, B., & Ghassemi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02633 · Cited by 212 · see citing works
R2GenGPT: Radiology Report Generation with frozen LLMs
Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100033 · Cited by 205 · see citing works
Evaluating progress in automatic chest X-ray radiology report generation
Yu, F., Endo, M., Krishnan, R., Pan, I., Tsai, A., Reis, E. P., Fonseca, E. K. U. N., Lee, H. M. H., Abad, Z. S. H., Ng, A. Y., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100802 · Cited by 200 · cited 7× · see citing works
“Previous works have approached this challenge by proposing automated metrics for evaluating the clinical quality of generated reports 18–21 but many limitations remain.”
InfluentialRadiologist shortage leaves patient care at risk, warns royal college
Rimmer, A. (2017) · BMJ · doi:10.1136/bmj.j4683 · Cited by 193 · see citing works
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.3386/w31422 · Cited by 161 · see citing works
Improving chest X-ray report generation by leveraging warm starting
Nicolson, A., Dowling, J., & Koopman, B. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102633 · Cited by 161 · see citing works
2020 ACR Data Science Institute Artificial Intelligence Survey
Allen, B., Agarwal, S., Coombs, L. P., Wald, C., & Dreyer, K. J. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2021.04.002 · Cited by 154 · see citing works
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.416 · Cited by 149 · see citing works
Ahn, J. S., Ebrahimian, S., McDermott, S., Lee, S., Naccarato, L., Di Capua, J. F., Wu, M. Y., Zhang, E. W., Muse, V., Miller, B., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.29289 · Cited by 138 · cited 1× · see citing works
“…published results on classification performance are unavailable, so we use labels for these findings that were collected in a separate study 45 from a group of 18 board-certified radiologists (American Board of Radiology) in the United States, and we use the corresponding consensus labels…”
Rajpurkar, P., O’Connell, C., Schechter, A., Asnani, N., Li, J., Kiani, A., Ball, R. L., Mendelson, M., Maartens, G., van Hoving, D. J., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00322-2 · Cited by 137 · see citing works
Dvijotham, K., Winkens, J., Barsbey, M., Ghaisas, S., Stanforth, R., Pawlowski, N., Strachan, P., Ahmed, Z., Azizi, S., Bachrach, Y., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02437-x · Cited by 136 · see citing works
Self-citationTurning a blind eye: the mobilization of radiology services in resource-poor regions
Maru, D., Schwarz, R. K., Andrews, J. R., Basu, S., Sharma, A., & Moore, C. L. (2010) · Globalization and Health · doi:10.1186/1744-8603-6-18 · Cited by 125 · see citing works
How to Create a Great Radiology Report
Hartung, M. P., Bickle, I. C., Gaillard, F., & Kanne, J. P. (2020) · Radiographics · doi:10.1148/rg.2020200020 · Cited by 118 · see citing works
LLaVA-MR: Large Language-and-Vision Assistant for Video Moment RetrievalPreprint
Li, J. (2024) · Qeios · doi:10.32388/vlxb6m · Cited by 114 · see citing works
Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department
Huang, J., Neill, L., Wittbrodt, M., Melnick, D., Klug, M., Thompson, M., Bailitz, J., Loftus, T., Malik, S., Phull, A., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.36100 · Cited by 113 · see citing works
Harvey, H. B., & Gowda, V. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.09.017 · Cited by 112 · see citing works
Med-Flamingo: a Multimodal Medical Few-shot LearnerPreprint
Moor, M., Huang, Q., Wu, S., Yasunaga, M., Zakka, C., Dalmia, Y., Reis, E. P., Rajpurkar, P., & Leskovec, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.15189 · Cited by 111 · see citing works
Commonly Missed Findings on Chest Radiographs
Gefter, W. B., Post, B. A., & Hatabu, H. (2022) · CHEST Journal · doi:10.1016/j.chest.2022.10.039 · Cited by 83 · see citing works
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
Yan, A., He, Z., Lü, X., Jiang, D., Chang, E., Gentili, A., McAuley, J., & Hsu, C. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.336 · Cited by 79 · see citing works
Nabulsi, Z., Sellergren, A., Jamshy, S., Lau, C. T., Santos, E. R. G., Kiraly, A. P., Ye, W., Yang, J., Pilgrim, R., Kazemzadeh, S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-93967-2 · Cited by 55 · see citing works
Self-citationMilam, M. E., & Koo, C. W. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.08.135 · Cited by 53 · see citing works
Baselines for Chest X-Ray Report Generation
Boag, W., Hsu, T.-M. H., McDermott, M. B. A., Berner, G., Alesentzer, E., & Peter Szolovits, B. (2019) · Cited by 44 · see citing works
RadGraph: Extracting Clinical Entities and Relations from Radiology ReportsPreprint
Jain, S., Agrawal, A., Saporta, A., Truong, S. Q., Nguyen Duong, D., Bui, T., Chambon, P., Lungren, M. P., Ng, A. Y., Langlotz, C. P., et al. (2021) · arXiv (Cornell University) · doi:10.13026/2f5n-pb92 · Cited by 36 · see citing works
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Pătrăucean, V., Smaira, L., Gupta, A., Recasens, A., Markeeva, L., Banarse, D., Koppula, S., Heyward, J., Malinowski, M., Yang, Y., et al. (2023) · neural information processing systems · doi:10.52202/075280-1852 · Cited by 34 · see citing works
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Pătrăucean, V., Smaira, L., Gupta, A., Continente, A. R., Markeeva, L., Banarse, D., Koppula, S., Heyward, J., Malinowski, M., Yang, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.13786 · Cited by 34
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
Jain, S., Agrawal, A., Saporta, A., Truong, S. Q., Nguyen Duong, D., Bui, T., Chambon, P., Lungren, M., Ng, A., Langlotz, C., et al. (2021) · PhysioNet · doi:10.13026/hm87-5p47 · Cited by 34 · see citing works
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray InterpretationPreprint
Chen, Z., Varma, M., Xu, J., Paschali, M., Veen, D. V., Johnston, A., Youssef, A., Blankemeier, L., Blüthgen, C., Altmayer, S., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.12208 · Cited by 26 · see citing works
Multimodal Foundation Models: From Specialists to General-Purpose AssistantsPreprint
Li, C., Gan, Z., Yang, Z., Yang, J., Li, L., Wang, L., & Gao, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.10020 · Cited by 24 · see citing works
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
Yan, B., Liu, R., Kuo, D., Adithan, S., Reis, E., Kwak, S., Venugopal, V., O’Connell, C., Saenz, A., Rajpurkar, P., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.977 · Cited by 22 · see citing works
Ramesh, V., Chi, N. A., & Rajpurkar, P. (2022) · arXiv · doi:10.48550/arxiv.2210.06340 · Cited by 17 · see citing works
RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information ExtractionPreprint
Khanna, S., Dejl, A., Yoon, K., Truong, Q. H., Duong, H., Saenz, A. D., & Rajpurkar, P. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.05046 · Cited by 3 · see citing works
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Karunanayake, N. (2025) · Informatics and Health · doi:10.1016/j.infoh.2025.03.001 · Cited by 165 · see citing works
Multimodal generative AI for medical image interpretation
Rao, V. M., Hla, M., Moor, M., Adithan, S., Kwak, S., Topol, E. J., & Rajpurkar, P. (2025) · Nature · doi:10.1038/s41586-025-08675-y · Cited by 128 · see citing works
A comprehensive survey of large language models and multimodal large language models in medicine
Xiao, H., Zhou, F., Liu, X., Liu, T., Ли, Ж., Liu, X., & Huang, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102888 · Cited by 130 · see citing works
Multimodal Large Language Models in Medical Imaging: Current State and Future Directions
Nam, Y., Kim, D., Kyung, S., Seo, J., Song, J. M., Kwon, J., Kim, J., Jo, W., Park, H. J., Sung, J., et al. (2025) · Korean Journal of Radiology · doi:10.3348/kjr.2025.0599 · Cited by 106 · see citing works
An eyecare foundation model for clinical assistance: a randomized controlled trial
Wu, Y.-L., Qian, B., Li, T., Qin, Y., Guan, Z., Chen, T., Jia, Y., Zhang, P., Zeng, D., Moroi, S. E., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03900-7 · Cited by 60 · see citing works
Efficiency and Quality of Generative AI–Assisted Radiograph Reporting
Huang, J., Wittbrodt, M. T., Teague, C. N., Karl, E., Galal, G., Thompson, M., Chapa, A. R., Chiu, M.-L., Herynk, B. A., Linchangco, R., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.13921 · Cited by 54 · see citing works
Zambrano Chaves, J. M., Huang, S.-C., Xu, Y., Xu, H., Usuyama, N., Zhang, S., Wang, F., Xie, Y., Khademi, M., Yang, Z., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58344-x · Cited by 45 · see citing works
“Concurrently, Flamingo-CXR fi ne-tuned the Flamingovisionlanguagemodel 47 andincorporatedregularizationand adaptation techniques to tailor their applications to the nuances of radiology report generation 10 .”
Benchmarking the diagnostic performance of open source LLMs in 1933 Eurorad case reports
Kim, S. H., Schramm, S., Adams, L. C., Braren, R., Bressem, K. K., Keicher, M., Platzek, P., Paprottka, K. J., Zimmer, C., Hedderich, D. M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01488-3 · Cited by 41 · see citing works
Mitigating the risk of health inequity exacerbated by large language models
Ji, Y., Ma, W., Sivarajkumar, S., Zhang, H., Sadhu, E. M., Li, Z., Wu, X., Visweswaran, S., & Wang, Y. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01576-4 · Cited by 42 · see citing works
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Yuan, W., Chen, G., Wang, Z., & You, F. (2025) · Advanced Materials · doi:10.1002/adma.202502771 · Cited by 34 · see citing works
Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging
Pallumeera, M., Giang, J. C., Singh, R., Pracha, N. S., & Makary, M. S. (2025) · Cancers · doi:10.3390/cancers17091510 · Cited by 29 · see citing works
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Arora, R. K., Wei, J., Hicks, R. S., Bowman, P., Quiñonero-Candela, J., Tsimpourlas, F., Sharman, M., Shah, M., Vallone, A., Beutel, A., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.08775 · Cited by 28 · see citing works
“…generating differential diagnoses (Kanjee et al., 2023; McDuff et al., 2025), assisting with clinical documentation (Williams et al., 2024; Hartman et al., 2024; Baker et al., 2024), mental health treatment (Heinz et al., 2025), and radiology report generation (Tu et al., 2024; Tanno et al., 2025).”
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Singh, P., & Singh, S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1535168 · Cited by 26 · 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
Advancements in Radiology Report Generation: A Comprehensive Analysis
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Wihl, J., Rosenkranz, E., Schramm, S., Berberich, C., Griessmair, M., Woźnicki, P., Pinto, F., Ziegelmayer, S., Adams, L. C., Bressem, K. K., et al. (2025) · European Radiology Experimental · doi:10.1186/s41747-025-00600-2 · Cited by 18 · see citing works
Schneider‐Kamp, A., & Godono, A. (2025) · AI & Society · doi:10.1007/s00146-025-02319-5 · Cited by 16 · see citing works
Rethinking radiology reports: A survey of referring physicians’ perspectives
Reschke, P., Booz, C., Gruenewald, L. D., Koch, V., Höhne, E., Gökduman, A., Eichler, K., Schlüchtermann, J., Vogl, T. J., & Gotta, J. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112068 · Cited by 14 · see citing works
Benchmarking vision-language models for diagnostics in emergency and critical care settings
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Application of Artificial Intelligence in Medical Imaging: Current Status and Future Directions
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Understanding the robustness of vision-language models to medical image artefacts
Cheng, Z., Ong, A. Y., Wagner, S. K., Merle, D. A., Ju, L., Zhang, H., Chen, R., Pang, L., Li, B., He, T., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02108-w · Cited by 14 · see citing works
Dutta, N., Bose, K., Syailendra, E. A., Chu, L. C. H., & Gupta, P. K. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106227 · Cited by 13 · see citing works
Li, J., Zhou, Z.-C., Wang, Z.-C., & Lv, H. (2026) · Military Medical Research · doi:10.1186/s40779-026-00684-w · Cited by 13 · see citing works
Hallucinations in medical devices
Granstedt, J., Prabhat, K. C., Deshpande, R., Garcia, V. L., & Badano, A. G. (2025) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2025.100145 · Cited by 15 · see citing works
Kuo, S.-M., Tai, S.-K., Lin, H., & Chen, R.-C. (2025) · AI · doi:10.3390/ai6080188 · Cited by 13 · see citing works
Understanding pre-training data effects in retinal foundation models using two large fundus cohorts
Zhou, Y., Wang, Z., Wu, Y., Ong, A. Y., Wagner, S. K., Ruffell, E., Chia, M. A., Guan, Z., Ju, L., Engelmann, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-70077-z · Cited by 14 · see citing works
Pawelczyk, J., Kraus, M., Voigtlaender, S., Siebenlist, S., & Rupp, M. (2025) · HSS Journal® The Musculoskeletal Journal of Hospital for Special Surgery · doi:10.1177/15563316251341321 · Cited by 10 · see citing works
Liu, Y., Liu, F., Tu, S., Liu, S., & Han, B. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104975 · Cited by 9 · see citing works
Self-citationPreliminary evaluation of ChatGPT model iterations in emergency department diagnostics
Wang, J., Shue, K., Liu, L., & Hu, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-95233-1 · Cited by 9 · see citing works
Comparing the accuracy of large language models and prompt engineering in diagnosing realworld cases
Yao, G., Zhang, W. D., Zhu, Y., Wong, U.-K., Zhang, Y., Cui, Y., Shen, G., Li, Z., & Gao, H. Y. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106026 · Cited by 10 · see citing works
Xiong, Y., Lian, W., Sun, Y., Liu, W., Guo, J., Tang, W., & Liu, C. (2025) · Clinical Oral Investigations · doi:10.1007/s00784-025-06498-9 · Cited by 9 · see citing works
Foundation Models Meet Medical Image Interpretation
Jiao, L., Hao, J., Li, R., Li, L., Liu, X., Liu, F., Ma, W., Chen, P., Huang, Z., Yang, J., et al. (2026) · Research · doi:10.34133/research.1024 · Cited by 9 · see citing works
Al-Jebrni, A. H., Jiang, Z., Qin, Y., Qiu, Y., Jiang, N., Li, G., Zheng, Y., Wong, T. Y., Kim, S., Jung, Y., et al. (2026) · Computational Visual Media · doi:10.26599/cvm.2026.9450530 · Cited by 9 · see citing works
Employing large language models safely and effectively as a practicing neurosurgeon
Patil, A., Serrato, P., Cleaver, G., Limbania, D., See, A. P., & Huang, K. T. (2025) · Acta Neurochirurgica · doi:10.1007/s00701-025-06515-6 · Cited by 8 · see citing works
Self-citationHamamoto, R., Koyama, T., Takahashi, S., Yasuda, T., Kobayashi, K., Akagi, Y., Kouno, N., Sudo, K., Hirata, M., Sunami, K., et al. (2026) · Journal of Hematology & Oncology · doi:10.1186/s13045-026-01781-y · Cited by 8 · see citing works
Zhong, W., Sun, M.-Y., Yao, S., Liu, Y., Peng, D., Liu, Y., Yang, K., Gao, H., Yan, H., Hao, W., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/73233 · Cited by 7 · see citing works
Self-citationC2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network
Wang, R., Xu, Z., Wang, X., Liu, W., & Lukasiewicz, T. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103442 · Cited by 7 · see citing works
Zhong, Z., Wang, Y., Bi, L., Ma, Z., Ahn, S. H., Mullin, C. J., Greineder, C. F., Atalay, M. K., Collins, S., Baird, G. L., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103786 · Cited by 7 · see citing works
Jeong, W. G., Hwang, E. J., Jin, G. Y., Lee, J. H., Kang, S. R., Ko, H., Gil, B. M., Kim, J. H., Kim, T. J., Park, C. H., et al. (2025) · Korean Journal of Radiology · doi:10.3348/kjr.2025.0457 · Cited by 7 · see citing works
Chang, L., Li, H., Wu, W., Liu, X., Yan, J., Chen, Z., Wu, H., & Song, S. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07591-z · Cited by 10 · see citing works
Perspective: AI productivity will not benefit employed radiologists
Ruthven, H., & Agten, C. A. (2025) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2025.100033 · Cited by 7 · see citing works
Causal insights from clinical information in radiology: Enhancing future multimodal AI development
Jantscher, M., Gunzer, F., Reishofer, G., & Kern, R. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108810 · Cited by 7 · see citing works
He, Z., Wong, A. N. N., & Yoo, J. S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110625 · Cited by 7 · see citing works
A survey on electronic health record driven multimodal representation learning
Dou, Y., Liu, Y., Zou, H., Zeng, W., Xu, K., & Peng, S. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103810 · Cited by 7 · see citing works
Self-citationJin, K., Sun, Q., Kang, D., Luo, Z., Yu, T., Han, W., Zhang, Y., Wang, M., Shi, D., & Grzybowski, A. E. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02300-y · Cited by 6 · see citing works
Jiang, R., Chen, B., Dong, Z., Zeng, X., You, H., Li, Y., Deng, Y., Mu, G., Wang, J., Huang, L., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02569-7 · Cited by 6 · see citing works
Wang, R., Wang, Z., Xiao, Y., Liu, X., Tan, G., & Liu, J. (2025) · Meta-Radiology · doi:10.1016/j.metrad.2025.100138 · Cited by 6 · see citing works
Guan, H., Hou, P. C., Hong, P., Wang, L., Zhang, W., Du, X., Zhou, Z., & Zhou, L. (2025) · medRxiv · doi:10.1101/2025.07.13.25331222 · Cited by 6 · see citing works
Neuwieser, H., Jami, N. V. S. J., Meier, R. J., Liebsch, G., Felthaus, O., Klein, S., Schreml, S., Berneburg, M., Prantl, L., Leutheuser, H., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15172184 · Cited by 6 · see citing works
Zanardo, M., Albano, D., Molinari, V., Fabrizio, R., Conca, M., Asmundo, L., Pardo, F., Traina, F., Montechiari, M., Gitto, S., et al. (2026) · European Radiology Experimental · doi:10.1186/s41747-026-00682-6 · Cited by 6 · see citing works
A deep learning vision–language model for diagnosing pediatric dental diseases
Pham, T. D. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100364 · Cited by 5 · see citing works
Gertz, R. J., Beste, N. C., Dratsch, T., Lennartz, S., Bremm, J., Iuga, A.-I., Bunck, A. C., Laukamp, K. R., Schönfeld, M. H., & Kottlors, J. (2025) · European Radiology · doi:10.1007/s00330-025-11929-y · Cited by 5 · see citing works
Performance of o1 pro and GPT-4 in Self-Assessment Questions for Nephrology Board Renewal
Noda, R., Yuasa, C., Kitano, F., Ichikawa, D., & Shibagaki, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1702668 · Cited by 5 · see citing works
Prompt injection attacks on vision-language models for surgical decision support
Zhang, Z., Qadir, M. I., Carstens, M., Zhang, E. H., Loiselle, M. S., Martinus, F. M., Mroczkowski, M. K., Clusmann, J., Kather, J. N., & Kolbinger, F. R. (2026) · npj Digital Surgery · doi:10.1038/s44484-026-00014-6 · Cited by 4 · see citing works
Ren, G., Hou, M., Yang, X., Huang, Y., Li, D., Zhang, N., Liu, P., Ye, H., Chen, Z., & Chen, H. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02044-9 · Cited by 4 · see citing works
Chen, C.-H., Chen, C.-W., Hsieh, K.-Y., Huang, K.-E., & Lai, H.-Y. (2026) · Diagnostics · doi:10.3390/diagnostics16030376 · Cited by 5 · 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
Progress in the application of machine learning in CT diagnosis of acute appendicitis
Li, J., Ye, J., Luo, Y., Xu, T., & Jia, Z. (2025) · Abdominal Radiology · doi:10.1007/s00261-025-04864-5 · Cited by 4 · see citing works
From task-specific to foundation models: A paradigm shift in medical vision-language analysis
Ali, M. U., Zafar, A., Kim, S., Kim, K. S., & Lee, S. W. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100831 · Cited by 5 · see citing works
Li, Z., Gao, Y., Ji, K., Zhang, K.-Y., Zhang, Q., Wang, J., Han, L., Zhai, X.-Y., Wang, W., Liu, W.-L., et al. (2025) · European Radiology · doi:10.1007/s00330-025-12065-3 · Cited by 3 · see citing works
Vaishya, R., Gupta, B. M., Chaman Sab, M., Guechairi, S., Vaish, A., & Botchu, R. (2025) · Journal of Clinical Imaging Science · doi:10.25259/jcis_245_2025 · Cited by 3 · see citing works
Automatic medical report generation: a comprehensive review of methodologies and applications
Yan, T., He, H. W., Chan, I. N., Qin, Y. Y., Li, Z., & Wong, P. K. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3474 · Cited by 3 · see citing works
Song, X., Li, Y., Ouyang, H. J., Yao, M., Zhao, F., Yu, K., Tian, S., Xing, X., Zhou, Y., Zhang, E., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69023-w · Cited by 3 · see citing works
Sarwal, R. D., Tarca, V., Dubin, C. A., Kalavros, N., Bhatti, G., Bhattacharya, S., Butte, A. J., Romero, R., Stolovitzky, G. A., Oskotsky, T., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102594 · Cited by 3 · see citing works
Learning with less supervision: A survey of label-efficient learning for medical image analysis
Jin, C., Guo, Z., Lin, Y., Luo, L., & Chen, H. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104062 · Cited by 31 · see citing works
Park, Y. W., Kang, M., Ryu, H., Han, K., Sim, Y., Park, J. E., Chang, J. H., Kim, S. H., Lee, S.-K., Park, S. H., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02581-x · Cited by 3 · see citing works
Farahani, S., Ieva, A. D., Coiera, E. W., & Liu, S. (2026) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2026.100092 · Cited by 3 · see citing works
Tracing the Pen: Electronic Health Records Amid the Rise of Generative AI
Nargesi, A. A., You, J. G.-T., Bitterman, D. S., Succi, M. D., Mishuris, R. G., Poon, E. G., & Landman, A. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02508-6 · Cited by 3 · see citing works
DxDirector: an agentic large language model driving the full-process clinical diagnosis
Xu, S., Huang, X., Wei, Z., Pang, L., Shen, H., & Cheng, X. (2026) · Nature Communications · doi:10.1038/s41467-026-71928-5 · Cited by 4 · see citing works
Multi-Modality and Multi-Grained Transformer for Accurate Radiology Report Generation
Li, H., Zhang, L., Zhong, X., Zhang, J., Feng, S., & Li, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0033-8_8 · Cited by 3 · see citing works
Mensah, P. B., Quao, N. S. A., & Dagadu, S. (2024) · medRxiv · doi:10.1101/2024.04.17.24305971 · Cited by 3 · see citing works
Artificial Intelligence in Healthcare: 2025 Year in ReviewPreprint
Edara, S. R., Khare, A., Atreja, A., Awasthi, R., Highum, B., Hakimzadeh, N., Ramachandran, S. P., Mishra, S., Mahapatra, D., Shree, S., et al. (2026) · medRxiv · doi:10.64898/2026.02.23.26346888 · Cited by 3 · see citing works
Zając, H. D., & Ternov, N. K. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719979 · Cited by 2 · see citing works
Kim, C., Cho, S., & Yoon, J. H. (2025) · Healthcare Informatics Research · doi:10.4258/hir.2025.31.4.416 · Cited by 2 · see citing works
Uppuluri, P. C., & Yang, C. W. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.10.063 · Cited by 2 · see citing works
Generative AI and Scientific Authorship
Hsieh, S.-C., Lee, K.-H., Karmakar, R., Kandalkar, A., Mukundan, A., & Wang, H. (2025) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.394323 · Cited by 2 · see citing works
Recent Progress in Deep Learning for Chest X-Ray Report Generation
Salhi, M., & Akhloufi, M. A. (2026) · BioMedInformatics · doi:10.3390/biomedinformatics6010003 · Cited by 2 · see citing works
Text-image alignment for ILD imaging: linking CXR evidence to CT quantification
Gao, J., Ren, Y., Yang, F., Hu, X., Changbo, S. U. N., Wang, S., & Chen, C. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02292-9 · Cited by 2 · see citing works
Medical multimodal large language models: A survey
Xiao, H., Hui, N., Xu, Y., Li, Z., & Peng, J. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104386 · Cited by 2 · see citing works
Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging
Nia, N. G., Manwar, R., & Avanaki, K. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04241-3 · Cited by 2 · see citing works
Generative Artificial Intelligence and Large Language Models in Clinical Oncology
Yu, Y., Zhao, Z., Wang, Z., Lin, R., Tan, Y., Chen, Y., Li, T., Baptista‐Hon, D. T., Zhang, X., Wu, C., et al. (2026) · MedComm · doi:10.1002/mco2.70833 · Cited by 2 · see citing works
A Unified Platform for Radiology Report Generation and Clinician-Centered AI EvaluationPreprint
Ma, Z., Yang, X., Atalay, Z., Yang, A. E., Collins, S., Bai, H. X., Bernstein, M. H., Baird, G. L., & Jiao, Z. (2025) · medRxiv · doi:10.1101/2025.07.07.25331018 · Cited by 2 · see citing works
Sun, X., Hong, Q., Zhang, M., Li, Y., Chen, T., Huang, Z., Liang, G., Tang, W., Xu, S., Ni, X., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102547 · Cited by 1 · see citing works
“Future research should establish robust human-AI collaboration frameworks 35,36 to validate MCC’s performance in real clinical settings.”
Allaberdiev, S., Chen, X., Mamatov, D., Abdusalomov, A., Ubaydullaev, U., & Khan, A. (2026) · IET Software · doi:10.1049/sfw2/5529107 · Cited by 1 · see citing works
Hood, D. C., Lau, W. T., Stowman, A. L., Gebhardt, T., Mao, G., Bruna, S. L., Leshno, A., Tsamis, E., & Thakoor, K. A. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.10.23 · Cited by 1 · see citing works
Arteaga-Arteaga, H. B., Giraldo-Arias, J. A., Bravo-Ortíz, M. A., Landín, C. J., Guillen-Rondon, P., & Tabares-Soto, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130196 · Cited by 1 · see citing works
CHEF-VL: Detecting Cognitive Sequencing Errors in Cooking with Vision-language Models
Wang, R., Gao, P., Lynch, P., Liu, T., Lee, Y., Baum, C. M., Connor, L. T., & Lu, C. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3770714 · Cited by 1 · see citing works
Haghighi, T., Gholami, S., Sokol, J. T., Biswas, A., Lim, J. I., Leng, T., Thompson, A. C., Tabkhi, H., & Alam, M. N. (2025) · Communications Medicine · doi:10.1038/s43856-025-01293-9 · Cited by 1 · see citing works
Jeong, W. G., Hwang, E. J., & Jin, G. Y. (2026) · Korean Journal of Radiology · doi:10.3348/kjr.2025.1918 · Cited by 1 · see citing works
FiR-Rad: Fine-Grained Reinforcement With Structured Reasoning for Chest X-Ray Report Generation
Mei, X., Yang, L., Gao, D., Cai, X., Han, J., & Liu, T. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3665561 · Cited by 2 · see citing works
Artificial Intelligence in Point-of-Care Ultrasound
Wu, D., Arntfield, R. T., & Millington, S. J. (2026) · Journal of Intensive Care Medicine · doi:10.1177/08850666261427329 · Cited by 1 · see citing works
Clinical usability of generative artificial intelligence for MR safety advice
Rose, H., Thorpe, J. C., Panek, R., Goncalves, E., & Morgan, P. S. (2026) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2026.100083 · Cited by 1 · see citing works
Med-ViX-Ray: Enhancing explainable chest X-ray analysis with clinical knowledge graphs
Cieri, M., & Palomba, F. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109313 · Cited by 1 · see citing works
Hao, P., Zhang, J., Zhang, S., Wu, F., & Tian, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-41439-w · Cited by 1 · see citing works
Meta-Entity Driven Triplet Mining for Aligning Medical Vision-Language Models
Yilmaz, M., Öztürk, Ş., Kara, M., Yavuz, M. T., Gümeler, E., Koç, A., & Çukur, T. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3678306 · Cited by 2 · see citing works
Barzegar, M., Rostami, H., Sanati, A., Afshoon, R., Kosari, A., & Keshavarz, A. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110173 · Cited by 2 · see citing works
Prajapati, V., Kolhar, S., & Barbole, D. K. (2026) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2026.101777 · Cited by 1 · see citing works
Zhu, Q., Lin, Y., Fu, W., Tang, W. H., Li, J., Zhang, Y., Li, B., Guo, X., Wang, F. S., Qi, H., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01713-8 · Cited by 2 · see citing works
A roadmap for medical large language models: a review of foundations, applications, and challenges
Sha, Y.-Y., Yu, L., Lin, Z.-H., Kaur, A., Lou, Y., Gornale, S. S., Zhang, T., Wong, L. S., Wang, Z., Yan, Y., et al. (2026) · Military Medical Research · doi:10.1016/j.mmr.2026.100050 · Cited by 2 · see citing works
- Authors
- Tanno, R., Barrett, D. G. T., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Lau, C., Tu, T., Azizi, S., Singhal, K., Schaekermann, M., May, R., Lee, R., Man, S., Mahdavi, S., Ahmed, Z., Matias, Y., Barral, J., Eslami, S. M. A., Belgrave, D., Liu, Y., Kalidindi, S. R., Shetty, S., Natarajan, V., Kohli, P., Huang, P.-S., Karthikesalingam, A., & Ktena, I.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Google (United Kingdom) (GB)
- GlaxoSmithKline (United Kingdom) (GB)
- Apollo Hospitals (IN)
- Corresponding author
- Tanno, Ryutaro
- Barrett, David G. T.
- Karthikesalingam, Alan
- ORCID
- Tanno, Ryutaro
- Barrett, David G. T.
- Sellergren, Andrew
- Ghaisas, Sumedh
- Dathathri, Sumanth
- See, Abigail
- Lau, Charles
- Tu, Tao
- Azizi, Shekoofeh
- Singhal, Karan
- Schaekermann, Mike
- Lee, Roy
- Man, SiWai
- Mahdavi, Sara
- Matias, Yossi
- Barral, Joelle
- Eslami, S. M. Ali
- Liu, Yun
- Kalidindi, Sreenivasa Raju
- Shetty, Shravya
- Natarajan, Vivek
- Kohli, Pushmeet
- Huang, Po-Sen
- Karthikesalingam, Alan
- Ktena, Ira
- Venue
- Nature Medicine
- Published
- 2024-11-07
- Volume
- 31
- Issue
- 2
- Pages
- 599-608
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11835717
- OpenAlex
- W4404143589
- Cited by
- 186
- References
- 54
- Citations by year
- 2026: 106 · 2025: 77 · 2024: 2 · 2023: 1
- FWCI
- 17.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- DeepMind
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- automated radiology report generation, vision-language models, chest radiograph, clinician-AI collaboration, radiologist workload
- MeSH terms
- Radiologists, Artificial Intelligence, Humans, Radiology, Radiography, Thoracic
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.
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