Artificial intelligence-generated preliminary reports from a radiologist's perspective - A systematic review.

Dr. Priyadarshini  Subramani; Dr. Karthik  Shunmugavelu

doi:10.51168/sjhrafrica.v3i12.2460

Authors

Dr. Priyadarshini Subramani Assistant Professor, Department of Radiology, PSP Medical College Hospital and Research Institute, Tambaram, Kanchipuram main road, Oragadam, Panruti Kanchipuram district, Tamilnadu 631604 India
Dr. Karthik Shunmugavelu Assistant Professor, Department of Dentistry, PSP Medical College Hospital and Research Institute Tambaram Kanchipuram main road Oragadam Panruti Kanchipuram district Tamilnadu 631604 India

DOI:

https://doi.org/10.51168/sjhrafrica.v3i12.2460

Keywords:

Artificial intelligence, radiology, preliminary report, structured report, human–AI collaboration, workflow efficiency, diagnostic accuracy

Abstract

Background

Artificial intelligence (AI) is increasingly integrated into radiology to generate preliminary reports. While AI promises to improve efficiency and standardization, its impact on diagnostic accuracy and radiologist workflow requires further investigation.

Objective: To systematically review evidence on radiologist interaction with AI-generated radiology reports, evaluating effects on reporting efficiency, diagnostic accuracy, report quality, radiologist confidence, and patient comprehension.

Methods

A systematic literature search of PubMed, MEDLINE, Embase, Scopus, and Web of Science was conducted for studies published between January 2016 and March 2025. Eligible studies included original research and reviews assessing AI-generated preliminary or structured radiology reports with radiologist involvement. Data were extracted on study characteristics, imaging modalities, AI system type, and key outcomes. Narrative synthesis was performed due to heterogeneity in study designs.

Results

Eight studies met the inclusion criteria, encompassing imaging modalities such as chest radiographs, MRI, CT, and spine imaging. Across studies, AI-assisted reporting consistently improved efficiency, with reductions in reporting time ranging from 15% to over 40%. Diagnostic accuracy and report quality were generally maintained, although variability was noted in complex or abnormal cases. Radiologists' confidence and experience were enhanced when interacting with AI-generated reports. Structured reporting and patient-friendly summaries further improved standardization and patient comprehension. Limitations included occasional AI misinterpretations, ethical and regulatory concerns, and the continued need for human oversight, particularly for complex imaging findings.

Conclusions

AI-generated radiology reports offer significant benefits in efficiency, standardization, and patient-centered communication while preserving diagnostic accuracy when combined with expert radiologist review. Human–AI collaboration emerges as the most effective model.

Future research

Future research should focus on optimizing AI performance for complex cases, validating patient-centered reporting, and establishing robust clinical and ethical frameworks for AI integration.

Author Biographies

Dr. Priyadarshini Subramani, Assistant Professor, Department of Radiology, PSP Medical College Hospital and Research Institute, Tambaram, Kanchipuram main road, Oragadam, Panruti Kanchipuram district, Tamilnadu 631604 India

MD, Radiology , Dr. Priyadarshini Subramani is an Assistant Professor in the Department of Radiology at PSP Medical College Hospital and Research Institute, Tamilnadu, India. She completed her MD in Radiology and has clinical and academic expertise in diagnostic imaging, with research interests in artificial intelligence applications in radiology, musculoskeletal imaging, and neuroradiology. She is actively involved in postgraduate teaching and curriculum development in radiology education.

Dr. Karthik Shunmugavelu, Assistant Professor, Department of Dentistry, PSP Medical College Hospital and Research Institute Tambaram Kanchipuram main road Oragadam Panruti Kanchipuram district Tamilnadu 631604 India

BDS, MDS OMFP, MSC LONDON, MFDSRCS ENGLAND, MFDSRCPS GLASGOW, FACULTY AFFILIATE RCS IRELAND, AFFILIATE RCS EDINBURGH, ASSOCIATE FACULTY OF DENTAL TRAINERS EDINBURGH, MCIP, FIBMS USA, MASID AUSTRALIA

Dr. Karthik Shunmugavelu is a Senior Resident in the Department of Dentistry at PSP Medical College Hospital and Research Institute, Tamilnadu, India. He holds multiple international qualifications in oral medicine and facial pathology, including an MSc from the University of London and fellowships from the Royal College of Surgeons of England, Glasgow, and Edinburgh. His research interests include artificial intelligence in healthcare, medical education, and interdisciplinary collaboration between radiology and dentistry. He has authored several publications in peer-reviewed journals and serves as a faculty affiliate for multiple international surgical colleges.

References

Gorelik N, Gyftopoulos S. Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report. Canadian Association of Radiologists Journal. 2020;72(1):45-59. https://doi.org/10.1177/0846537120947148 PMid:32809857

Al-Naser Y, Sharma S, Niure K, Ibach K, Khosa F, Yong-Hing CJ. Geographic prompting and content fidelity in generative Artificial Intelligence: A multi-model study of demographics and imaging equipment in AI-generated videos and images of Canadian medical radiation technologists. Journal of Medical Imaging and Radiation Sciences. 2025 Dec 1;56(6):102122.https://doi.org/10.1016/j.jmir.2025.102122 PMid:41110417

Archer H, Reine S, Alshaikhsalama A, Wells J, Kohli A, Vazquez L, et al. Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia. Bone Jt Open. 2022 Nov 14;3(11):877-884. https://doi.org/10.1302/2633-1462.311.BJO-2022-0125.R1 PMid:36373773 PMCid:PMC9709495

Zhang S, Sun J, Liu C, Fang J, Xie H, Ning B. Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip. Bone Joint J. 2020 Nov 2;102-B(11):1574-1581. https://doi.org/10.1302/0301-620X.102B11.BJJ-2020-0712.R2 PMid:33135455

Hong, E. K., Suh, C. H., Nukala, M., Esfahani, A., Licaros, A., Madan, R., Hunsaker, A., & Hammer, M. (2026). Radiologist Interaction with Artificial Intelligence-Generated Preliminary Reports: A Longitudinal Multireader Study. Journal of the American College of Radiology: JACR, 23(2), 292-298. https://doi.org/10.1016/j.jacr.2025.09.015 PMid:40983179

Rajmohamed, R. F., Chapala, S., Shazahan, M. A., Wali, P., & Botchu, R. (2025). Evaluating the Accuracy and Efficiency of AI-Generated Radiology Reports Based on Positive Findings: A Qualitative Assessment of AI in Radiology. Academic radiology, 32(12), 7035-7040. https://doi.org/10.1016/j.acra.2025.09.012 PMid:41015710

Sacoransky, E., Kwan, B. Y. M., & Soboleski, D. (2024). ChatGPT and assistive AI in structured radiology reporting: A systematic review. Current problems in diagnostic radiology, 53(6), 728-737. https://doi.org/10.1067/j.cpradiol.2024.07.007 PMid:39004580

Feng, W., Yazdani, A., Bornet, A., Platon, A., & Teodoro, D. (2025). Synthesising evidence regarding artificial intelligence-generated radiological reports based on medical images: a scoping review protocol. BMJ open, 15(10), e104112. https://doi.org/10.1136/bmjopen-2025-104112 PMid:41043831 PMCid:PMC12496096

Huang J, Wittbrodt MT, Teague CN, et al. Efficiency and Quality of Generative AI-Assisted Radiograph Reporting. JAMA Netw Open. 2025;8(6):e2513921. https://doi.org/10.1001/jamanetworkopen.2025.13921 PMid:40471579 PMCid:PMC12142447

Mehdiratta, G., Duda, J.T., Elahi, A. et al. Automated Integration of AI Results into Radiology Reports Using Common Data Elements. J Digit Imaging. Inform. med. 38, 2623-2629 (2025). https://doi.org/10.1007/s10278-025-01414-9 PMid:39871037 PMCid:PMC12572421

Park, J., Oh, K., Han, K., & Lee, Y. H. (2024). Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting. Scientific reports, 14(1), 13218. https://doi.org/10.1038/s41598-024-63824-z PMid:38851825 PMCid:PMC11162416

Hartung, M. P., Bickle, I. C., Gaillard, F., & Kanne, J. P. (2020). How to Create a Great Radiology Report. Radiographics: a review publication of the Radiological Society of North America, Inc, 40(6), 1658-1670. https://doi.org/10.1148/rg.2020200020 PMid:33001790

Mityul, M. I., Gilcrease-Garcia, B., Mangano, M. D., Demertzis, J. L., & Gunn, A. J. (2018). Radiology Reporting: Current Practices and an Introduction to Patient-Centered Opportunities for Improvement. AJR. American journal of roentgenology, 210(2), 376-385. https://doi.org/10.2214/AJR.17.18721 PMid:29140114

Veras Magalhães, G., L. de S. Santos, R., H. S. Vogado, L., Cardoso de Paiva, A., & de Alcântara dos Santos Neto, P. (2024). XRaySwinGen: Automatic medical reporting for X-ray exams with a multimodal model. Heliyon, 10(7), e27516. https://doi.org/10.1016/j.heliyon.2024.e27516 PMid:38560155 PMCid:PMC10979158

Hwang, E. J., Goo, J. M., Nam, J. G., Park, C. M., Hong, K. J., & Kim, K. H. (2023). Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients with Acute Respiratory Symptoms in the Emergency Department: A Pragmatic Randomized Clinical Trial. Korean journal of radiology, 24(3), 259-270. https://doi.org/10.3348/kjr.2022.0651 PMid:36788769 PMCid:PMC9971841

Haider, S. A., Prabha, S., Gomez-Cabello, C. A., Genovese, A., Collaco, B., Wood, N., Lifson, M. A., Bagaria, S., Tao, C., & Forte, A. J. (2025). Artificial Intelligence Physician Avatars for Patient Education: A Pilot Study. Journal of Clinical Medicine, 14(23), 8595. https://doi.org/10.3390/jcm14238595 PMid:41375898 PMCid:PMC12692777

Zalake M. (2023). Doctors' perceptions of using their digital twins in patient care. Scientific reports, 13(1), 21693. https://doi.org/10.1038/s41598-023-48747-5 PMid:38066016 PMCid:PMC10709415

Ye, J. Artificial intelligence-generated content (AIGC) in biomedical research, healthcare delivery, and clinical practices: technologies, applications, and regulatory considerations. Artif Intell Rev 59, 86 (2026). https://doi.org/10.1007/s10462-025-11487-1

Hong, E. K., Roh, B., Park, B., Jo, J. B., Bae, W., Soung Park, J., & Sung, D. W. (2025). Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study. Radiology, 314(3), e241646. https://doi.org/10.1148/radiol.241646 PMid:40067108

Lee, J. H., Sun, H. Y., Park, S., Kim, H., Hwang, E. J., Goo, J. M., & Park, C. M. (2020). Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population. Radiology, 297(3), 687-696. https://doi.org/10.1148/radiol.2020201240 PMid:32960729

Schwalbe N, Wahl B. Artificial intelligence and the future of global health. The Lancet. 2020 May 16;395(10236): 1579-86.doi not available https://doi.org/10.1016/S0140-6736(20)30226-9 PMid:32416782 PMCid:PMC7255280

Terranova C, Cestonaro C, Fava L, Cinquetti A. AI and professional liability assessment in healthcare. A revolution in legal medicine. Frontiers in Medicine. 2024 Jan 8; 10:1337335. https://doi.org/10.3389/fmed.2023.1337335 PMid:38259835 PMCid:PMC10800912

Asan O, Bayrak AE, Choudhury A. Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of medical Internet research. 2020 Jun 19;22(6):e15154. https://doi.org/10.2196/15154 PMid:32558657 PMCid:PMC7334754

Sandeep Reddy, Sonia Allan, Simon Coghlan, Paul Cooper, A governance model for the application of AI in health care, Journal of the American Medical Informatics Association, Volume 27, Issue 3, March 2020, Pages 491-497, https://doi.org/10.1093/jamia/ocz192 PMid:31682262 PMCid: PMC7647243