Automated Protocolling in Radiology: Enhancing Efficiency and Reducing Workload
DOI:
https://doi.org/10.33178/boolean.2026.1.1Keywords:
Radiology, Automated Protocoling, Artificial Intelligence, Workload ReductionAbstract
The increasing demand for medical imaging has placed a heavy workload on radiologists, leading to fatigue and potential errors. For example, the volume of medical imaging studies has been increasing annually in various regions. Automated protocoling (AP) using artificial intelligence (AI) promises to reduce this burden by automating routine tasks. Protocoling in radiology involves selecting the best imaging study for a patient based on their history and symptoms. AI can automate this process, ensuring consistent decisions and freeing radiologists to handle more complex cases. This article reviews the impact of AP in radiology, highlighting its benefits in improving workflow efficiency.
References
Stec N., Arje D., Moody AR., Krupinski EA., Tyrrell PN. A systematic review of fatigue in radiology: Is it a problem? American Journal of Roentgenology 2018;210(4):799–806. Doi: 10.2214/AJR.17.18613/ASSET/IMAGES/LARGE/04_17_18613_01.JPEG.
Denck J., Haas O., Guehring J., Maier A., Rothgang E. Automated Protocoling for MRI Exams-Challenges and Solutions. J Digit Imaging 2022;35(5):1293–302. Doi: 10.1007/S10278-022-00610-1.
Raju N., Woodburn M., Kachel S., O’Shaughnessy J., Sorace L., Yang N., et al. A Review of Published Machine Learning Natural Language Processing Applications for Protocolling Radiology Imaging 2022.
Ranschaert E., Topff L., Pianykh O. Optimization of Radiology Workflow with Artificial Intelligence. Radiol Clin North Am 2021;59(6):955–66. Doi: 10.1016/J.RCL.2021.06.006.
Chung R., Demers JP., Tiberio R., Savage CA., McNulty F., Stout M., et al. Implementation of an Institution-Wide Rules-Based Automated CT Protocoling System. Https://DoiOrg/102214/AJR2329806 2024. Doi: 10.2214/AJR.23.29806. Reproduced with permission of the American Roentgen Ray Society Copyright© 2024 American Roentgen Ray Society.
Ashikyan O., Xia S., Chhabra A. Automatic Protocolling of Non-contrast Musculoskeletal MRIs Does Not Result in Increase in Patient Recall Rates for Contrast-Enhanced Studies. Acad Radiol 2024. Doi: 10.1016/J.ACRA.2023.12.028.
Chillakuru YR., Munjal S., Laguna B., Chen TL., Chaudhari GR., Vu T., et al. Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing. BMC Med Inform Decis Mak 2021;21(1). Doi: 10.1186/S12911-021-01574-Y.
Goisauf M., Cano Abadía M. Ethics of AI in Radiology: A Review of Ethical and Societal Implications. Front Big Data 2022;5:850383. Doi: 10.3389/FDATA.2022.850383/BIBTEX.
Stogiannos N., Malik R., Kumar A., Barnes A., Pogose M., Harvey H., et al. Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK. British Journal of Radiology 2023;96(1152). Doi: 10.1259/BJR.20221157/7498954.
van Kooten MJ., Tan CO., Hofmeijer EIS., van Ooijen PMA., Noordzij W., Lamers MJ., et al. A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist. Insights Imaging 2024;15(1):1–14. Doi: 10.1186/S13244-023-01595-3/FIGURES/4.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yasser H. Hadi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
