{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T11:09:31Z","timestamp":1791457771545,"version":"4.3.4"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mayo Clinic Department of Radiology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Radiology is undergoing a paradigm shift from traditional single-function AI systems to sophisticated multi-agent networks capable of autonomous reasoning, coordinated decision-making, and adaptive workflow management. These agentic AI systems move beyond simple pattern recognition to encompass complex radiological workflows including image analysis, report generation, clinical communication, and care coordination. While multi-agent radiological AI promises enhanced diagnostic accuracy, improved workflow efficiency, and reduced physician burden, it simultaneously amplifies the long-standing \u201cblack box\u201d problem. Traditional explainable AI methods, which are adequate for understanding isolated diagnostic predictions, fail when applied to multi-step reasoning processes involving multiple specialized agents coordinating across imaging interpretation, clinical correlation, and treatment planning. This paper examines how agentic AI systems in radiology create \u201ccompound opacity\u201d layers of inscrutability from agent interactions and distributed decision-making processes. We analyze the autonomy\u2013transparency paradox specific to radiological practice, where increasing AI capability directly conflicts with interpretability requirements essential for clinical trust and regulatory oversight. Through examination of emerging multi-agent radiological workflows, we propose frameworks for responsible implementation that preserve both diagnostic innovation and the fundamental principles of medical transparency and accountability.<\/jats:p>","DOI":"10.3390\/bioengineering12101100","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:13:27Z","timestamp":1760361207000},"page":"1100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Beyond Single Systems: How Multi-Agent AI Is Reshaping Ethics in Radiology"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3223-9059","authenticated-orcid":false,"given":"Sara","family":"Salehi","sequence":"first","affiliation":[{"name":"Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5848-7072","authenticated-orcid":false,"given":"Yashbir","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parnian","family":"Habibi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-6095","authenticated-orcid":false,"given":"Bradley J.","family":"Erickson","sequence":"additional","affiliation":[{"name":"Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"888","DOI":"10.3348\/kjr.2025.0370","article-title":"Uncover This Tech Term: Agentic Artificial Intelligence in Radiology","volume":"26","author":"Faghani","year":"2025","journal-title":"Korean J. Radiol."},{"key":"ref_2","unstructured":"Botti, V. (2025). Agentic AI and Multiagentic: Are We Reinventing the Wheel?. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bousetouane, F. (2025). Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents. arXiv.","DOI":"10.32388\/2DKDCK"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kandogan, E., Bhutani, N., Zhang, D., Chen, R.L., Gurajada, S., and Hruschka, E. (2025). Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI. arXiv.","DOI":"10.1109\/ICDEW67478.2025.00007"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Singh, Y., Hathaway, Q.A., Keishing, V., Salehi, S., Wei, Y., Horvat, N., Vera-Garcia, D.V., Choudhary, A., Mula Kh, A., and Quaia, E. (2025). Beyond Post Hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency, Interpretability, and Explainability. Bioengineering, 12.","DOI":"10.3390\/bioengineering12080879"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"110787","DOI":"10.1016\/j.ejrad.2023.110787","article-title":"Explainable AI in Medical Imaging: An Overview for Clinical Practitioners\u2014Saliency-Based XAI Approaches","volume":"162","author":"Borys","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"20230142","DOI":"10.1259\/bjr.20230142","article-title":"Interpretable Artificial Intelligence in Radiology and Radiation Oncology","volume":"96","author":"Cui","year":"2023","journal-title":"Br. J. Radiol."},{"key":"ref_8","unstructured":"Yi, Z., Xiao, T., and Albert, M.V. (2025). A Multimodal Multi-Agent Framework for Radiology Report Generation. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1109\/TCYB.2020.2977374","article-title":"Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications","volume":"50","author":"Nguyen","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"129059","DOI":"10.1016\/j.eswa.2025.129059","article-title":"Feature Disentanglement and Cross-Domain Synthesis via Federated Style Transfer for Non-IID Segmentation","volume":"296","author":"Ma","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107452","DOI":"10.1016\/j.bspc.2024.107452","article-title":"CGAM: An End-to-End Causality Graph Attention Mamba Network for Esophageal Pathology Grading","volume":"103","author":"Qu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.53623\/apga.v4i1.495","article-title":"Hybrid and Blended Learning Models: Innovations, Challenges, and Future Directions in Education","volume":"4","author":"Mulenga","year":"2025","journal-title":"Acta Pedagog. Asian."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Leutz-Schmidt, P., Palm, V., Mathy, R.M., Gr\u00f6zinger, M., Kauczor, H.U., Jang, H., and Sedaghat, S. (2025). Performance of Large Language Models ChatGPT and Gemini on Workplace Management Questions in Radiology. Diagnostics, 15.","DOI":"10.3390\/diagnostics15040497"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110273","DOI":"10.1016\/j.knosys.2023.110273","article-title":"Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities","volume":"263","author":"Saeed","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e190043","DOI":"10.1148\/ryai.2020190043","article-title":"On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities","volume":"2","author":"Reyes","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.2214\/AJR.21.26139","article-title":"Automated Processing of Head CT Perfusion Imaging for Ischemic Stroke Triage: A Practical Guide to Quality Assurance and Interpretation","volume":"217","author":"Chung","year":"2021","journal-title":"Am. J. Roentgenol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"984021","DOI":"10.3389\/fonc.2022.984021","article-title":"Data-Driven Support to Decision-Making in Molecular Tumour Boards for Lymphoma: A Design Science Approach","volume":"12","author":"Eloranta","year":"2022","journal-title":"Front. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1148\/radiol.2019190613","article-title":"A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH\/RSNA\/ACR\/The Academy Workshop","volume":"291","author":"Langlotz","year":"2019","journal-title":"Radiology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1180773","DOI":"10.3389\/fmed.2023.1180773","article-title":"Explainable Artificial Intelligence (XAI) in Radiology and Nuclear Medicine: A Literature Review","volume":"10","author":"Zwezerijnen","year":"2023","journal-title":"Front. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.crad.2022.08.135","article-title":"The Current Status and Future of FDA-Approved Artificial Intelligence Tools in Chest Radiology in the United States","volume":"78","author":"Milam","year":"2023","journal-title":"Clin. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1038\/s41746-024-01232-3","article-title":"Navigating the EU AI Act: Implications for Regulated Digital Medical Products","volume":"7","author":"Aboy","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fraser, A.G., Buccheri, S., Byrne, R.A., Kjaersgaard-Andersen, P., James, S., J\u00fcni, P., Bally, L., Bulbulia, R., Koletzko, B.V., and Landray, M.J. (2025). Recommended Methodologies for Clinical Investigations of High-Risk Medical Devices\u2014Conclusions from the European Union CORE\u2013MD Project. Lancet Reg. Health Eur., 101460.","DOI":"10.1016\/j.lanepe.2025.101460"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100001","DOI":"10.1016\/j.ibmed.2020.100001","article-title":"What We Talk About When We Talk About Trust: Theory of Trust for AI in Healthcare","volume":"1\u20132","author":"Gille","year":"2020","journal-title":"Intell. Based Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1093\/jamia\/ocaa268","article-title":"Trust and Medical AI: The Challenges We Face and the Expertise Needed to Overcome Them","volume":"28","author":"Quinn","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e250477","DOI":"10.1148\/radiol.250477","article-title":"Beyond Assistance: The Case for Role Separation in AI-Human Radiology Workflows","volume":"316","author":"Rajpurkar","year":"2025","journal-title":"Radiology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1038\/s41551-025-01363-2","article-title":"Coordinated AI Agents for Advancing Healthcare","volume":"9","author":"Moritz","year":"2025","journal-title":"Nat. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/d41586-025-02454-5","article-title":"We Need a New Ethics for a World of AI Agents","volume":"644","author":"Gabriel","year":"2025","journal-title":"Nature"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/10\/1100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:14:16Z","timestamp":1760368456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/10\/1100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"references-count":27,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["bioengineering12101100"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12101100","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,13]]}}}