{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T22:38:50Z","timestamp":1789166330226,"version":"build-2803163510"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Oncology practice is increasingly aiming to be patient centric. Imaging is a decisive part of the management of cancer patients and with the introduction of Digital Health Records (DHR) patients have the possibility of accessing their imaging results independently, yet the optimal way of doing so is still not clear. The introduction of Large Language Models (LLM) offers the potential to turn radiology reports into a clearer, accessible and unambiguous format and to democratise patient\u2019s access to their own medical records.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A multi-reader retrospective Service Evaluation (SE) conducted at a tertiary oncology hospital aimed to assess the capability of an LLM to generate two versions of simplified oncology imaging reports. The SE assessed Patient and Public Involvement (PPI) representatives and healthcare professionals\u2019 (HCP) preferences using original radiology reports from two cohorts, colorectal (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200930) and lung (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200930) cancer. A Prompt-development phase created two prompts to generate the summarised (version A) and the full-length (version B) report versions. The review was performed by radiologists with 360 reads and PPI representatives with 180 reads.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Radiologists scores between summaries and full-length reports differed per cohort. In the lung cohort, version A was rated higher for factual correctness (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.001), completeness (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.0001), accessibility and readability (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.026), and benefit to patients (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.0001). The opposite was seen in the colorectal cohort, version B achieved consistently higher scores (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.002). When the two cohorts were combined, median scores for version A and B did not differ significantly (all\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&gt;\u20090.057). PPI reviews indicated that full-length reports were favoured significantly (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.0001). Qualitative results from radiologists and PPI identified incorrect statements (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200928), complex terminology (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200918), addition of confusion (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910), and missing information (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>LLM simplified reports have the potential to improve patient accessibility in oncology imaging. PPI and HCP preferences for summarised versus full-length reports vary. Findings suggest these outputs are likely to benefit from appropriate adjustments to individual patient needs and clinical context. Reports with incorrect, confusing and missing content, highlight that LLM need improvement, ahead of potential clinical use in this setting.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40644-026-01031-x","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:45:20Z","timestamp":1776105920000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving patient understanding of oncology imaging: radiologist and patient evaluation of summarised versus full-length AI-simplified reports from a tertiary cancer centre"],"prefix":"10.1186","volume":"26","author":[{"given":"Ana S. F.","family":"Ribeiro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Husson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheila","family":"Matharu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saffron","family":"Cox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Meo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Sidebottom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robby","family":"Emsley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Shur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Castagnoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharmin","family":"Malekout","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Robinson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chin Lian","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Kelly-Morland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wim J.G.","family":"Oyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Winette T. A.","family":"van der Graaf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Messiou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"1031_CR1","doi-asserted-by":"publisher","unstructured":"Rong J, Liu Y. Advances in medical imaging techniques. BMC Methods. 2024\/08\/01 2024;1(1):10. https:\/\/doi.org\/10.1186\/s44330-024-00010-7.","DOI":"10.1186\/s44330-024-00010-7"},{"issue":"2","key":"1031_CR2","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.jacr.2021.10.008","volume":"19","author":"J Kemp","year":"2022","unstructured":"Kemp J, Short R, Bryant S, Sample L, Befera N. Patient-Friendly Radiology Reporting\u2014Implementation and Outcomes. J Am Coll Radiol. 2022;19(2):377\u201383. https:\/\/doi.org\/10.1016\/j.jacr.2021.10.008.","journal-title":"J Am Coll Radiol"},{"issue":"5","key":"1031_CR3","doi-asserted-by":"publisher","first-page":"None","DOI":"10.1016\/j.hlpt.2024.100915","volume":"13","author":"ASF Ribeiro","year":"2024","unstructured":"Ribeiro ASF, Husson O, Reuvers M, Oyen WJG, Messiou C, van der Graaf WTA. Perspectives on access to imaging digital health records in oncology: A mixed methods systematic review. Health Policy Technol Nov. 2024;13(5):None. https:\/\/doi.org\/10.1016\/j.hlpt.2024.100915.","journal-title":"Health Policy Technol Nov"},{"key":"1031_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2025.112111","author":"MHE van Driel","year":"2025","unstructured":"van Driel MHE, Blok N, van den Brand JAJG, et al. Leveraging GPT-4 enables patient comprehension of radiology reports. Eur J Radiol. 2025. https:\/\/doi.org\/10.1016\/j.ejrad.2025.112111. 06\/01\/ 2025;187:112111.","journal-title":"Eur J Radiol"},{"key":"1031_CR5","doi-asserted-by":"publisher","unstructured":"Gordon EB, Towbin AJ, Wingrove P, et al. Enhancing patient communication with chat-GPT in radiology: evaluating the efficacy and readability of answers to common imaging-related questions. J Am Coll Radiol. 2024\/02\/01\/ 2024;21(2):353\u2013359. https:\/\/doi.org\/10.1016\/j.jacr.2023.09.011.","DOI":"10.1016\/j.jacr.2023.09.011"},{"issue":"12","key":"1031_CR6","doi-asserted-by":"publisher","first-page":"1862","DOI":"10.1016\/j.jacr.2024.08.006","volume":"21","author":"M Alarifi","year":"2024","unstructured":"Alarifi M, Hughes MC, Jabour AM, Alashban Y, Vernon E, Patient. Referring Physician, and Radiologist Opinions Over Time on Providing Patients Access to Radiology Reports: A Systematic Review. J Am Coll Radiol Dec. 2024;21(12):1862\u201374. https:\/\/doi.org\/10.1016\/j.jacr.2024.08.006.","journal-title":"J Am Coll Radiol Dec"},{"issue":"1","key":"1031_CR7","doi-asserted-by":"publisher","first-page":"e231114","DOI":"10.1148\/radiol.231114","volume":"309","author":"CP Langlotz","year":"2023","unstructured":"Langlotz CP. The Future of AI and Informatics in Radiology: 10 Predictions. Radiology. 2023;309(1):e231114. https:\/\/doi.org\/10.1148\/radiol.231114.","journal-title":"Radiology"},{"key":"1031_CR8","doi-asserted-by":"publisher","unstructured":"Yang J, Li HB, Wei D. The impact of ChatGPT and LLMs on medical imaging stakeholders: perspectives and use cases. Meta Radiol. 2023\/06\/01\/ 2023;1(1):100007. https:\/\/doi.org\/10.1016\/j.metrad.2023.100007.","DOI":"10.1016\/j.metrad.2023.100007"},{"key":"1031_CR9","doi-asserted-by":"publisher","unstructured":"Perera Molligoda Arachchige AS. Empowering radiology: the transformative role of ChatGPT. Clinical Radiology. 2023\/11\/01\/ 2023;78(11):851\u2013855. https:\/\/doi.org\/10.1016\/j.crad.2023.08.006.","DOI":"10.1016\/j.crad.2023.08.006"},{"key":"1031_CR10","doi-asserted-by":"publisher","unstructured":"Herwald SE, Shah P, Johnston A, Olsen C, Delbrouck J-B, Langlotz CP. RadGPT: a system based on a large language model that generates sets of patient-centered materials to explain radiology report information. J Am Coll Radiol. https:\/\/doi.org\/10.1016\/j.jacr.2025.06.013.","DOI":"10.1016\/j.jacr.2025.06.013"},{"issue":"6","key":"1031_CR11","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1001\/jamainternmed.2023.1838","volume":"183","author":"JW Ayers","year":"2023","unstructured":"Ayers JW, Poliak A, Dredze M, et al. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Intern Med. 2023;183(6):589\u201396. https:\/\/doi.org\/10.1001\/jamainternmed.2023.1838.","journal-title":"JAMA Intern Med"},{"issue":"4","key":"1031_CR12","doi-asserted-by":"publisher","first-page":"e230725","DOI":"10.1148\/radiol.230725","volume":"307","author":"LC Adams","year":"2023","unstructured":"Adams LC, Truhn D, Busch F, et al. Leveraging GPT-4 for Post Hoc Transformation of Free-text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study. Radiology. 2023;307(4):e230725. https:\/\/doi.org\/10.1148\/radiol.230725.","journal-title":"Radiology"},{"issue":"3","key":"1031_CR13","doi-asserted-by":"publisher","first-page":"e233117","DOI":"10.1148\/radiol.233117","volume":"311","author":"R Bhayana","year":"2024","unstructured":"Bhayana R, Nanda B, Dehkharghanian T, et al. Large Language Models for Automated Synoptic Reports and Resectability Categorization in Pancreatic Cancer. Radiology. 2024;311(3):e233117. https:\/\/doi.org\/10.1148\/radiol.233117.","journal-title":"Radiology"},{"key":"1031_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10213-1","author":"K Jeblick","year":"2023","unstructured":"Jeblick K, Schachtner B, Dexl J, et al. ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol. 2023https:\/\/doi.org\/10.1007\/s00330-023-10213-1.","journal-title":"Eur Radiol"},{"issue":"1","key":"1031_CR15","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s42492-023-00136-5","volume":"2023\/05\/18","author":"Q Lyu","year":"2023","unstructured":"Lyu Q, Tan J, Zapadka ME, et al. Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: results, limitations, and potential. Visual Comput Ind Biomed Art. 2023;2023\/05\/18(1):9. https:\/\/doi.org\/10.1186\/s42492-023-00136-5.","journal-title":"Visual Comput Ind Biomed Art"},{"issue":"2","key":"1031_CR16","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1177\/1098214005283748","volume":"27","author":"DR Thomas","year":"2006","unstructured":"Thomas DR. A General Inductive Approach for Analyzing Qualitative Evaluation Data. Am J Evaluation. 2006;27(2):237\u201346. https:\/\/doi.org\/10.1177\/1098214005283748.","journal-title":"Am J Evaluation"},{"key":"1031_CR17","unstructured":"Dong J. Simpson\u2019s Paradox. Encyclopedia of Biostatistics. 2005."},{"key":"1031_CR18","unstructured":"Guidance on patient digital notification of diagnostic imaging reports (NHS E). 2025."},{"key":"1031_CR19","doi-asserted-by":"publisher","unstructured":"dos Santos DP, Kotter E, Mildenberger P, Mart\u00ed-Bonmat\u00ed L. European society of R. ESR paper on structured reporting in radiology\u2014update 2023. Insights into Imaging. 2023\/11\/23 2023;14(1):199. https:\/\/doi.org\/10.1186\/s13244-023-01560-0.","DOI":"10.1186\/s13244-023-01560-0"},{"issue":"6","key":"1031_CR20","doi-asserted-by":"publisher","first-page":"e2330343","DOI":"10.2214\/ajr.23.30343","volume":"222","author":"JR Pollock","year":"2024","unstructured":"Pollock JR, Petty SAB, Schmitz JJ, Varner J, Metcalfe AM, Tan N. Patient Access of Their Radiology Reports Before and After Implementation of 21st Century Cures Act Information-Blocking Provisions at a Large Multicampus Health System. AJR Am J Roentgenol Jun. 2024;222(6):e2330343. https:\/\/doi.org\/10.2214\/ajr.23.30343.","journal-title":"AJR Am J Roentgenol Jun"},{"key":"1031_CR21","doi-asserted-by":"publisher","first-page":"100287","DOI":"10.1016\/j.ibmed.2025.100287","volume":"12","author":"Y Artsi","year":"2025","unstructured":"Artsi Y, Klang E, Collins JD, et al. Large language models in radiology reporting - A systematic review of performance, limitations, and clinical implications. Intelligence-Based Med. 2025;12:100287. https:\/\/doi.org\/10.1016\/j.ibmed.2025.100287. 2025\/01\/01\/.","journal-title":"Intelligence-Based Med"},{"key":"1031_CR22","doi-asserted-by":"publisher","unstructured":"Doshi R, Amin K, Khosla P, Bajaj S, Chheang S, Forman HP. Utilizing large language models to simplify radiology reports: a comparative analysis of ChatGPT 3.5, ChatGPT 4.0, Google Bard, and, Bing M. medRxiv. 2023. https:\/\/doi.org\/10.1101\/2023.06.04.23290786.","DOI":"10.1101\/2023.06.04.23290786"},{"key":"1031_CR23","doi-asserted-by":"publisher","unstructured":"Gertz RJ, Beste NC, Dratsch T et al. From dictation to diagnosis: enhancing radiology reporting with integrated speech recognition in multimodal large language models. Eur Radiol. 2025\/08\/15 2025;https:\/\/doi.org\/10.1007\/s00330-025-11929-y","DOI":"10.1007\/s00330-025-11929-y"},{"key":"1031_CR24","doi-asserted-by":"publisher","unstructured":"Martin-Carreras T, Cook TS, Kahn CE. Readability of radiology reports: implications for patient-centered care. Clin Imaging. 2019\/03\/01\/ 2019;54:116\u2013120. https:\/\/doi.org\/10.1016\/j.clinimag.2018.12.006.","DOI":"10.1016\/j.clinimag.2018.12.006"},{"key":"1031_CR25","doi-asserted-by":"publisher","first-page":"e65547","DOI":"10.2196\/65547","volume":"3","author":"D Hu","year":"2025","unstructured":"Hu D, Zhang S, Liu Q, Zhu X, Liu B. Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study. J Med Internet Res Apr. 2025;3:27:e65547. https:\/\/doi.org\/10.2196\/65547.","journal-title":"J Med Internet Res Apr"},{"key":"1031_CR26","doi-asserted-by":"publisher","first-page":"e76097","DOI":"10.2196\/76097","volume":"27","author":"A Sunshine","year":"2025","unstructured":"Sunshine A, Honce GH, Callen AL, et al. Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study. JMIR Form Res Aug. 2025;27:9:e76097. https:\/\/doi.org\/10.2196\/76097.","journal-title":"JMIR Form Res Aug"},{"issue":"2","key":"1031_CR27","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1067\/j.cpradiol.2024.12.008","volume":"54","author":"KS Amin","year":"2025","unstructured":"Amin KS, Davis MA, Naderi A, Forman HP. Release of complex imaging reports to patients, do radiologists trust AI to help? Curr Probl Diagn Radiol. Mar-Apr. 2025;54(2):147\u201350. https:\/\/doi.org\/10.1067\/j.cpradiol.2024.12.008.","journal-title":"Mar-Apr"},{"key":"1031_CR28","doi-asserted-by":"publisher","first-page":"112198","DOI":"10.1016\/j.ejrad.2025.112198","volume":"189","author":"A Gupta","year":"2025","unstructured":"Gupta A, Hussain M, Nikhileshwar K, Rastogi A, Rangarajan K. Integrating Large language models into radiology workflow: Impact of generating personalized report templates from summary. Eur J Radiol Aug. 2025;189:112198. https:\/\/doi.org\/10.1016\/j.ejrad.2025.112198.","journal-title":"Eur J Radiol Aug"},{"key":"1031_CR29","doi-asserted-by":"publisher","unstructured":"Patwardhan V, Balchander D, Fussell D, et al. Leveraging large language models to enhance radiology report readability: a systematic review. J Am Coll Radiol. Sep 2025;11. https:\/\/doi.org\/10.1016\/j.jacr.2025.09.004.","DOI":"10.1016\/j.jacr.2025.09.004"},{"key":"1031_CR30","doi-asserted-by":"publisher","unstructured":"Gardner R, Canin B, Loh KP, et al. Will evidence-based patient-centred care meet the future inter-generational needs of older adults with cancer? A diverse perspective. J Geriatr Oncol. 2025\/06\/01\/ 2025;16(5):102241. https:\/\/doi.org\/10.1016\/j.jgo.2025.102241.","DOI":"10.1016\/j.jgo.2025.102241"},{"key":"1031_CR31","doi-asserted-by":"publisher","unstructured":"Mahajan A, Bates DW. Clinical artificial intelligence\u2014the case for a new physician role. Lancet Reg Health \u2013 Americas. 2025;51. https:\/\/doi.org\/10.1016\/j.lana.2025.101280.","DOI":"10.1016\/j.lana.2025.101280"}],"container-title":["Cancer Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40644-026-01031-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-026-01031-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-026-01031-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T21:57:58Z","timestamp":1781647078000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40644-026-01031-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1031"],"URL":"https:\/\/doi.org\/10.1186\/s40644-026-01031-x","relation":{},"ISSN":["1470-7330"],"issn-type":[{"value":"1470-7330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,13]]},"assertion":[{"value":"4 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval for this study was obtained from the Committee for Clinical Research (CCR) - Royal Marsden NHS Foundation Trust (Service Evaluation 1362).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All CT scan reports used were from deceased colorectal and lung cancer patients, performed between 2015 and 2016, and all were subject to the National Data Opt-Out (NDOO) mesh framework and fully anonymised by the Trust Performance and Information team prior to becoming available to the researchers. The study was conducted in accordance with the Declaration of Helsinki, and approval was also received from the Trust Information Governance board following a Data Protection Impact Assessment approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"68"}}