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In a report-based reader study, four radiologists classified MRI reports with and without large language model assistance. In standalone evaluation, a rule-based baseline and 10 models were compared under naive and criteria-guided prompting. The reference standard was expert consensus based on the report text.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 433 patients (mean age, 65.9 years \u00b1 13.4; 248 males), AI assistance improved six-class agreement with the text-based reference standard. At the vessel level, junior radiologists improved from 61.75% to 83.12% (Qwen3-8B) and 87.75% (DeepSeek-R1); senior radiologists from 77.00% to 87.50% and 92.25%, respectively (all Holm-corrected\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Patient-level agreement increased from 41.0% to 78.8% (junior) and from 59.8% to 87.8% (senior) with DeepSeek-R1. Agreement increased (quadratic-weighted\n                      <jats:italic>\u03ba<\/jats:italic>\n                      : 0.231 without AI vs 0.775 with DeepSeek-R1). In standalone evaluation, criteria-guided prompting improved Qwen3-8B agreement from 38.91% to 81.52% (95% CI: 78.87\u201384.06%), remaining 5.89 percentage points below DeepSeek-R1 (87.41%; 95% CI: 85.10\u201389.61%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this report-based classification setting, large language model assistance improved radiologists\u2019 agreement with the text-based reference standard, with larger gains in junior radiologists. When prompts explicitly encoded AHA criteria, an 8B local candidate model performed well, supporting its potential use as a standardization aid for text-based workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      Report-based modified AHA classification of carotid plaques is experience-dependent and difficult to standardize, as mapping multi-sequence imaging findings to classification conclusions imposes substantial cognitive load.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      Large language model assistance improved agreement with a text-based reference standard and raised inter-reader consistency (weighted \u03ba 0.231\u20130.775), narrowing the junior\u2013senior gap.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Critical relevance statement<\/jats:italic>\n                      <\/jats:bold>\n                      Embedding explicit AHA criteria in the prompt let an 8B model approach 671B-model agreement (38.91\u201381.52%), supporting standardized text-based workflows without fine-tuning.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13244-026-02421-2","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:57:22Z","timestamp":1792173442000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large language model assistance for report-based carotid plaque AHA classification: a multi-reader study"],"prefix":"10.1186","volume":"17","author":[{"given":"Mengfei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiao","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongzheng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runjianya","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruipeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoer","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2417-0867","authenticated-orcid":false,"given":"Yuehua","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"2421_CR1","doi-asserted-by":"publisher","unstructured":"North American Symptomatic Carotid Endarterectomy Trial Collaborators, Barnett HJM, Taylor DW et al (1991) Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. 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