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Although large language models (LLMs) have shown promise in medical imaging diagnosis, their application in cardiac MRI is limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Hypothesis<\/jats:title>\n                    <jats:p>LLMs may be effective in achieving cardiac MRI diagnosis based on standardized descriptions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>A total of 203 hypertrophic cardiomyopathy, 186 dilated cardiomyopathy, 46 hypertensive heart disease, 198 ischemic cardiomyopathy, 38 constrictive pericarditis, 45 cardiac amyloidosis, 91 myocarditis, and 144 normal controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequences<\/jats:title>\n                    <jats:p>Balanced steady\u2010state free\u2010precession, short tau inversion recovery, and breath\u2010hold inversion\u2010recovery segmented gradient\u2010echo sequences at 3.0\u2009T.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Clinical and cardiac MRI information from each subject was converted into standardized descriptions and input into Generative Pre\u2010trained Transformer\u20104.5 (GPT\u20104.5), GPT\u20104 Omni (GPT\u20104o), Deepseek\u2010V3, and Deepseek\u2010R1 LLMs. Cardiac MRI information included LV function, wall thickness and motion, and abnormalities on T2WI, perfusion and late gadolinium enhancement sequences. Each model was asked to generate an imaging diagnosis. In addition, a medical student (8\u2009months experience) and three radiologists (junior, mid\u2010level and senior: with 3, 6, and 10\u2009years' experience, respectively) provided diagnoses based on cardiac MRI images and clinical information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistic Tests<\/jats:title>\n                    <jats:p>\n                      Frequency\u2010weighted sensitivity and specificity were calculated. The diagnostic performances of the LLMs and human readers were compared using the McNemar test with Bonferroni correction. A\n                      <jats:italic>p<\/jats:italic>\n                      value &lt;\u20090.05 was considered significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      All LLMs showed excellent frequency\u2010weighted specificity (0.973\u20130.983). The frequency\u2010weighted sensitivities of all LLMs were not significantly different from that of the junior radiologist, were significantly higher than that of the medical student, and significantly inferior to those of the senior radiologist (GPT\u20104.5: 0.863, GPT\u20104o: 0.821, Deepseek\u2010V3: 0.843, and Deepseek\u2010R1: 0.851 vs. junior radiologist: 0.850, all adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20091.000; vs. medical student: 0.731, all adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001; vs. senior radiologist: 0.942, all adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Additionally, the mid\u2010level radiologist achieved a frequency\u2010weighted sensitivity of 0.895, outperforming all LLMs except GPT\u20104.5.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>LLMs may generate accurate diagnoses from standardized cardiac MRI descriptions, potentially benefiting less experienced physicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>4.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 5.<\/jats:p>\n                  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