Etiologic classification of suspected MINOCA using cardiovascular magnetic resonance reports: a comparison of a large language model and human readers

Lindahl B, Baron T, Albertucci M, Prati F (2021) Myocardial infarction with non-obstructive coronary artery disease. EuroIntervention 17:e875–e887. https://doi.org/10.4244/EIJ-D-21-00426

Article  PubMed  PubMed Central  Google Scholar 

Pasupathy S, Air T, Dreyer RP et al (2015) Systematic review of patients presenting with suspected myocardial infarction and nonobstructive coronary arteries. Circulation 131:861–870. https://doi.org/10.1161/CIRCULATIONAHA.114.011201

Article  CAS  PubMed  Google Scholar 

Parwani P, Kang N, Safaeipour M et al (2023) Contemporary Diagnosis and Management of Patients with MINOCA. Curr Cardiol Rep 25:561–570. https://doi.org/10.1007/s11886-023-01874-x

Article  PubMed  PubMed Central  Google Scholar 

Mohammed A-Q, Abdu FA, Liu L et al (2023) Coronary microvascular dysfunction and myocardial infarction with non-obstructive coronary arteries: Where do we stand? Eur J Intern Med 117:8–20. https://doi.org/10.1016/j.ejim.2023.07.016

Article  PubMed  Google Scholar 

Tamis-Holland JE, Jneid H, Reynolds HR et al (2019) Contemporary Diagnosis and Management of Patients With Myocardial Infarction in the Absence of Obstructive Coronary Artery Disease: A Scientific Statement From the American Heart Association. Circulation 139:e891–e908. https://doi.org/10.1161/CIR.0000000000000670

Article  PubMed  Google Scholar 

Yildiz M, Ashokprabhu N, Shewale A et al (2022) Myocardial infarction with non-obstructive coronary arteries (MINOCA). Front Cardiovasc Med 9:1032436. https://doi.org/10.3389/fcvm.2022.1032436

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dastidar AG, Baritussio A, De Garate E et al (2019) Prognostic Role of CMR and Conventional Risk Factors in Myocardial Infarction With Nonobstructed Coronary Arteries. JACC Cardiovasc Imaging 12:1973–1982. https://doi.org/10.1016/j.jcmg.2018.12.023

Article  PubMed  Google Scholar 

Friedrich MG, Sechtem U, Schulz-Menger J et al (2009) Cardiovascular magnetic resonance in myocarditis: A JACC White Paper. J Am Coll Cardiol 53:1475–1487. https://doi.org/10.1016/j.jacc.2009.02.007

Article  PubMed  PubMed Central  Google Scholar 

Ferreira VM, Schulz-Menger J, Holmvang G et al (2018) Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation: Expert Recommendations. J Am Coll Cardiol 72:3158–3176. https://doi.org/10.1016/j.jacc.2018.09.072

Article  PubMed  Google Scholar 

Rao SV, O’Donoghue ML, Ruel M et al (2025) 2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the Management of Patients With Acute Coronary Syndromes: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 151:e771–e862. https://doi.org/10.1161/CIR.0000000000001309

Article  PubMed  Google Scholar 

Byrne RA, Rossello X, Coughlan JJ et al (2023) 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J 44(38):3720–3826. https://doi.org/10.1093/eurheartj/ehad191

Article  CAS  PubMed  Google Scholar 

Károlyi M, Polacin M, Kolossváry M et al (2024) Comparative analysis of late gadolinium enhancement assessment techniques for monitoring fibrotic changes in myocarditis follow-up. Eur Radiol 34:7264–7274. https://doi.org/10.1007/s00330-024-10756-x

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kotanidis CP, Bazmpani M-A, Haidich A-B et al (2018) Diagnostic Accuracy of Cardiovascular Magnetic Resonance in Acute Myocarditis: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging 11:1583–1590. https://doi.org/10.1016/j.jcmg.2017.12.008

Article  PubMed  Google Scholar 

Floridi L, Chiriatti M (2020) GPT-3: Its Nature, Scope, Limits, and Consequences. Minds Machines 30:681–694. https://doi.org/10.1007/s11023-020-09548-1

Article  Google Scholar 

Clusmann J, Kolbinger FR, Muti HS et al (2023) The future landscape of large language models in medicine. Commun Med (Lond) 3:141. https://doi.org/10.1038/s43856-023-00370-1

Article  PubMed  PubMed Central  Google Scholar 

Liu M, Okuhara T, Chang X (2024) Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis. J Med Internet Res 26:e60807. https://doi.org/10.2196/60807

Article  PubMed  PubMed Central  Google Scholar 

Adams LC, Truhn D, Busch F et al (2023) Leveraging GPT-4 for Post Hoc Transformation of Free-text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study. Radiology 307:e230725. https://doi.org/10.1148/radiol.230725

Article  PubMed  Google Scholar 

Kottlors J, Bratke G, Rauen P et al (2023) Feasibility of Differential Diagnosis Based on Imaging Patterns Using a Large Language Model. Radiology 308:e231167. https://doi.org/10.1148/radiol.231167

Article  PubMed  Google Scholar 

Gertz RJ, Bunck AC, Lennartz S et al (2023) GPT-4 for Automated Determination of Radiological Study and Protocol Based on Radiology Request Forms: A Feasibility Study. Radiology 307:e230877. https://doi.org/10.1148/radiol.230877

Article  PubMed  Google Scholar 

Kramer CM, Barkhausen J, Bucciarelli-Ducci C et al (2020) Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update. J Cardiovasc Magn Reson 22:17. https://doi.org/10.1186/s12968-020-00607-1

Article  PubMed  PubMed Central  Google Scholar 

Schulz-Menger J, Bluemke DA, Bremerich J et al (2020) Standardized image interpretation and post-processing in cardiovascular magnetic resonance – 2020 update: Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task Force on Standardized Post-Processing. J Cardiovasc Magn Reson 22:19. https://doi.org/10.1186/s12968-020-00610-6

Article  PubMed  PubMed Central  Google Scholar 

Plein S, Schulz-Menger J, Almeida A et al (2011) Training and accreditation in cardiovascular magnetic resonance in Europe: a position statement of the working group on cardiovascular magnetic resonance of the European Society of Cardiology. Eur Heart J 32:793–798. https://doi.org/10.1093/eurheartj/ehq474

Article  PubMed  Google Scholar 

OpenAI (2023) GPT-4 Technical Report. arXiv:2303.08774. https://arxiv.org/abs/2303.08774

Koo TK, Li MY (2016) A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 15:155–163. https://doi.org/10.1016/j.jcm.2016.02.012

Article  PubMed  PubMed Central  Google Scholar 

Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull. https://doi.org/10.1037/h0031619

Article  Google Scholar 

Kaya K, Gietzen C, Hahnfeldt R et al (2024) Generative Pre-trained Transformer 4 analysis of cardiovascular magnetic resonance reports in suspected myocarditis: A multicenter study. J Cardiovasc Magn Reson 26:101068. https://doi.org/10.1016/j.jocmr.2024.101068

Article  PubMed  PubMed Central  Google Scholar 

Hasani AM, Singh S, Zahergivar A et al (2024) Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports. Eur Radiol 34:3566–3574. https://doi.org/10.1007/s00330-023-10384-x

Article  PubMed  Google Scholar 

Salam B, Kravchenko D, Nowak S et al (2024) Generative Pre-trained Transformer 4 makes cardiovascular magnetic resonance reports easy to understand. J Cardiovasc Magn Reson 26:101035. https://doi.org/10.1016/j.jocmr.2024.101035

Article  PubMed  PubMed Central  Google Scholar 

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