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
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
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
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
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
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
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
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
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
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
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
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
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
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
Comments (0)