Olender, M.L.; de la Torre Hernández, J.M.; Athanasiou, L.S.; Nezami, F.R.; Edelman, E.R. Artificial intelligence to generate medical images: augmenting the cardiologist's visual clinical workflow. Eur Heart J Digit Health 2021, 2, 539-544, doi:https://doi.org/10.1093/ehjdh/ztab052.

Article  PubMed  PubMed Central  Google Scholar 

Brand, A.L. A picture worth (even more than) a thousand words. Med Econ 2011, 88, 52, 55.

PubMed  Google Scholar 

Levie, W.H.; Lentz, R. Effects of text illustrations: A review of research. Ectj 1982, 30, 195-232.

Article  Google Scholar 

Filippatou, D.; Pumfrey, P.D. Pictures, titles, reading accuracy and reading comprehension: a research review (1973‐95). Educational Research 1996, 38, 259-291.

Article  Google Scholar 

Kools, M.; van de Wiel, M.W.; Ruiter, R.A.; Kok, G. Pictures and text in instructions for medical devices: effects on recall and actual performance. Patient Educ Couns 2006, 64, 104-111. https://doi.org/10.1016/j.pec.2005.12.003.

Article  PubMed  Google Scholar 

Johnson-Laird, P.N. Mental models: Towards a cognitive science of language, inference, and consciousness; Harvard University Press: 1983.

Mayer, R.E. Multimedia learning: Are we asking the right questions? Educational psychologist 1997, 32, 1-19.

Article  Google Scholar 

Schnotz, W.; Bannert, M. Influence of the type of visualization on the construction of mental models during picture and text comprehension. Zeitschrift fur Experimentelle Psychologie: Organ der Deutschen Gesellschaft fur Psychologie 1999, 46, 217-236.

Article  CAS  PubMed  Google Scholar 

Ganier, F. Processing text and pictures in procedural instructions. Information Design Journal 2000, 10, 146-153.

Article  Google Scholar 

Ker, J. Teaching made easy: A manual for health professionals. Bmj 2000, 320, 1677.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deci, E.L.; Ryan, R.M. Intrinsic motivation and self-determination in human behavior; Springer Science & Business Media: 2013.

Kolb, A.Y.; Kolb, D.A. Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of management learning & education 2005, 4, 193-212.

Article  Google Scholar 

Mukhalalati, B.A.; Taylor, A. Adult Learning Theories in Context: A Quick Guide for Healthcare Professional Educators. J Med Educ Curric Dev 2019, 6, 2382120519840332. https://doi.org/10.1177/2382120519840332.

Article  PubMed  PubMed Central  Google Scholar 

Reed, J.M. Using Generative AI to Produce Images for Nursing Education. Nurse Educ 2023, 48, 246. https://doi.org/10.1097/nne.0000000000001453.

Article  PubMed  Google Scholar 

Zhu, L.; Mou, W.; Wu, K.; Zhang, J.; Luo, P. Can DALL-E 3 Reliably Generate 12-Lead ECGs and Teaching Illustrations? Cureus 2024, 16, e52748. https://doi.org/10.7759/cureus.52748.

Article  PubMed  PubMed Central  Google Scholar 

Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; Sutskever, I. Zero-shot text-to-image generation. 2021; pp. 8821–8831.

Black, J. DALL-E 3 vs Midjourney: which AI photo tool is better? Available online: https://www.pickfu.com/blog/dall-e-vs-midjourney/ (accessed on 6 Jan 2024).

OpenAI. DALL·E 3. Available online: https://openai.com/dall-e-3 (accessed on 6 Jan 2024).

Adams, L.C.; Busch, F.; Truhn, D.; Makowski, M.R.; Aerts, H.; Bressem, K.K. What Does DALL-E 2 Know About Radiology? J Med Internet Res 2023, 25, e43110. https://doi.org/10.2196/43110.

Article  PubMed  PubMed Central  Google Scholar 

Jamal, A.; Solaiman, M.; Alhasan, K.; Temsah, M.H.; Sayed, G. Integrating ChatGPT in Medical Education: Adapting Curricula to Cultivate Competent Physicians for the AI Era. Cureus 2023, 15, e43036. https://doi.org/10.7759/cureus.43036.

Article  PubMed  PubMed Central  Google Scholar 

Hajar, R. Medical illustration: art in medical education. Heart Views 2011, 12, 83-91. https://doi.org/10.4103/1995-705x.86023.

Article  PubMed  PubMed Central  Google Scholar 

Kong, F.; Stocker, S.; Choi, P.S.; Ma, M.; Ennis, D.B.; Marsden, A. SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects. In ArXiv; United States, 2023.

Beetz, M.; Corral Acero, J.; Banerjee, A.; Eitel, I.; Zacur, E.; Lange, T.; Stiermaier, T.; Evertz, R.; Backhaus, S.J.; Thiele, H.; et al. Interpretable cardiac anatomy modeling using variational mesh autoencoders. Front Cardiovasc Med 2022, 9, 983868. https://doi.org/10.3389/fcvm.2022.983868.

Qiao, M.; Wang, S.; Qiu, H.; De Marvao, A.; O'Regan, D.P.; Rueckert, D.; Bai, W. CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy. IEEE Trans Med Imaging 2023.https://doi.org/10.1109/tmi.2023.3331982.

Campello, V.M.; Xia, T.; Liu, X.; Sanchez, P.; Martín-Isla, C.; Petersen, S.E.; Seguí, S.; Tsaftaris, S.A.; Lekadir, K. Cardiac aging synthesis from cross-sectional data with conditional generative adversarial networks. Front Cardiovasc Med 2022, 9, 983091. https://doi.org/10.3389/fcvm.2022.983091.

Article  PubMed  PubMed Central  Google Scholar 

Vieira, M.S.; Hussain, T.; Figueroa, C.A. Patient-specific image-based computational modeling in congenital heart disease: a clinician perspective. Journal of Cardiology and Therapy 2015, 2, 436-448.

Article  Google Scholar 

Tikenoğulları, O.Z.; Peirlinck, M.; Chubb, H.; Dubin, A.M.; Kuhl, E.; Marsden, A.L. Effects of cardiac growth on electrical dyssynchrony in the single ventricle patient. Comput Methods Biomech Biomed Engin 2023, 1–17. https://doi.org/10.1080/10255842.2023.2222203.

Biffi, C.; Cerrolaza, J.J.; Tarroni, G.; Bai, W.; de Marvao, A.; Oktay, O.; Ledig, C.; Le Folgoc, L.; Kamnitsas, K.; Doumou, G.; et al. Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models. IEEE Trans Med Imaging 2020, 39, 2088-2099. https://doi.org/10.1109/tmi.2020.2964499.

Article  PubMed  PubMed Central  Google Scholar 

Mullins, C.E.; Mayer, D.C. Congenital heart disease: a diagrammatic atlas. (No Title) 1988.

Park, I.S. An Illustrated Guide to Congenital Heart Disease: From Diagnosis to Treatment–From Fetus to Adult; Springer: 2019.

Ottaviani, G.; Buja, L.M. Congenital heart disease: pathology, natural history, and interventions. In Cardiovascular pathology; Elsevier: 2022; pp. 223-264.

Google Scholar 

Singh, S. 9 Capabilities Of DALL-E That One Must Know. Available online: https://www.labellerr.com/blog/dall-e-everything-you-need-to-know/ (accessed on 6 Jan 2024).

AppMaster. Challenges and Limitations: Understanding DALL-E's Capabilities. Available online: https://appmaster.io/blog/challenges-and-limitations-dall-e (accessed on 6 Jan 2024).

Kather, J.N.; Ghaffari Laleh, N.; Foersch, S.; Truhn, D. Medical domain knowledge in domain-agnostic generative AI. NPJ Digit Med 2022, 5, 90. https://doi.org/10.1038/s41746-022-00634-5.

Article  PubMed  PubMed Central  Google Scholar 

Seetharaman, R. Revolutionizing Medical Education: Can ChatGPT Boost Subjective Learning and Expression? In J Med Syst; © 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.: United States, 2023; Volume 47, p. 61.

BaHammam, A.S. Balancing Innovation and Integrity: The Role of AI in Research and Scientific Writing. In Nat Sci Sleep; New Zealand, 2023; Volume 15, pp. 1153–1156.

Temsah, R.; Altamimi, I.; Alhasan, K.; Temsah, M.H.; Jamal, A. Healthcare's New Horizon With ChatGPT's Voice and Vision Capabilities: A Leap Beyond Text. Cureus 2023, 15, e47469. https://doi.org/10.7759/cureus.47469.

Article  PubMed  PubMed Central  Google Scholar 

Alkaissi, H.; McFarlane, S.I. Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. Cureus 2023, 15, e35179. https://doi.org/10.7759/cureus.35179.

Article  PubMed  PubMed Central  Google Scholar 

Aljamaan, F; Temsah, M.H.; Tamimi, I.; Al-Al-Eyadhy, A.; Jamal, A.; Alhasan, K.; Mesallam, T. A.; Farahat, M.; Malki, K. H. AI Chatbots’ Medical Hallucination: Innovation of References Hallucination Score and Comparison of Six Large Language Models (Preprint). 2023. https://doi.org/10.2196/preprints.54345.

Simonsanvil. DALL-E-Explained. Available online: https://github.com/simonsanvil/DALL-E-Explained/blob/main/README.md (accessed on 6 Jan 2024).

OpenAI. Fine-tuning: Learn how to customize a model for your application. Available online: https://platform.openai.com/docs/guides/fine-tuning (accessed on 6 Jan 2024).

Temsah, M.H.; Jamal, A.; Aljamaan, F.; Al-Tawfiq, J.A.; Al-Eyadhy, A. ChatGPT-4 and the Global Burden of Disease Study: Advancing Personalized Healthcare Through Artificial Intelligence in Clinical and Translational Medicine. Cureus 2023, 15, e39384. https://doi.org/10.7759/cureus.39384.

Article  PubMed  PubMed Central  Google Scholar 

Hurrell, L. DALL-E 3 watermark launched by OpenAI to reduce AI misuse. Available online: https://techmonitor.ai/technology/ai-and-automation/dall-e-3-watermark (accessed on 23 April 2024).

Noel, G. Evaluating AI-powered text-to-image generators for anatomical illustration: A comparative study. Anat Sci Educ 2023.https://doi.org/10.1002/ase.2336.

Article  PubMed  Google Scholar 

Goddard, K.; Roudsari, A.; Wyatt, J.C. Automation bias: a systematic review of frequency, effect mediators, and mitigators. J Am Med Inform Assoc 2012, 19, 121-127. https://doi.org/10.1136/amiajnl-2011-000089.

Article  PubMed  Google Scholar 

Preiksaitis, C.; Rose, C. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Med Educ 2023, 9, e48785. https://doi.org/10.2196/48785.

Article  PubMed  PubMed Central  Google Scholar 

Liu, J.; Liu, F.; Fang, J.; Liu, S. The application of Chat Generative Pre-trained Transformer in nursing education. Nurs Outlook 2023, 71, 102064. https://doi.org/10.1016/j.outlook.2023.102064.

Article  PubMed  Google Scholar 

Kim, T.W. Application of artificial intelligence chatbot, including ChatGPT in education, scholarly work, programming, and content generation and its prospects: a narrative review. J Educ Eval Health Prof 2023, 20, 38. https://doi.org/10.3352/jeehp.2023.20.38.

Article  PubMed  Google Scholar 

Abdel Aziz, M.H.; Rowe, C.; Southwood, R.; Nogid, A.; Berman, S.; Gustafson, K. A scoping review of artificial intelligence within pharmacy education. Am J Pharm Educ 2023, 100615. https://doi.org/10.1016/j.ajpe.2023.100615.

Tiwari, A.; Kumar, A.; Jain, S.; Dhull, K.S.; Sajjanar, A.; Puthenkandathil, R.; Paiwal, K.; Singh, R. Implications of ChatGPT in Public Health Dentistry: A Systematic Review. Cureus 2023, 15, e40367. https://doi.org/10.7759/cureus.40367.

Article  PubMed  PubMed Central  Google Scholar 

Padovan, M.; Cosci, B.; Petillo, A.; Nerli, G.; Porciatti, F.; Scarinci, S.; Carlucci, F.; Dell’Amico, L.; Meliani, N.; Necciari, G.; et al. ChatGPT in Occupational Medicine: A Comparative Study with Human Experts. Bioengineering 2024, 11.https://doi.org/10.3390/bioengineering11010057.

Lautrup, A.D.; Hyrup, T.; Schneider-Kamp, A.; Dahl, M.; Lindholt, J.S.; Schneider-Kamp, P. Heart-to-heart with ChatGPT: the impact of patients consulting AI for cardiovascular health advice. Open Heart 2023, 10.https://doi.org/10.1136/openhrt-2023-002455.

Jone, P.-N.; Gearhart, A.; Lei, H.; Xing, F.; Nahar, J.; Lopez-Jimenez, F.; Diller, G.-P.; Marelli, A.; Wilson, L.; Saidi, A.; et al. Artificial Intelligence in Congenital Heart Disease: Current State and Prospects. JACC: Advances 2022, 1, 100153. https://doi.org/10.1016/j.jacadv.2022.100153.

Mohsin, S.N.; Gapizov, A.; Ekhator, C.; Ain, N.U.; Ahmad, S.; Khan, M.; Barker, C.; Hussain, M.; Malineni, J.; Ramadhan, A.; et al. The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases. Cureus 2023, 15, e44374. https://doi.org/10.7759/cureus.44374.

Article  PubMed  PubMed Central  Google Scholar