Can DALL-E 3 Reliably Generate 12-Lead ECGs and Teaching Illustrations?

Lingxuan Zhu; Weiming Mou; Keren Wu; Jian Zhang; Peng Luo

doi:10.7759/cureus.52748

Can DALL-E 3 Reliably Generate 12-Lead ECGs and Teaching Illustrations?

Cureus. 2024 Jan 22;16(1):e52748. doi: 10.7759/cureus.52748. eCollection 2024 Jan.

Authors

Lingxuan Zhu¹, Weiming Mou², Keren Wu¹, Jian Zhang¹, Peng Luo¹

Affiliations

¹ Department of Oncology, Zhujiang Hospital of Southern Medical University, Guangzhou, CHN.
² Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, CHN.

Abstract

The recent integration of the latest image generation model DALL-E 3 into ChatGPT allows text prompts to easily generate the corresponding images, enabling multimodal output from ChatGPT. We explored the feasibility of DALL-E 3 for drawing a 12-lead ECG and found that it can draw rudimentary 12-lead electrocardiograms (ECG) displaying some of the parameters, although the details are not completely accurate. We also explored DALL-E 3's capacity to create vivid illustrations for teaching resuscitation-related medical knowledge. DALL-E 3 produced accurate CPR illustrations emphasizing proper hand placement and technique. For ECG principles, it produced creative heart-shaped waveforms tying ECGs to the heart. With further training, DALL-E 3 shows promise to expand easy-to-understand visual medical teaching materials and ECG simulations for different disease states. In conclusion, DALL-E 3 has the potential to generate realistic 12-lead ECGs and teaching schematics, but expert validation is still needed.

Keywords: 12-lead ecg; artificial intelligence in medicine; chatgpt; dall-e 3; medical education.