This study explores the capabilities of DALL-E 3, an advanced image generation model integrated with ChatGPT, in generating 12-lead ECGs and teaching illustrations. The researchers found that while DALL-E 3 can produce rudimentary 12-lead ECGs with some basic parameters, the details are not entirely accurate. For example, the heart rate and T waves were often incorrect. However, DALL-E 3 successfully created vivid and accurate illustrations for CPR techniques, emphasizing proper hand placement and technique. The ECG illustrations, though creative, lacked key details and were not physiologically accurate. The study concludes that while DALL-E 3 shows promise for generating basic medical illustrations, it requires significant refinement, additional training data, and expert validation to produce clinically valid outputs. Further development and validation are necessary to address the limitations and ensure reliable use in medical education and patient care settings.This study explores the capabilities of DALL-E 3, an advanced image generation model integrated with ChatGPT, in generating 12-lead ECGs and teaching illustrations. The researchers found that while DALL-E 3 can produce rudimentary 12-lead ECGs with some basic parameters, the details are not entirely accurate. For example, the heart rate and T waves were often incorrect. However, DALL-E 3 successfully created vivid and accurate illustrations for CPR techniques, emphasizing proper hand placement and technique. The ECG illustrations, though creative, lacked key details and were not physiologically accurate. The study concludes that while DALL-E 3 shows promise for generating basic medical illustrations, it requires significant refinement, additional training data, and expert validation to produce clinically valid outputs. Further development and validation are necessary to address the limitations and ensure reliable use in medical education and patient care settings.