Assessing the Anatomical Accuracy of AI-Generated Medical Illustrations: A Comparative Study of Text-to-Image Generator Tools in Anatomy Education.

Abstract

Historically, human anatomy education has been an essential part of medical training, depending on cadaveric dissection and anatomical representations. However, financial and ethical limitations have resulted in a decline in conventional teaching techniques, necessitating the investigation of alternative resources such as digital drawings and artificial intelligence (AI). The aim of this research was to assess and compare the anatomical precision of graphics produced by four AI text-to-image generators: Microsoft Bing, DeepAI, Freepik, and Gemini, emphasizing their value in medical education. On February 6, 2025, four AI text-to-image generators were used. Prompts for creating intricate anatomical images included the human heart, brain, skeletal thorax, and hand bones. Two anatomists and a radiologist evaluated the pictures produced according to anatomical standards. Bing and Gemini generated anatomically correct representations of the human heart, but DeepAI and Freepik were less accurate. All generators offered accurate reconstructions of the human brain; however, there were disparities in sulci and gyri, with Gemini performing best. Only Gemini delivered a correct sternum; the other generators misrepresented the rib count. The Gemini platform provided a satisfactory depiction of the human hand skeleton, but the outputs from other text-to-image generators were not anatomically accurate. This work examines the potential of generative AI in medical illustration, noting significant limitations in accuracy and detail, especially with bony structures. Although AI accelerates the drawing process, it cannot replace the proficiency of skilled medical illustrators. Continuous assessment and improvement of AI-generated material are essential to ensure that the criteria mandated for medical education are met.