Exploring the Potential of DALL-E 2 in Pediatric Dermatology: A Critical Analysis.

INTRODUCTION

Artificial intelligence (AI) is becoming increasingly explored for its potential applications in dermatology. Among various AI models, DALL-E 2 (San Francisco, CA: OpenAI), which generates de novoimages from textual inputs, has garnered significant attention for its remarkable photorealism. In our study, we aimed to analyze the performance of DALL-E 2 in the context of dermatology.

METHODS

The following 12 pediatric dermatological conditions common to ages <15 years were selected as tests: acne, atopic dermatitis, contact dermatitis, vitiligo, congenital melanocytic nevus, warts, molluscum contagiosum, seborrheic dermatitis, alopecia areata, infantile hemangioma, impetigo, and dermatophytosis, specifically tinea corporis. Representative morphological descriptions of each diagnosis, along with their corresponding names, were inputted into DALL-E 2 as textual prompts and subsequently compared. The accuracy of the generated images and their alignment with the intended descriptions were assessed.

RESULTS

Among the total of 24 images reported, 18 were photorealistic and six were cartoons. More cartoons were generated when providing the model with morphological descriptions as textual inputs compared to when diagnoses were inputted. While not entirely accurate, acne stood out as the only diagnosis that was the most consistent and closest to the actual diagnosis. Both images of acne portrayed erythematous papules scattered across the face. However, DALL-E 2 resulted in poor performance for the remaining eleven diagnoses. They did not accurately represent the intended diagnoses nor align with their counterpart image. Moreover, most of the generated images featured lighter skin tones.

CONCLUSION

In assessing DALL-E 2's applications in dermatology, our study highlights the need for more domain-specific and demographically inclusive training data in its algorithms to improve performance.