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Seeing is Believing: How AI might transform skin cancer education

The integration of digital technologies, e.g., smartphone apps, has been shown to represent an impactful advancement in training for melanoma diagnosis.1

As artificial intelligence (AI) rapidly advances, its integration into dermatology, particularly through Cycle-Consistent Adversarial Networks2 (Cycle-GANs), we present possible new horizons in patient education and skin cancer diagnosis. A GAN functions with two neural networks: a generator creating images and a discriminator evaluating them, producing increasingly realistic results. A Cycle-GAN links two GANs for bidirectional data transformation between domains. GANs have already found a significant application in dermatology, particularly for data augmentation in skin cancer classification models. The GANs have been enhancing the robustness and accuracy of these diagnostic models.3 We propose the application of Cycle-GANs in the transformation of benign dermoscopic images into artificially created malignant counterparts. This technology allows dermatologists to visually demonstrate to patients the subtle differences between benign and malignant lesions using actual dermoscopic images from the patient's body. During skin screening procedures, dermatologists can present both the original benign image and the AI-generated malignant version. This side-by-side comparison aids in explaining why certain lesions do not require excision and what changes to look for in the future, potentially making patients more attentive. Conversely, for lesions appearing malignant, the AI could generate a benign counterpart to illustrate the necessity of excision.

Case Presentation

Figure 1 shows a dermoscopic image of a benign lesion from the ISIC dataset4 and an artificially created malignant counterpart as well as selected frames from a frame interpolation, showing the gradual transformation from the original benign image to the artificial malignancy image.

 Figure 1 Top: Original benign skin lesion from ISIC dataset. Centre: Selected frames from the frame interpolation video generated with Runway (2024 Runway AI, Inc., New York, USA).  Bottom: AI-generated image showing the lesion's potential malignant progression.

Utilizing frame interpolation, we create a seamless and gradual transition from the original benign dermoscopic image to the AI-generated malignant counterpart. This results in a brief video, illustrating the subtle progression of skin changes, potentially enhancing patient understanding of skin cancer indicators. Figure 2 displays the transformation for another example.

Figure 2 Transformation of a second sample benign image into its AI-generated pseudo-malignant counterpart with frame interpolation.

The frames demonstrate the transformation from the dermoscopic image of a benign skin lesion to an artificially generated pseudo-malignant counterpart.

Conclusion

This visual tool goes beyond the traditional ABCDE rule for skin cancer diagnosis, offering a more intuitive and understandable approach. By seeing the potential progression from benign to malignant, patients gain a clearer understanding of their diagnosis, fostering confidence in self-examinations and early detection. Melanoma patient education on skin self-examination improves their self-efficacy. With this, the level of perceived physician support increases.5 ­ Generative AI in dermatology is not just a technological advancement; it could be a step towards empowering patients with a deeper understanding of their skin health. By bridging the gap between complex skin cancer diagnostics and patient comprehension, technology could enhance proactive skin care and early cancer detection. The broader application of this technology could improve patient education across various diseases, that require visual diagnosis. Deploying AI in patient care and education necessitates careful consideration of ethical issues, including patient privacy, data security, and the need for transparent AI decision-making processes.6

 References

  1. Nervil GG, Ternov NK, Vestergaard T, Sølvsten H, Chakera AH, Tolsgaard MG, Hölmich LR; Improving Skin Cancer Diagnostics Through a Mobile App With a Large Interactive Image Repository: Randomized Controlled Trial;

    JMIR Dermatol 2023;6:e48357; doi: 10.2196/48357

  2. -Y. Zhu, T. Park, P. Isola and A. A. Efros, "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 2242-2251, doi: 10.1109/ICCV.2017.244

  3. Eduardo Pérez, Sebastián Ventura, Progressive growing of Generative Adversarial Networks for improving data augmentation and skin cancer diagnosis, Artificial Intelligence in Medicine, Volume 141, 2023, 102556, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2023.102556

  4. Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Langer, S., Lioprys, K., Malvehy, J., Musthaq, S., Nanda, J., Reiter, O., Shih, G., Stratigos, A., Tschandl, P., Weber, J. & Soyer, P. A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci Data 8, 34 (2021). https://doi.org/10.1038/s41597-021-00815-z

  5. Czajkowska, N.C. Hall, M. Sewitch, B. Wang, A. Körner, The role of patient education and physician support in self-efficacy for skin self-examination among patients with melanoma, Patient Education and Counseling, Volume 100, Issue 8, 2017, Pages 1505-1510, ISSN 0738-3991, https://doi.org/10.1016/j.pec.2017.02.020

  6. Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23, 689 (2023). https://doi.org/10.1186/s12909-023-04698-z