Ethically Aware Dermatological AI

Abstract

The rising rates of skin cancer across the globe have needed the development of largely automated webbing tools that can effectively identify skin cancer at early stages. These early findings play a prominent part in defining the outgrowth of cancer- stricken individualities. Although former attempts exercising traditional deep literacy algorithms have proven feasible in performing skin cancer groups directly enough, they\'ve proven problematic in that they frequently operate as\" black box\" algorithmsthat constantlyfail in furnishing equal results across different races or skin textures. The current study proposes an advanced complex skin cancer opinion tool that can effectively classify skin cancer as either benign or cancerous with remarkable perfection while at the same time icing that it operates in an equal manner across all races and skin textures. The skin cancer opinion algorithm of choice has been grounded upon a complex model that synergizes the capabilities of Convolutional Neural Networks (CNN) alongside Vision Mills. substantially, EfficientNet- B0 has been employed effectively alongside Vision Mills because of its capabilities in effectively landing original textures alongside complex vascular features of skin lesions while at the same time effectively landing contextual features alongside structural harmony using Vision Mills. This research takes a preventive approach to the ethical use of AI and equitable access to algorithmic resources while treating users fairly. We also recognize that there is a bias towards lighter skin tones in medicaldatasets. The Individual Typology Angle (ITA) is used as a colorimetric measure of skin tone, which does not rely on human observers, thus eliminating subjectivity or inconsistency in labeling. We will also balance the representation of minorities within the Monk Skin Tone (MST) scale by utilizing StyleGAN to createaugments of existing data so we can create high-fidelity examples of different types of pathology on the MST scale. This will result in a robust, unbiased structure for the diagnostic framework when dealing with diverse populations around the world. Finally, to bridge the gap between computational output and physician trust in the results, the framework offers a multi - layered approach to explainability. The use of Grad-CAM visual heatmaps allows physicians to see the morphologic locations that influenced the model\'s decision and to verify the model\'s reasoning for diagnosis. Additionally, we can reduce the risk of errors resulting from the automated diagnosis by incorporating MC dropout techniques as measures of uncertainty, enabling the systems to assess their own confidence level

Introduction

Skin cancer, particularly malignant melanoma, remains a major global health concern, where early detection is critical for survival. Current diagnosis primarily relies on manual dermoscopic examination, which is subjective, experience-dependent, and prone to inter-observer variability. The growing number of cases and limited availability of dermatologists further create diagnostic delays, highlighting the need for standardized and objective early screening tools. Artificial Intelligence (AI)–based systems are therefore proposed as reliable “second-opinion” tools to reduce misdiagnosis, standardize screening, and ease the burden on healthcare systems.

Recent advances in computer-aided diagnosis have shifted from handcrafted features such as the ABCD rule to deep learning–based approaches, especially Convolutional Neural Networks (CNNs). While CNNs achieve strong performance, they struggle with dataset bias, skin-tone imbalance, limited global context understanding, and lack of clinical interpretability. To overcome these limitations, this research proposes a bias-aware hybrid CNN–Transformer framework for automated skin cancer detection.

The proposed system emphasizes equitable performance across skin tones by objectively quantifying pigmentation using the Individual Typology Angle (ITA) and balancing datasets through Latent Diffusion Models that generate synthetic melanoma images for underrepresented darker skin types. A hybrid CNN–Vision Transformer architecture is employed, where CNNs capture fine-grained local textures and Transformers model global lesion patterns such as asymmetry. An attention-guided feature fusion mechanism adaptively balances local and global information for improved classification.

To enhance clinical trust and usability, the framework integrates Grad-CAM for visual interpretability and Monte Carlo Dropout for uncertainty estimation, enabling the model to flag low-confidence predictions for further expert review. Experimental results demonstrate high diagnostic accuracy (over 95%) with minimal performance disparity across skin tones, along with improved sensitivity for melanoma detection in darker skin types.

Overall, the study presents a transparent, equitable, and clinically reliable AI-based screening system that supports dermatologists as a decision-support tool, offering a strong foundation for future mobile and accessible skin cancer screening applications.

Conclusion

The above study has successfully proved that the application of the Hybrid CNN-Transformer model with additional Fairness-Aware Pre-processing techniques along with Explainable AI (XAI) approaches has greatly improved the state-of-the-art for automated skin cancer image classification. As our proposed system has filled the gap between the local textural description and the global context, it reached a maximum accuracy rate of 97.83%, which is much higher than the past models. The combination of ITA and LDMs was critical in mitigating the long-standing issue of equity in dermatological AI. We have reconciled skin-toned bias to below 1.5% and improved sensitivity in darker skin types by 12%, reducing the gap in efficiency over the entire pigmentation range. This is an important pointer to the need to focus on CIE Lab* color space in AI for the purpose of achieving equity in dermatological AI . Moreover, the use of Grad-CAM and Monte Carlo (MC) Dropout makes this model no longer \"black-box\" and instead helps to make this model sufficiently transparent for better decision-making. Our model, with an IoU of 0.88 for lesion localization and an accura cy of 92% for identifying out-of-distribution data, offers strong qualitative and quantitative measures to support diagnosing doctors. Our model was also successful in preventing misclassifications and saved 23% of misclassifications, thereby creating a novel standard for safety in clinical AI. This research presents a strong foundation for the future tools that will be used in dermatology - a new breed of tools that not only perform well but are mathematically just, interpretable, and come with their own limitations as well. Future research will consider expanding this model for rarer forms of non-melanocytic lesions as well as how the architecture may be optimized for real-time execution on mobile edge devices for developing regions as well.

References

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Copyright

Copyright © 2026 Mansi V, Varsha N, Roopika P, Chandana K, Dr. Satish Kumar A, Dr. Venkata Ramana B. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.