Unresolved Challenges and Future Directions in AI-based Skin Cancer Detection: A Review of Data Diversity, Model Interpretability, and Clinical Integration

Abstract

The fast development of artificial intelligence (AI) and machine learning (ML) have made it possible to make substantial advances in automated skin-cancer diagnosis, but its use in practice is still scarce. This paper focuses on three unresolved issues in AI-assisted dermatology by systematically reviewing recent progress in the field, such as data diversity, model interpretability, and clinical integration. The articles reviewed in 20172024 were analyzed according to a systematic search of medical-imaging databases and IEEE/Scopus repositories. The research shows that the main gaps that require attention include the lack of balance of the datasets within the demographics of skin-tone and the obscurity of deep-learning models, as well as regulatory or workflow obstacles as impediments to clinical validation. Thematic synthesis and comparative information about state-of-the-art methods, interpretability techniques, and fairness frameworks are described. Lastly, the review also suggests a roadmap for responsible, explainable, and workflow-centric AI systems in dermatology. The results indicate that success in the future lies not only in the accuracy of the model but also in fairness in the representation of data, clear decision-making, and flawless clinical usability.

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