Using the power of artificial intelligence to improve the diagnosis and management of nonmelanoma skin cancer

Abstract

Nonmelanoma skin cancer (NMSC), including basal cell carcinoma and squamous cell carcinoma, is the most prevalent type of skin cancer. While generally less aggressive than melanoma, early detection and treatment are crucial to prevent the complications. Artificial intelligence (AI) systems show promise in enhancing the accuracy, efficiency, and accessibility of NMSC diagnosis and management. These systems can facilitate early interventions, reduce unnecessary procedures, and promote collaboration among healthcare providers. Despite AI algorithms demonstrating moderate-to-high performance in diagnosing NMSC, several challenges remain. Ensuring the robustness, explainability, and generalizability of these models is vital. Collaborative efforts focusing on data diversity, image quality standards, and ethical considerations are necessary to address these issues. Building patient trust is also essential for the successful implementation of AI in the clinical settings. AI algorithms may outperform experts in controlled environments but can fall short in the real-world clinical applications, indicating a need for more prospective studies to evaluate their effectiveness in the practical scenarios. Continued research and development are essential to fully realize AI's potential in improving NMSC diagnosis and management by overcoming the existing challenges and conducting comprehensive studies.