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Artificial Intelligence and Its Possible Advantages in Skin Cancer Diagnostics

In: Digital Management and Artificial Intelligence

Author

Listed:
  • Susanna Minder

    (IU International University)

  • Amelie Schweiger

    (IU International University)

Abstract

The digitalization of various industries plays a significant role not only in Germany but also worldwide. It is not limited to sectors such as technology, industry, and education but increasingly impacts healthcare as well. In the healthcare sector, digitalization particularly influences communication, administration, data processing, and patient treatments (German Federal Ministry of Health, 2023). Through structured technical analysis of patient data, new therapies can be discovered, and better chances of recovery can be achieved. Artificial Intelligence (AI) contributes to these developments. AI, as a technical tool, can positively impact various processes. This includes accelerating medical research, introducing novel treatment methods, and optimizing and automating business and treatment processes (Pfannstiel, 2022, p. 1). This article examines the application of artificial intelligence in skin cancer diagnostics. The aim of the research is to identify the advantages of AI-based technologies in diagnostics and to show how this technology can be implemented. The insights and perspectives on the integration of AI in skin cancer diagnosis are captured in this article. The capabilities of precise image recognition and data processing as well as the competence for early detection of skin cancer lesions are included in the analysis. The results provide a comprehensive overview of the potential and positive developments resulting from AI applications in skin cancer diagnosis. These include early detection, reduction of misdiagnosis and increased efficiency.

Suggested Citation

  • Susanna Minder & Amelie Schweiger, 2025. "Artificial Intelligence and Its Possible Advantages in Skin Cancer Diagnostics," Springer Proceedings in Business and Economics, in: Richard C. Geibel & Shalva Machavariani (ed.), Digital Management and Artificial Intelligence, pages 557-568, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-88052-0_44
    DOI: 10.1007/978-3-031-88052-0_44
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