Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics

Abstract

Background

Artificial intelligence (AI) is transforming dermatopathology by enhancing diagnostic accuracy, efficiency, and precision medicine. Despite its promise, challenges such as dataset biases, underrepresentation of diverse populations, and limited transparency hinder its widespread adoption. Addressing these gaps can set a new standard for equitable and patient-centered care. To evaluate how AI mitigates biases, improves interpretability, and promotes inclusivity in dermatopathology while highlighting novel technologies like multimodal models and explainable AI (XAI).

Results

AI-driven tools demonstrate significant improvements in diagnostic precision, particularly through multimodal models that integrate histological, genetic, and clinical data. Inclusive frameworks, such as the Monk scale, and advanced segmentation methods effectively address dataset biases. However, challenges such as the “black box” nature of AI, ethical concerns about data privacy, and limited access to advanced technologies in low-resource settings remain.

Conclusion

AI offers transformative potential in dermatopathology, enabling equitable, and innovative diagnostics. Overcoming persistent challenges will require collaboration among dermatopathologists, AI developers, and policymakers. By prioritizing inclusivity, transparency, and interdisciplinary efforts, AI can redefine global standards in dermatopathology and foster patient-centered care.