Artificial Intelligence (AI) in Modern Dermatopathology Practice

Abstract

Nazik Abed, Marwah Mahfoudh, Sami Al Abadie, Faris Kubba and Mohammed S Al Abadie

The integration of Artificial Intelligence is fundamentally reshaping the field of dermatopathology, which traditionally suffers from high interobserver variability and reliance on subjective interpretations of histopathological features, especially in complex melanocytic lesions and the diagnostic "gray zone". Leveraging computer vision techniques, primarily through CNNs, and relying on high-resolution WSI derived from digital pathology workflows, AI models are achieving performance comparable to or exceeding human experts in many classification tasks. Furthermore, AI provides advanced capabilities such as predictive analytics and prognostic assessments through the integration of multimodal data. AI also drastically optimizes clinical workflows and reduces turnaround times by automating repetitive tasks, including mitotic figure counting, classifying diseases, and aiding in diagnosis decision-making. Despite these achievements, significant barriers to widespread clinical integration remain, notably the scarcity of adequately diverse and annotated training datasets, which can result in algorithmic bias in diverse populations.

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