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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Deep Learning Image Processing Models in Dermatopathology.

Apoorva Mehta¹, Mateen Motavaf², Danyal Raza²

¹Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.

Diagnostics (Basel, Switzerland)

|October 16, 2025

Summary

This summary is machine-generated.

Deep learning and artificial intelligence (AI) are revolutionizing dermatopathology with advanced models for slide analysis. Future integration requires addressing dataset bias, ensuring AI interpretability, and adhering to regulations for safe clinical use.

Keywords:

convolutional neural networks (CNNs)dataset bias deep learning dermatopathology foundation models good machine learning practice (GMLP)vision transformers (ViTs)whole-slide imaging (WSI)

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Area of Science:

Dermatopathology
Artificial Intelligence
Deep Learning

Background:

Deep learning models, including convolutional neural networks (CNNs) and transformer-based foundation models, have significantly advanced dermatopathology.
These AI systems enable accurate whole-slide analysis and multimodal data integration in pathology.

Purpose of the Study:

To review the evolution of deep learning architectures in dermatopathology, from early CNNs to current foundation models.
To assess the performance of established and emerging AI models in real-world dermatopathology applications.
To examine barriers to clinical adoption and propose future research directions.

Main Methods:

Literature review synthesizing recent advancements in deep learning architectures for dermatopathology.
Examination of performance benchmarks for deployed and developmental AI models.
Analysis of challenges including dataset bias, AI interpretability, and regulatory hurdles.

Main Results:

AI models demonstrate increasing accuracy in whole-slide analysis and multimodal integration.
Performance benchmarks highlight the capabilities of models like Paige's PanDerm AI, DermAI, and PathAssist Derm.
Key barriers to clinical workflow integration include dataset bias, lack of AI interpretability, and regulatory considerations.

Conclusions:

Deep learning shows immense potential to transform dermatopathology diagnostics.
Overcoming challenges related to data diversity, explainability, and regulatory compliance is crucial for clinical integration.
Future research should focus on developing robust, interpretable, and compliant AI solutions for scalable dermatopathology applications.