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Purpose of Review <p>Suboptimal skin image quality can contribute to diagnostic error in asynchronous teledermatology and by AI models. We review the impact of image quality on diagnostic performance in teledermatology and evaluate artificial intelligence (AI)-based dermatologic image quality assessment (DIQA) systems. The goal is to assess the state of DIQA tools and potential for addressing limitations in skin image acquisition.</p> Recent Findings <p>Deep learning approaches, particularly convolutional neural networks (CNNs), have achieved moderate to high accuracy in detecting specific quality issues. Vision transformers (ViTs), though not as well studied, show promise in capturing complex image features due to global attention mechanisms. Hybrid models and transfer learning have been explored to improve model generalizability, but with limited dermatology-specific data.</p> Summary <p>AI-based DIQA tools offer real-time quality assessment, supporting standardized image acquisition and robust AI diagnostics. Future development requires more diverse datasets, prospective validation, and regulatory compliance to enable clinical integration.</p>

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Artificial Intelligence for Dermatological Image Quality Assessment

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • Philip E. Shih,
  • Dennis H. Oh

摘要

Purpose of Review

Suboptimal skin image quality can contribute to diagnostic error in asynchronous teledermatology and by AI models. We review the impact of image quality on diagnostic performance in teledermatology and evaluate artificial intelligence (AI)-based dermatologic image quality assessment (DIQA) systems. The goal is to assess the state of DIQA tools and potential for addressing limitations in skin image acquisition.

Recent Findings

Deep learning approaches, particularly convolutional neural networks (CNNs), have achieved moderate to high accuracy in detecting specific quality issues. Vision transformers (ViTs), though not as well studied, show promise in capturing complex image features due to global attention mechanisms. Hybrid models and transfer learning have been explored to improve model generalizability, but with limited dermatology-specific data.

Summary

AI-based DIQA tools offer real-time quality assessment, supporting standardized image acquisition and robust AI diagnostics. Future development requires more diverse datasets, prospective validation, and regulatory compliance to enable clinical integration.