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<p>Traditional medical diagnostic methods face bottlenecks such as high cost, poor accessibility, and delayed diagnosis in genetic syndromes, neurological disorders, psychiatric disorders, and endocrine disorders. Face-based machine learning (ML) technology provides a new path for early screening of diseases by analyzing facial phenotypes, dynamic expressions, facial skin, and 3D structural abnormalities, and is gradually becoming a clinically assisted screening tool. This paper provides a comprehensive overview of the applications, advances, and challenges of the technology. We summarize the range of diseases for which facial diagnosis is applicable and describe the basic process and related techniques for face-based ML diagnostic systems. In addition, this paper organizes the resources of current publicly available facial medical datasets and clarifies their disease coverage and sample size. Finally, possible future solutions to challenges hindering widespread adoption in clinical practice such as data bias, privacy, interpretability, generalizability, clinical value, and resource constraints are discussed. This review aims to provide researchers with a comprehensive foundation that integrates clinical perspectives, technological insights, and practical resources, to facilitate the development and successful implementation of face-based ML diagnostics in real-world clinical practice.</p>

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Face-based machine learning diagnostics: applications, challenges and opportunities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月13日

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  • Jie Song,
  • Mengqiao He,
  • Xin Zheng,
  • Yuxin Zhang,
  • Cheng Bi,
  • Jinhua Feng,
  • Jiale Du,
  • Hang Li,
  • Bairong Shen

摘要

Traditional medical diagnostic methods face bottlenecks such as high cost, poor accessibility, and delayed diagnosis in genetic syndromes, neurological disorders, psychiatric disorders, and endocrine disorders. Face-based machine learning (ML) technology provides a new path for early screening of diseases by analyzing facial phenotypes, dynamic expressions, facial skin, and 3D structural abnormalities, and is gradually becoming a clinically assisted screening tool. This paper provides a comprehensive overview of the applications, advances, and challenges of the technology. We summarize the range of diseases for which facial diagnosis is applicable and describe the basic process and related techniques for face-based ML diagnostic systems. In addition, this paper organizes the resources of current publicly available facial medical datasets and clarifies their disease coverage and sample size. Finally, possible future solutions to challenges hindering widespread adoption in clinical practice such as data bias, privacy, interpretability, generalizability, clinical value, and resource constraints are discussed. This review aims to provide researchers with a comprehensive foundation that integrates clinical perspectives, technological insights, and practical resources, to facilitate the development and successful implementation of face-based ML diagnostics in real-world clinical practice.