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Review
. 2025 Feb 4;6(2):101175.
doi: 10.1016/j.patter.2025.101175. eCollection 2025 Feb 14.

AI-assisted facial analysis in healthcare: From disease detection to comprehensive management

Chaoyu Lei  1   2   3 , Kang Dang  4   5 , Sifan Song  4   5 , Zilong Wang  6 , Sien Ping Chew  1   2   3 , Ruitong Bian  1   2   3 , Xichen Yang  1   2   3 , Zhouyu Guan  7 , Claudia Isabel Marques de Abreu Lopes  8 , Mini Hang Wang  9 , Richard Wai Chak Choy  9 , Xiaoyan Hu  9 , Kenneth Ka Hei Lai  9   10 , Kelvin Kam Lung Chong  9   10   11   12 , Chi Pui Pang  9   10   11 , Xuefei Song  1   2   3 , Jionglong Su  4 , Xiaowei Ding  13 , Huifang Zhou  1   2   3
Affiliations
Review

AI-assisted facial analysis in healthcare: From disease detection to comprehensive management

Chaoyu Lei et al. Patterns (N Y). .

Abstract

Medical conditions and systemic diseases often manifest as distinct facial characteristics, making identification of these unique features crucial for disease screening. However, detecting diseases using facial photography remains challenging because of the wide variability in human facial features and disease conditions. The integration of artificial intelligence (AI) into facial analysis represents a promising frontier offering a user-friendly, non-invasive, and cost-effective screening approach. This review explores the potential of AI-assisted facial analysis for identifying subtle facial phenotypes indicative of health disorders. First, we outline the technological framework essential for effective implementation in healthcare settings. Subsequently, we focus on the role of AI-assisted facial analysis in disease screening. We further expand our examination to include applications in health monitoring, support of treatment decision-making, and disease follow-up, thereby contributing to comprehensive disease management. Despite its promise, the adoption of this technology faces several challenges, including privacy concerns, model accuracy, issues with model interpretability, biases in AI algorithms, and adherence to regulatory standards. Addressing these challenges is crucial to ensure fair and ethical use. By overcoming these hurdles, AI-assisted facial analysis can empower healthcare providers, improve patient care outcomes, and enhance global health.

Keywords: artificial intelligence; disease screening; facial analysis; global health; healthcare.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The workflow of AI-assisted facial analysis in disease detection There are four distinct stages of a prototypical AI-assisted facial analysis technology pipeline: face detection, face alignment, face reconstruction, and face recognition.
Figure 2
Figure 2
Diseases detectable by AI-assisted facial analysis Diseases detectable by AI-assisted facial analysis include skin diseases, neuropsychiatric diseases, ophthalmic diseases, genetic diseases, endocrine diseases, cardiovascular diseases, hematological diseases, and digestive diseases.
Figure 3
Figure 3
Comprehensive AI-assisted disease management This includes five aspects: health monitoring, early screening, disease diagnosis, treatment decision-making, and follow-up.
Figure 4
Figure 4
Challenges and future directions They include privacy concerns, unverified accuracy, weak interpretability, model unfairness, and regulation adherence.
See this image and copyright information in PMC

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