AI at the Bedside: Automated Diagnosis, Progression Tracking, and Treatment Forecasting in Vitiligo

ABSTRACT Background Vitiligo is an acquired depigmenting disorder characterized by selective melanocyte destruction, leading to visible psychosocial burden and diagnostic challenges. Conventional assessment tools such as VASI and VIDA are semi‐quantitative and subjective, with limited reproducibility. Early detection, accurate monitoring, and treatment response prediction remain unmet needs. Artificial intelligence (AI) has emerged as a promising adjunct to enhance diagnostic precision, track disease progression, and forecast therapeutic outcomes. Methods A narrative synthesis of recent studies was undertaken, focusing on AI‐assisted imaging modalities (clinical photography, dermoscopy, multisource fusion), deep learning algorithms (CNNs, transformers, attention‐based models), and explainable AI tools. Primary outcomes included accuracy in diagnosis, reproducibility of progression tracking, and feasibility of treatment response prediction. Results AI‐based tools demonstrated high diagnostic accuracy (> 90% in several studies) in distinguishing vitiligo from mimickers and in lesion segmentation. Automated progression tracking using serial imaging and OCT improved objectivity over conventional indices. Mobile AI applications enabled remote monitoring with reliable lesion detection. Predictive models integrating clinical, imaging, and laboratory data showed potential for individualized treatment forecasting, particularly for NB‐UVB and JAK inhibitors. However, limitations include small, heterogeneous datasets, underrepresentation of skin‐of‐color, lack of prospective trials, and concerns over model transparency. Conclusion AI shows substantial promise in vitiligo by enhancing diagnosis, monitoring progression, and forecasting treatment response. To realize clinical utility, future efforts must address dataset diversity, standardization, explainability, and regulatory validation. AI has the potential to become an effective bedside adjunct for personalized vitiligo management.

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Publication Details

Journal
Dermatological Reviews
Published
2025-11-19
DOI
https://doi.org/10.1002/der2.70055
Primary Topic
melanin and skin pigmentation
Type
article
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article

AI at the Bedside: Automated Diagnosis, Progression Tracking, and Treatment Forecasting in Vitiligo

Keepa Manandhar, Somesh Gupta, Shreya K Gowda
Dermatological Reviews
melanin and skin pigmentation
article

AI at the Bedside: Automated Diagnosis, Progression Tracking, and Treatment Forecasting in Vitiligo

Keepa Manandhar, Somesh Gupta, Shreya K Gowda
article en

Abstract

ABSTRACT Background Vitiligo is an acquired depigmenting disorder characterized by selective melanocyte destruction, leading to visible psychosocial burden and diagnostic challenges. Conventional assessment tools such as VASI and VIDA are semi‐quantitative and subjective, with limited reproducibility. Early detection, accurate monitoring, and treatment response prediction remain unmet needs. Artificial intelligence (AI) has emerged as a promising adjunct to enhance diagnostic precision, track disease progression, and forecast therapeutic outcomes. Methods A narrative synthesis of recent studies was undertaken, focusing on AI‐assisted imaging modalities (clinical photography, dermoscopy, multisource fusion), deep learning algorithms (CNNs, transformers, attention‐based models), and explainable AI tools. Primary outcomes included accuracy in diagnosis, reproducibility of progression tracking, and feasibility of treatment response prediction. Results AI‐based tools demonstrated high diagnostic accuracy (> 90% in several studies) in distinguishing vitiligo from mimickers and in lesion segmentation. Automated progression tracking using serial imaging and OCT improved objectivity over conventional indices. Mobile AI applications enabled remote monitoring with reliable lesion detection. Predictive models integrating clinical, imaging, and laboratory data showed potential for individualized treatment forecasting, particularly for NB‐UVB and JAK inhibitors. However, limitations include small, heterogeneous datasets, underrepresentation of skin‐of‐color, lack of prospective trials, and concerns over model transparency. Conclusion AI shows substantial promise in vitiligo by enhancing diagnosis, monitoring progression, and forecasting treatment response. To realize clinical utility, future efforts must address dataset diversity, standardization, explainability, and regulatory validation. AI has the potential to become an effective bedside adjunct for personalized vitiligo management.

Dermatological ReviewsVol. 6(6)
All India Institute of Medical Sciences Raipur (IN), All India Institute of Medical Sciences Bhopal (IN), All India Institute of Medical Sciences (IN)
Openalex Percentile: Top 63%
melanin and skin pigmentation
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