AI at the Bedside: How Automated Solutions are Transforming Vitiligo Management

Vitiligo’s primary assessment tools, such as the Vitiligo Area Scoring Index (VASI) and Vitiligo Disease Activity (VIDA) score, are fundamentally semi-quantitative, relying heavily on clinical judgment. This may lead to limitations in accurate monitoring of disease state, clinical management, and therapeutic efficacy. As detailed in a recent review titled “AI at the Bedside: Automated Diagnosis, Progression Tracking, and Treatment Forecasting in Vitiligo,” artificial intelligence (AI) and deep learning algorithms are rapidly emerging as transformative adjuncts.¹

Investigators synthesized the latest research on AI-assisted imaging modalities (clinical photography, dermoscopy, multisource fusion), deep learning algorithms (CNNs, transformers, attention-based models), and explainable AI tools. The primary outcomes included accuracy in diagnosis, reproducibility of progression tracking, and feasibility of treatment response prediction.

Enhanced Diagnostic Precision

The most immediate clinical application of AI in vitiligo management is computer vision for diagnostic support and objective disease burden quantification. Traditional vitiligo assessment often requires specialized modalities like Wood’s lamp examination to detect subtle or subclinical lesions, particularly in lighter skin types. The review synthesizes evidence demonstrating that Convolutional Neural Networks (CNNs), a class of deep learning algorithms, have achieved high diagnostic accuracy, often exceeding 90% in classification studies, for distinguishing vitiligo from clinical mimickers such as pityriasis alba or tinea versicolor.²

Beyond simple classification, AI models excelled at lesion segmentation when compared to human scoring. Using clinical photography and dermoscopic imaging, AI systems can precisely calculate the extent of depigmentation as a percentage of the affected body area. Furthermore, advanced AI tools incorporating multisource fusion techniques (such as combining clinical, dermoscopic, and optical coherence tomography (OCT) data) can detect early-stage or active vitiligo, facilitating even earlier intervention.

Monitoring and Progression Tracking

No matter the level of disease activity, automated progression tracking using serial imaging and deep learning offered superior objectivity and reproducibility compared to conventional measures. According to the authors, AI systems can analyze consecutive images to identify minute changes in lesion size, colorimetric properties, and border characteristics, such as the comet-tail sign or micro-Koebner phenomenon. The integration of OCT with AI is particularly noteworthy, allowing for non-invasive, near-histological resolution assessment of melanocyte destruction and re-pigmentation attempts.

The development of mobile AI applications is expanding the clinical reach of these technologies. These apps can allow patients to capture and upload standardized images for remote monitoring, enabling clinicians to reliably track disease stability and therapeutic response without frequent in-person visits. This paradigm can potentially improve patient engagement and access to specialist care, especially in resource-limited or geographically remote settings.

Treatment Forecasting and Personalized Medicine

One of the most noteworthy future applications detailed in the review is the potential for AI to move beyond mere diagnosis and monitoring toward personalized treatment forecasting. The effectiveness of current vitiligo treatments, such as narrowband ultraviolet B (NB-UVB) phototherapy or Janus kinase (JAK) inhibitors, can vary widely among patients. Predictive AI models are being developed to address this uncertainty.

These models integrate complex multi-modal data—including clinical features (disease duration and subtype), imaging data (lesion activity and hair follicle reserve), and laboratory markers (genetic susceptibility data and inflammatory cytokines)—to predict a patient's likelihood of achieving a target response (VASI-50 or VASI-75) to a specific regimen. By providing quantitative probabilities of success, these predictive algorithms offer clinicians a data-driven approach to therapy selection, minimizing exposure to ineffective treatments and maximizing therapeutic outcomes for each individual patient.

Clinical Outlook

While the findings demonstrate the substantial promise of AI in vitiligo, the study authors outlined several limitations. Current research is frequently constrained by small, heterogeneous datasets and a significant underrepresentation of Fitzpatrick skin types IV to VI, which limits generalizability. Furthermore, large-scale, prospective, randomized controlled trials are needed to validate these findings and thus, potentially lead to routine clinical adoption. Moving forward, researchers highlight 3 areas of focus:

• Standardization of imaging protocols and dataset creation
• Explainability to ensure model transparency and build physician trust
• Regulatory Validation to establish clear, evidence-based guidelines for integrating AI tools into clinical practice

If these challenges are systematically addressed, AI has the potential to become an effective “bedside tool” for providing personalized care for patients with vitiligo.

References

1. SK Gowda, K Manandhar, S Gupta, “AI at the Bedside: Automated Diagnosis, Progression Tracking, and Treatment Forecasting in Vitiligo,” Dermatological Reviews 6 (2025): 1-8, https://doi.org/10.1002/der2.70055.

2. Mazzetto R, Sernicola A, Tartaglia J, Ciolfi C, Alaibac M. Potential of automated image analysis for the measurement of vitiligo lesions. Front Med (Lausanne). 2025;12:1623408. Published 2025 Aug 14. doi:10.3389/fmed.2025.1623408