From Diagnosis To Prognosis: A Scoping Review Of Artificial Intelligence In Alopecia Research

January 2026 in “ Open Science Framework ”

Timothy Musharu

alopecia artificial intelligence machine learning convolutional neural networks image-based diagnosis prognostic frameworks treatment-response prediction personalized prognosis equitable AI tools AI ML CNNs

TLDR AI in alopecia research needs better tools for predicting treatment outcomes and ensuring fairness.

This scoping review explores the application of artificial intelligence (AI) and machine learning (ML) in alopecia research, focusing on diagnosis, severity assessment, prognosis, and management. It identifies gaps in prognostic modeling, fairness, and privacy-preserving approaches, emphasizing the need for demographic considerations in AI/ML applications. The review synthesizes evidence from 75 to 150 studies, highlighting methodological rigor and areas for future investigation. It includes studies from 2012 onwards, resulting in 2,068 unique records, and employs a structured protocol with automated triage and human screening. The review aims to map the landscape of AI in alopecia research, providing a comprehensive overview without inferring causal relationships.

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