Publicly Available Large Language Models for Trichoscopy: A Head-To-Head Comparison With Dermatologists

    January 2026 in “ Diagnostics ”
    Basil Signer, Ali Mokhtari, Simone Cazzaniga … S. Morteza Seyed Jafari
    Studysummary This study reports that publicly available large language models are currently less accurate than human experts in diagnosing trichoscopic images, suggesting the need for further development and specialized training for these AI tools in trichology.
    Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer
    Read the full study on mdpi.com →
    Discuss this study in the Community →

    Research cited in this study 5

    1. Leveraging Deep Neural Networks to Uncover Unprecedented Levels of Precision in the Diagnosis of Hair and Scalp Disorders Skin research and technology · 2024
    2. How Good Is Artificial Intelligence at Solving Hairy Problems? A Review of AI Applications in Hair Restoration and Hair Disorders Dermatologic Therapy · 2021
    3. Hair Tone Estimation at Roots via Imaging Device with Embedded Deep Learning Electronic Imaging · 2019
    4. Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation Journal of Investigative Dermatology Symposium Proceedings · 2017
    5. SALT II: A New Take on the Severity of Alopecia Tool for Determining Percentage Scalp Hair Loss Journal of The American Academy of Dermatology · 2016