ReviewJournal of cosmetic dermatology2025
The Use of Light-Based Therapies in the Treatment of Alopecia.
Review in Journal of cosmetic dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Microneedle-mediated intelligent combination delivery for hair regeneration: A comprehensive review of technologies, mechanistic evidence, and translational prospects.Materials today. Bio · 2026Review
- Botulinum Toxin A Myths, Evidence, and Global Media Narratives.Aesthetic plastic surgery · 2026Review
- The Use of Light-Based Therapies in the Treatment of Alopecia.Journal of cosmetic dermatology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlopecia encompasses a group of conditions that can significantly impact quality of life, especially among women and younger individuals. While pharmaceutical therapies remain the cornerstone of treatment, laser and light-based therapies, especially low-level light therapy (LLLT), offer promising noninvasive alternatives. LLLT uses specific wavelengths of light to stimulate hair follicle repair, prolong the hair growth phase, and promote regrowth. Recent advancements in at-home devices and dual-wavelength LED systems have expanded access to these therapies. This review explores the role of LLLT in treating alopecias, evaluating its mechanisms, efficacy, and clinical applications.
methodsA PubMed search using terms related to alopecia and laser/light therapy was conducted. Results were limited to English-only articles from 2020 to 2025, excluding duplicates. Additional articles were identified through citation tracking. Studies not focused on light-based therapies were excluded.
resultsOf 403 articles identified, 63 were included based on relevance to alopecia or hair loss and light-based therapy. Results were categorized by treatment modality and type of hair loss, with overlap between categories. DISCUSSION: This review highlights the growing role of LLLT as an adjunct or alternative treatment for various types of alopecia. In androgenetic alopecia, LLLT improves hair density and follicular responsiveness, with enhanced outcomes when combined with minoxidil or finasteride. For telogen effluvium, LLLT shows potential in prolonging the anagen phase and reducing shedding, although larger studies are needed. In alopecia areata, LLLT may promote regrowth by modulating immune responses and improving perifollicular microcirculation. Emerging data also support LLLT in lichen planopilaris and central centrifugal alopecia (CCCA), with case reports showing reduced inflammation and hair regrowth. Overall, LLLT offers a noninvasive, well-tolerated option across alopecia subtypes, though standardized protocols and long-term data remain limited.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.