20 citations
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December 2017 in “Journal of Investigative Dermatology Symposium Proceedings” Researchers created a fast, accurate computer program to measure hair loss in alopecia areata patients.
April 2025 in “PharmacoEconomics - Open” Patients with Alopecia Areata are willing to trade life duration for better quality of life.
86 citations
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January 2020 in “British Journal of Dermatology” The AA-IGA scale reliably measures treatment success in alopecia areata by considering both clinician and patient views.
November 2025 in “Frontiers in Medicine” The SAALIQ is a reliable tool for measuring the impact of alopecia areata on Spanish-speaking patients' quality of life.
3 citations
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April 2024 in “International Journal of Women’s Dermatology” Access to JAK inhibitor therapy for alopecia areata patients is difficult, especially for racial minorities.
39 citations
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November 2017 in “Journal of The American Academy of Dermatology” The document suggests using standardized methods to track and measure hair loss in alopecia areata, including patient self-assessment and a 50% improvement in specific scores as a treatment goal.
43 citations
,
December 1988 in “International Journal of Bio-Medical Computing” 2 citations
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July 2025 in “Frontiers in Veterinary Science” MicroRNAs and AI can improve cashmere goat hair quality and aid in hair disorder diagnosis.
December 2024 in “Journal of Cutaneous and Aesthetic Surgery” Advanced FUE systems have evolved to improve precision and efficiency in hair transplantation.
September 2023 in “Journal of the American Academy of Dermatology” Combining robotic and specialized tools improves hair transplant results.
May 2011 in “Value in Health” No current patient-reported outcome measure fully meets FDA requirements for alopecia treatments.
November 2006 in “評価・診断に関するシンポジウム講演論文集” KSR1 is crucial for certain skin tumor formation and could be a cancer therapy target.
October 2022 in “Hair Transplantation” Digital imaging tools help accurately assess hair transplant candidates.
November 2023 in “Advances and Applications in Statistics” AI can effectively predict COVID-19 mortality risk using patient data.
5 citations
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January 2025 in “BMC Medical Informatics and Decision Making” Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.
November 2023 in “Journal of Dermatological Science” A new computer tool quickly measures hair thickness differences in people with common types of hair loss.
December 2024 in “Drug Discoveries & Therapeutics” Baricitinib-loaded EVs help hair regrowth in alopecia areata by reducing inflammation and promoting hair follicle regeneration.
November 2025 in “Journal of Investigative Dermatology” Alopecia areata requires addressing both emotional and financial challenges for better patient care.
March 2026 in “Dermatology Online Journal” Medicaid coverage for alopecia areata treatments is inconsistent and often limited.
January 2026 in “JDDG Journal der Deutschen Dermatologischen Gesellschaft” Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
1 citations
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June 2023 in “PubMed” December 2025 in “Value in Health” Alopecia areata significantly strains healthcare and finances in the UAE, needing better management.
2 citations
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December 2024 in “PLoS ONE” Hematological ratios can effectively predict and manage alopecia areata severity.
September 2022 in “Journal of Investigative Dermatology” Patient-reported outcomes better reflect the quality of life impact of alopecia areata than traditional severity scores.
7 citations
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March 2020 in “Journal of King Saud University. Science/Maǧallaẗ ǧāmiʹaẗ al-malik Saʹūd. al-ʹUlūm” AiQingHua oil improves blood flow and promotes hair growth in mice.
26 citations
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February 2016 in “Respiratory Medicine” Auto-antibody testing is a useful but not definitive tool in diagnosing interstitial lung diseases, and using a specific algorithm could make testing more cost-effective.
7 citations
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July 2024 in “Dermatology Practical & Conceptual” SII is a useful and affordable tool to assess and monitor alopecia areata.
February 2026 in “Australasian Journal of Dermatology” A new tool simplifies alopecia areata severity scoring but needs validation.
April 2019 in “The journal of investigative dermatology/Journal of investigative dermatology” Machine learning can predict how well patients with alopecia areata will respond to certain treatments.
July 2023 in “Journal of Cosmetic Dermatology” Practitioners treating hair loss need better education and resources to overcome challenges like commercial bias and ethical dilemmas.