January 2024 in “Wiadomości Lekarskie” AI in heart scans improves diagnosis and treatment but has risks like misdiagnosis and high costs.
1 citations
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September 2025 in “Journal of Ultrasound in Medicine” AI can accurately identify some cosmetic fillers in ultrasound images but needs improvement for others.
12 citations
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November 2023 in “Medicine” AI in dermatology is growing rapidly, showing promise in diagnosing skin conditions as accurately as dermatologists.
January 2026 in “JDDG Journal der Deutschen Dermatologischen Gesellschaft” Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
November 1998 in “Hair transplant forum international” The document's conclusion cannot be summarized because the content is not understandable.
10 citations
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August 2011 in “Clinics” The author clarified that Alopecia Areata Incognita (AAI) and diffuse Alopecia Areata (AA) are different conditions and the case discussed was actually AA, not AAI.
4 citations
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November 2023 in “ArXiv.org” A new method improves the accuracy and reliability of language models by up to 42%.
March 2026 in “Journal of Evidence-Based Medicine” AI chatbots can be reliable learning tools for plastic surgery students.
April 2021 in “The journal of investigative dermatology/Journal of investigative dermatology” Patients using social media have mixed feelings about alopecia treatments, noting hair growth but also frustration with treatment recurrence.
April 2021 in “Journal of Investigative Dermatology” An AI photographic device effectively tracked hair growth improvements in women treated for hair loss.
43 citations
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December 1988 in “International Journal of Bio-Medical Computing”
May 2018 in “Hair transplant forum international” The document's conclusion cannot be summarized because the content is not accessible or understandable.
January 2024 in “Wiadomości Lekarskie” Robotic hair transplantation with AI offers more reliable, precise, and efficient hair restoration.
July 2025 in “Journal of Investigative Dermatology” AI-09 is safe, effective, and reduces wrinkles for up to 6 months.
March 2026 in “Frontiers in Medicine” A hybrid model using traditional methods, trichoscopy, and AI improves hair loss assessment.
106 citations
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August 2024 in “Annals of Medicine and Surgery” AI in robotic surgery improves precision and safety but faces cost and ethical challenges.
AI models are effective for detecting alopecia areata but face challenges like explaining results and data bias.
August 2024 in “Clinical and Experimental Dermatology” DALL-E 2 can create realistic hair images but struggles with specific hair disorders.
AI can personalize exercise routines to improve skin health.
July 2025 in “Anais Brasileiros de Dermatologia” The Brazilian version of the Alopecia Areata Quality of Life Index is reliable for assessing patients' quality of life.
1 citations
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December 2025 in “Scientific Reports” A machine learning model can predict alopecia areata early using specific gene markers.
June 2025 in “British Journal of Dermatology” The new AI software predicts melanoma outcomes more accurately than traditional methods.
November 2023 in “Advances and Applications in Statistics” AI can effectively predict COVID-19 mortality risk using patient data.
April 2023 in “Journal of Investigative Dermatology” The AI model somewhat predicts lymph node status in melanoma patients using skin sample images.
January 2009 in “2009 Annual Conference of Japanese Society for Investigative Dermatology, Fukuoka, Japan, December 4-5, 2009”
4 citations
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July 2012 in “Linguistic Annotation Workshop” Root hairs in barley improve growth and zinc uptake in zinc-deficient soil.
3 citations
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May 2023 in “Precision clinical medicine” Researchers found four genes that could help diagnose severe alopecia areata early.
February 2025 in “PubMed” AI-personalized hair loss treatments improved hair growth and scalp health without side effects.
November 1993 in “Hair transplant forum international” The document's conclusion cannot be provided because the document is not readable or understandable.
September 2024 in “arXiv (Cornell University)” Fine-tuned BERT models are better than LLMs for detecting bias in medical data.