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Artificial intelligence in dermatology: advancements and challenges in skin of color.
Fliorent, Rebecca; Fardman, Brian; Podwojniak, Alicia; Javaid, Kiran; Tan, Isabella J; Ghani, Hira; Truong, Thu M; Rao, Babar; Heath, Candrice.
Afiliação
  • Fliorent R; Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA.
  • Fardman B; Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA.
  • Podwojniak A; Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA.
  • Javaid K; Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA.
  • Tan IJ; Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
  • Ghani H; Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Truong TM; Center for Dermatology, Rutgers Robert Wood Johnson, Somerset, NJ, USA.
  • Rao B; Center for Dermatology, Rutgers Robert Wood Johnson, Somerset, NJ, USA.
  • Heath C; Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA.
Int J Dermatol ; 63(4): 455-461, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38444331
ABSTRACT
Artificial intelligence (AI) uses algorithms and large language models in computers to simulate human-like problem-solving and decision-making. AI programs have recently acquired widespread popularity in the field of dermatology through the application of online tools in the assessment, diagnosis, and treatment of skin conditions. A literature review was conducted using PubMed and Google Scholar analyzing recent literature (from the last 10 years through October 2023) to evaluate current AI programs in use for dermatologic purposes, identifying challenges in this technology when applied to skin of color (SOC), and proposing future steps to enhance the role of AI in dermatologic practice. Challenges surrounding AI and its application to SOC stem from the underrepresentation of SOC in datasets and issues with image quality and standardization. With these existing issues, current AI programs inevitably do worse at identifying lesions in SOC. Additionally, only 30% of the programs identified in this review had data reported on their use in dermatology, specifically in SOC. Significant development of these applications is required for the accurate depiction of darker skin tone images in datasets. More research is warranted in the future to better understand the efficacy of AI in aiding diagnosis and treatment options for SOC patients.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Dermatologia Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Dermatologia Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article