Your browser doesn't support javascript.
loading
Artificial intelligence in the detection of skin cancer.
Beltrami, Eric J; Brown, Alistair C; Salmon, Paul J M; Leffell, David J; Ko, Justin M; Grant-Kels, Jane M.
Afiliación
  • Beltrami EJ; University of Connecticut School of Medicine, Farmington.
  • Brown AC; Dermatology Surgery Unit, SkinCentre, New Zealand.
  • Salmon PJM; Dermatology Surgery Unit, SkinCentre, New Zealand.
  • Leffell DJ; Department of Dermatology, Yale School of Medicine, New Haven, Connecticut.
  • Ko JM; Department of Dermatology, Stanford Medicine, California.
  • Grant-Kels JM; Department of Dermatology, University of Connecticut School of Medicine, Farmington; University of Florida College of Medicine, Gainesville. Electronic address: grant@uchc.edu.
J Am Acad Dermatol ; 87(6): 1336-1342, 2022 12.
Article en En | MEDLINE | ID: mdl-35998842
ABSTRACT
Recent advances in artificial intelligence (AI) in dermatology have demonstrated the potential to improve the accuracy of skin cancer detection. These capabilities may augment current diagnostic processes and improve the approach to the management of skin cancer. To explain this technology, we discuss fundamental terminology, potential benefits, and limitations of AI, and commercial applications relevant to dermatologists. A clear understanding of the technology may help to reduce physician concerns about AI and promote its use in the clinical setting. Ultimately, the development and validation of AI technologies, their approval by regulatory agencies, and widespread adoption by dermatologists and other clinicians may enhance patient care. Technology-augmented detection of skin cancer has the potential to improve quality of life, reduce health care costs by reducing unnecessary procedures, and promote greater access to high-quality skin assessment. Dermatologists play a critical role in the responsible development and deployment of AI capabilities applied to skin cancer.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias Cutáneas / Inteligencia Artificial Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: J Am Acad Dermatol Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias Cutáneas / Inteligencia Artificial Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: J Am Acad Dermatol Año: 2022 Tipo del documento: Article