Generative Artificial Intelligence in Dermatology: A Primer.
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| Název: | Generative Artificial Intelligence in Dermatology: A Primer. |
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| Autoři: | Kantor J; Department of Medicine, Florida State University College of Medicine, Tallahassee, FL 32304, USA; Center for Global Health; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Engineering Science, University of Oxford, Oxford, UK. Electronic address: jonkantor@gmail.com. |
| Zdroj: | Dermatologic clinics [Dermatol Clin] 2025 Oct; Vol. 43 (4), pp. 603-609. Date of Electronic Publication: 2025 Jul 05. |
| Způsob vydávání: | Journal Article; Review |
| Jazyk: | English |
| Informace o časopise: | Publisher: Elsevier Health Sciences Division Country of Publication: United States NLM ID: 8300886 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1558-0520 (Electronic) Linking ISSN: 07338635 NLM ISO Abbreviation: Dermatol Clin Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2005->: Philadelphia, PA : Elsevier Health Sciences Division Original Publication: Philadelphia : Saunders, c1983- |
| Výrazy ze slovníku MeSH: | Dermatology*/methods , Artificial Intelligence*, Humans ; Generative Artificial Intelligence |
| Abstrakt: | Competing Interests: Disclosure None. The rise of advanced transformer-based generative artificial intelligence models represents one of the most profound changes to the medical landscape of the past century. Given the potential to impact everything from medical education to surgical decision-making, it is important to appreciate fundamental aspects of how these systems work and understand the ways in which they can be used responsibly to improve patient care. Prompt engineering provides the most obvious opportunity for clinicians to tailor these systems to their needs, but appreciating possible risks, including direct and indirect effects, is of profound importance for the clinician, educator, and researcher. (Copyright © 2025 Elsevier Inc. All rights reserved.) |
| Contributed Indexing: | Keywords: Artificial intelligence; ChatGPT; Epidemiology; Gemini; Generative artificial intelligence; Machine learning; Neural networks |
| Entry Date(s): | Date Created: 20251015 Date Completed: 20251015 Latest Revision: 20251015 |
| Update Code: | 20251016 |
| DOI: | 10.1016/j.det.2025.05.007 |
| PMID: | 41093482 |
| Databáze: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Disclosure None.<br />The rise of advanced transformer-based generative artificial intelligence models represents one of the most profound changes to the medical landscape of the past century. Given the potential to impact everything from medical education to surgical decision-making, it is important to appreciate fundamental aspects of how these systems work and understand the ways in which they can be used responsibly to improve patient care. Prompt engineering provides the most obvious opportunity for clinicians to tailor these systems to their needs, but appreciating possible risks, including direct and indirect effects, is of profound importance for the clinician, educator, and researcher.<br /> (Copyright © 2025 Elsevier Inc. All rights reserved.) |
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| ISSN: | 1558-0520 |
| DOI: | 10.1016/j.det.2025.05.007 |