The Promises and Perils of Foundation Models in Dermatology

基础(证据) 皮肤病科 医学 政治学 法学
作者
Haiwen Gui,Jesutofunmi A. Omiye,Crystal T. Chang,Roxana Daneshjou
出处
期刊:Journal of Investigative Dermatology [Elsevier]
标识
DOI:10.1016/j.jid.2023.12.019
摘要

Foundation models (FM), which are large-scale artificial intelligence (AI) models that can complete a range of tasks, represent a paradigm shift in AI. These versatile models encompass large language models, vision-language models, and multimodal models. Although these models are often trained for broad tasks, they have been applied either out of the box or after additional fine tuning to tasks in medicine, including dermatology. From addressing administrative tasks to answering dermatology questions, these models are poised to have an impact on dermatology care delivery. As FMs become more ubiquitous in health care, it is important for clinicians and dermatologists to have a basic understanding of how these models are developed, what they are capable of, and what pitfalls exist. In this paper, we present a comprehensive yet accessible overview of the current state of FMs and summarize their current applications in dermatology, highlight their limitations, and discuss future developments in the field.

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