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会议介绍:临床医学中的人工智能与机器学习——人机界面的生成式与交互式系统

Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface.

作者信息

Nateghi Haredasht Fateme, Kim Dokyoon, Romano Joseph D, Tison Geoff, Daneshjou Roxana, Chen Jonathan H

机构信息

Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA,

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA,

出版信息

Pac Symp Biocomput. 2025;30:33-39. doi: 10.1142/9789819807024_0003.

Abstract

Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems. However, thorough research is essential to fully understand the broader impact and potential consequences of deploying AI within the healthcare sector.

摘要

人工智能(AI)技术越来越有能力处理复杂的多层数据集。生成式人工智能和深度学习的创新显著增强了从非结构化文本、图像以及结构化数据中提取见解的能力。人工智能技术的这些突破激发了医学领域的一波研究热潮,催生了各种旨在改善临床决策、患者监测、图像分析和应急响应系统的工具。然而,进行全面研究对于充分理解在医疗保健领域部署人工智能的更广泛影响和潜在后果至关重要。

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