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Updated: Jun 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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复杂性科学对人工智能/机器学习的潜力进行了什么预测,以改善初级保健

Richard A Young1, Carmel M Martin1, Joachim P Sturmberg1

  • 1From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).

Journal of the American Board of Family Medicine : JABFM
|May 13, 2024
PubMed
概括

人工智能 (AI) 和机器学习 (ML) 在特定任务的初级保健中表现有前途,但在复杂的案例和数据不一致方面扎. 仔细的开发和测试对于有效的AI/ML集成至关重要.

关键词:
人工智能的人工智能临床决策 - 临床决策复杂性科学 复杂性科学信息技术 信息技术 信息技术机器学习 机器学习医疗信息学 医疗信息学主要护理医生 主要护理医生初级医疗保健是一项主要的医疗保健.提高质量 提高质量

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