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Purpose of Review <p>This review explores the potential of artificial intelligence (AI) and machine learning (ML) in enhancing geriatric anesthesia education, highlighting the need for a paradigm shift in training and supporting anesthesiologists in the era of digital transformation.</p> Recent Findings <p>AI and ML offer promising solutions for personalized learning experiences, real-time feedback, and data-driven decision-making in geriatric anesthesia education. Integrating AI simulations, adaptive learning modules, and AI-driven curricula can significantly enhance clinical skills and promote patient safety.</p> Summary <p>The integration of AI and ML in geriatric anesthesia education presents opportunities for adaptive and precision learning, enhanced proficiency in complex care coordination, and continuous professional development. Ethical considerations and the need for a collaborative environment are emphasized. Preparing anesthesiologists for AI integration requires focused education, comprehensive training, and a patient-centered approach. The synergy between AI capabilities and human expertise holds the potential to revolutionize geriatric anesthesia education and practice.</p>

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The Promise of Artificial Intelligence and Machine Learning in Geriatric Anesthesiology Education: An Idea Whose Time Has Come

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月6日

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  • Larry F. Chu,
  • Viji Kurup

摘要

Purpose of Review

This review explores the potential of artificial intelligence (AI) and machine learning (ML) in enhancing geriatric anesthesia education, highlighting the need for a paradigm shift in training and supporting anesthesiologists in the era of digital transformation.

Recent Findings

AI and ML offer promising solutions for personalized learning experiences, real-time feedback, and data-driven decision-making in geriatric anesthesia education. Integrating AI simulations, adaptive learning modules, and AI-driven curricula can significantly enhance clinical skills and promote patient safety.

Summary

The integration of AI and ML in geriatric anesthesia education presents opportunities for adaptive and precision learning, enhanced proficiency in complex care coordination, and continuous professional development. Ethical considerations and the need for a collaborative environment are emphasized. Preparing anesthesiologists for AI integration requires focused education, comprehensive training, and a patient-centered approach. The synergy between AI capabilities and human expertise holds the potential to revolutionize geriatric anesthesia education and practice.