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Advancements in artificial intelligence (AI) and machine learning (ML) have significantly improved health emergency forecasting, disease identification, population analysis, and understanding immune responses. While skepticism remains regarding the practical application and interpretation of ML in healthcare, its use is becoming more widespread. In this article, we provide an overview of ML-based methodologies and teaching strategies, including examples of three types of learning: reinforcement, supervised, and unsupervised. We also explore the application of ML in various medical fields, such as radiology, genetics, neuroimaging, and electronic medical records. Additionally, we address the challenges and risks of applying ML in healthcare, including concerns about system security and ethical considerations, and we offer suggestions for future applications.

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A Study of Artificial Intelligence Using Machine Learning Techniques in Healthcare Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Sonia Bhukra,
  • Pardeep Kumar Jindal,
  • Sharad Sharma,
  • Preeti Sharma

摘要

Advancements in artificial intelligence (AI) and machine learning (ML) have significantly improved health emergency forecasting, disease identification, population analysis, and understanding immune responses. While skepticism remains regarding the practical application and interpretation of ML in healthcare, its use is becoming more widespread. In this article, we provide an overview of ML-based methodologies and teaching strategies, including examples of three types of learning: reinforcement, supervised, and unsupervised. We also explore the application of ML in various medical fields, such as radiology, genetics, neuroimaging, and electronic medical records. Additionally, we address the challenges and risks of applying ML in healthcare, including concerns about system security and ethical considerations, and we offer suggestions for future applications.