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Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Healthcare is undergoing a significant transformation driven by advancements in artificial intelligence (AI) and machine learning (ML).
  • These technologies offer novel approaches to clinical decision-making, operational efficiency, and patient care.
  • Understanding the current landscape, challenges, and future potential of AI/ML in medicine is crucial.

Purpose of the Study:

  • To provide a comprehensive review of AI and ML applications in healthcare.
  • To explore current clinical uses, ethical considerations, and future research directions.
  • To emphasize the importance of responsible AI/ML adoption for patient and clinician benefit.

Main Methods:

  • Historical overview of AI/ML in medicine.
  • Glossary of key terms.
  • Review of current clinical applications, including diagnostics, treatment planning, and operations.
  • Examination of ethical, security, and bias concerns.
  • Analysis of case studies and practical impacts.
  • Discussion of regulatory trends and future research priorities.

Main Results:

  • AI and ML are actively reshaping healthcare delivery across various domains.
  • Radiology and pathology are mature fields for AI/ML application, while areas like sepsis prediction and personalized therapy show rapid advancement.
  • Ethical, security, and bias issues require careful consideration for effective implementation.
  • Case studies demonstrate the practical impact of AI/ML tools in clinical settings.

Conclusions:

  • AI and ML hold immense potential to enhance patient care and support clinicians.
  • Responsible and ethical adoption strategies are paramount to realizing these benefits.
  • Continued research and regulatory oversight are necessary to guide the future of AI/ML in healthcare.