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Machine learning in medicine: what clinicians should know.

Jordan Zheng Ting Sim1, Qi Wei Fong2, Weimin Huang3

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Artificial intelligence (AI) and machine learning (ML) are transforming medicine. Physicians should understand AI/ML to leverage these tools for enhanced decision-making and research, rather than viewing them as competitors.

Keywords:
Algorithmsartificial intelligencedeep learningmachine learningneural networks

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Artificial intelligence (AI) is increasingly capable of complex tasks with significant results.
  • Machine learning (ML), a key subset of AI, is poised to become integral to medical practice.
  • Understanding AI and ML is crucial for physicians to adapt to evolving healthcare technologies.

Purpose of the Study:

  • To introduce fundamental concepts and terminology in AI and ML for medical professionals.
  • To demystify common AI/ML algorithms like neural networks, deep learning, and decision trees.
  • To explore the applications of AI/ML in computer vision and natural language processing within medicine.

Main Methods:

  • Review of core AI and ML concepts and algorithms.
  • Illustrative examples of AI/ML applications in medical contexts.
  • Discussion of current machine capabilities and limitations in healthcare.

Main Results:

  • AI/ML algorithms, including neural networks and decision trees, are applicable to medical tasks.
  • Machines are currently augmenting physician decision-making processes.
  • AI/ML demonstrates potential in areas like computer vision and natural language processing for medicine.

Conclusions:

  • Physicians should embrace AI and ML as enabling tools, not competitors.
  • The impact of ML on medical practice and research is significant, with ongoing advancements.
  • The feasibility of full machine autonomy in medicine requires further consideration of current capabilities and limitations.