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Introduction to Machine Learning, Neural Networks, and Deep Learning.

Rene Y Choi1, Aaron S Coyner2, Jayashree Kalpathy-Cramer3

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This review explains machine learning (ML) and deep learning (DL) methods in medical research for a non-technical audience. It highlights the potential and challenges of artificial intelligence in medicine.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly utilized in medical research.
  • Understanding these complex computational methods is crucial for advancing healthcare.

Purpose of the Study:

  • To provide a non-technical overview of current ML methods in medical research.
  • To explain select ML techniques, best practices, and deep learning (DL) for a general audience.
  • To elucidate the potential and challenges of AI in the medical field.

Main Methods:

  • A systematic literature search was conducted in PubMed.
  • The search focused on artificial intelligence methods applied in medicine, with a specific emphasis on ophthalmology.

Main Results:

  • The review covers ML and DL methodology, making it accessible to readers without extensive programming backgrounds.
  • Key ML techniques and best practices relevant to medical research are discussed.

Conclusions:

  • Artificial intelligence holds significant promise for the future of medicine.
  • Despite its potential, numerous challenges must be addressed for widespread AI adoption in healthcare.