Machine learning in medical applications: A review of state-of-the-art methods

By Mohammad Shehab, Laith Mohammad Abualigah, Q. Shambour, Muhannad A. Abu-Hashem, M. Y. Shambour, A. I. Alsalibi, Amir H. Gandomi. Comput. Biol. Medicine, 2022

470 citations recorded by the source corpus.

Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.

Subfields: Health Information Management, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e6953924b6eb8fb3e62e98e45b8aa6f5a194b90d

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In Radiology, Nuclear Medicine and Imaging

  1. Ehsan Khodapanah Aghdam
  2. Moein Heidari
  3. Dorit Merhof
  4. Reza Azad
  5. Ilker Hacihaliloglu
  6. Amirhossein Kazerouni

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