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Clinical Applications of Machine Learning.

Nadayca Mateussi1, Michael P Rogers2, Emily A Grimsley2

  • 1From the Sporedata, Durham, NC.

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Summary

This review familiarizes end users with interpretable machine learning, natural language processing, image recognition, and reinforcement learning. Understanding these artificial intelligence (AI) methods is crucial for future clinical medicine applications.

Keywords:
image recognitioninterpretable predictive machine learningmachine learningnatural language processingreinforcement learning

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Area of Science:

  • Machine Learning in Medicine
  • Artificial Intelligence Applications
  • Clinical Data Analysis

Background:

  • Machine learning (ML), artificial intelligence (AI), and generative AI are increasingly used in clinical medicine.
  • End users require a fundamental understanding of these underlying AI methodologies.

Purpose of the Study:

  • To introduce interpretable predictive ML approaches, natural language processing (NLP), image recognition, and reinforcement learning (RL).
  • To familiarize end users with core AI methodologies driving future clinical applications.

Main Methods:

  • Description of publicly available datasets for interpretable predictive models, NLP, image recognition, and RL.
  • Outline of result interpretation for various analytical frameworks.
  • Provision of references for in-depth information on each analytical framework.

Main Results:

  • Introduction to interpretable predictive machine learning models.
  • Overview of natural language processing methodologies.
  • Introduction to image recognition and reinforcement learning techniques.

Conclusions:

  • Interpretable ML, NLP, image recognition, and RL are foundational AI methodologies for clinical medicine and surgery.
  • End users must understand the strengths and weaknesses of these AI tools for patient care.