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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Machine Learning in Medicine.

¹From Cardiovascular Research Institute, Department of Medicine and Institute for Human Genetics, University of California, San Francisco, and California Institute for Quantitative Biosciences, San Francisco. rahul.deo@ucsf.edu.

|November 18, 2015

Summary

This summary is machine-generated.

Machine learning shows promise in healthcare, but clinical impact is limited. This review explores potential applications and obstacles to integrating machine learning into medical practice.

Keywords:

artificial intelligence computers prognosis risk factors statistics

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Area of Science:

Computer Science
Medical Informatics
Data Science

Background:

Advances in computing power, data availability, and algorithms have enabled machine learning (ML) to excel in diverse fields.
ML applications are rapidly expanding across industries, including a growing interest in healthcare analytics.

Purpose of the Study:

To review potential medical applications for machine learning.
To introduce fundamental ML concepts using literature examples.
To identify barriers hindering ML's clinical adoption and propose solutions.

Main Methods:

Literature review of machine learning applications in medicine.
Analysis of existing research on ML in healthcare.
Identification of challenges and potential strategies for overcoming them.

Main Results:

Despite available data and algorithms, ML has had limited impact on clinical care compared to other industries.
Thousands of papers apply ML to medical data, but few translate to meaningful clinical improvements.

Conclusions:

Significant obstacles prevent the widespread clinical adoption of machine learning in medicine.
Addressing these barriers is crucial for realizing ML's potential to transform healthcare practices.