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Review
. 2024 Apr 18;5(2):e423.
doi: 10.1097/AS9.0000000000000423. eCollection 2024 Jun.

Clinical Applications of Machine Learning

Nadayca Mateussi  1 , Michael P Rogers  2 , Emily A Grimsley  2 , Meagan Read  2 , Rajavi Parikh  2 , Ricardo Pietrobon  1 , Paul C Kuo  2
Affiliations
Review

Clinical Applications of Machine Learning

Nadayca Mateussi et al. Ann Surg Open. .

Abstract

Objective: This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users.

Background: As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies.

Methods: This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinforcement learning models, outlines result interpretation, and provides references for in-depth information about each analytical framework.

Results: This review introduces interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning methodologies.

Conclusions: Interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning are core machine learning methodologies that underlie many of the artificial intelligence methodologies that will drive the future of clinical medicine and surgery. End users must be well versed in the strengths and weaknesses of these tools as they are applied to patient care now and in the future.

Keywords: image recognition; interpretable predictive machine learning; machine learning; natural language processing; reinforcement learning.

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Figures

Figure 1.
Figure 1.
Confusion Matrix. A confusion matrix is a visual tool used to assess the performance of a classification model. It displays the number of correct and incorrect classifications for each class.
See this image and copyright information in PMC

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