Clinical Applications of Machine Learning
Nadayca Mateussi1, Michael P Rogers2, Emily A Grimsley2
1From the Sporedata, Durham, NC.
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.
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