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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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A Surgeon's Guide to Machine Learning.

Daniel T Lammers¹, Carly M Eckert², Muhammad A Ahmad³

¹From the Department of Surgery, Madigan Army Medical Center, Tacoma, WA.

Annals of Surgery Open : Perspectives of Surgical History, Education, and Clinical Approaches

|August 28, 2023

Summary

This summary is machine-generated.

Machine learning (ML) offers advanced data modeling for pattern finding, expanding its use in healthcare and surgery. Understanding ML principles, applications, and limitations is crucial for clinicians and researchers.

Keywords:

artificial intelligence machine learning surgery

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Area of Science:

Computer Science
Medical Informatics

Background:

Machine learning (ML) encompasses advanced data modeling techniques distinct from traditional statistical methods.
ML, a subset of artificial intelligence, focuses on pattern recognition using complex algorithms.
The application of ML is growing rapidly within the healthcare sector, impacting clinical practice.

Purpose of the Study:

To provide surgeons with a foundational understanding of applied machine learning.
To guide clinicians in the implementation and research applications of ML techniques.
To facilitate the critical review of scientific publications involving ML.

Main Methods:

This primer introduces core machine learning concepts and principles.
It outlines common ML applications relevant to surgical practice.
Considerations for research and publication review are discussed.

Main Results:

Clinicians can gain an accelerated introduction to ML through this primer.
The content aims to demystify ML, separating it from science fiction portrayals of AI.
Essential knowledge for the appropriate implementation and evaluation of ML in surgery is provided.

Conclusions:

A grasp of ML principles, applications, and limitations is vital for surgical professionals.
This resource serves as a guide for surgeons engaging with ML in practice and research.
Enhanced understanding of ML promotes its responsible integration into healthcare.