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Updated: Jan 8, 2026

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

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

Published on: October 10, 2018

Machine learning and deep learning in internal medicine: demystifying concepts.

Luis Ramos-Ruperto¹, Juan Mora-Delgado², Alejandro Rodríguez-González³

¹Grupo de Trabajo Medicina Digital de la SEMI, Spain; Servicio de Medicina Interna, Hospital Universitario La Paz, Madrid, Spain.

Revista Clinica Espanola

|December 22, 2025

Summary

This summary is machine-generated.

Machine learning (ML), a form of artificial intelligence, offers powerful tools for medical decision-making. This guide introduces internists to ML applications in diagnosis, prognosis, and patient management.

Keywords:

Artificial intelligence Inteligencia artificial Machine learning

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Area of Science:

Artificial Intelligence in Medicine
Clinical Data Analysis
Medical Decision Support

Background:

Machine learning (ML) is revolutionizing clinical practice.
ML tools analyze large datasets to find patterns and make predictions.
This is crucial for modern medical decision-making.

Purpose of the Study:

To provide an accessible introduction to ML concepts for internists.
To highlight ML applications in clinical tasks like diagnosis and prognosis.
To explore advanced ML techniques and ethical considerations.

Main Methods:

Explanation of supervised, unsupervised, and reinforcement learning.
Emphasis on the critical role of data quality in medical ML.
Overview of the systematic process for developing ML projects in healthcare.

Main Results:

ML enhances diagnostic accuracy and treatment personalization.
It aids in optimizing healthcare resource allocation.
Advanced methods like neural networks and explainability are discussed.

Conclusions:

Integrating ML tools can significantly improve clinical outcomes.
A critical and ethical approach is essential when using ML in medicine.
ML empowers clinicians to enhance patient care and operational efficiency.