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Related Experiment Video

Updated: Jan 11, 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

Supervised Machine Learning and Clinical Decision Support.

Lainey G Bukowiec¹, Yining Lu¹

¹Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA.

|November 8, 2025

Summary

This summary is machine-generated.

Machine learning (ML) offers significant potential to enhance patient care through improved diagnostics and personalized treatments. Addressing challenges like data quality and ethical concerns is crucial for effective human-AI collaboration in healthcare.

Keywords:

Artificial intelligence Clinical decision support Deep learning Machine learning Supervised

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Last Updated: Jan 11, 2026

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

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Machine Learning Applications

Background:

Artificial intelligence (AI), specifically machine learning (ML), presents transformative potential for healthcare.
ML can enhance clinical decision-making, diagnostics, treatment personalization, and operational efficiency.
The integration of ML promises to revolutionize patient care delivery.

Purpose of the Study:

To explore the evolution of supervised learning models in healthcare.
To discuss the applications of classification and regression techniques in clinical settings.
To highlight the challenges and collaborative aspects of implementing AI in patient care.

Main Methods:

Review of supervised learning models, including classification and regression.
Analysis of current and potential applications of ML in clinical decision support.
Discussion of challenges related to data quality, ethics, bias, and privacy.

Main Results:

Supervised learning models demonstrate significant promise for improving healthcare outcomes.
Key applications include enhanced diagnostics and personalized treatment strategies.
Successful implementation requires careful consideration of data integrity and ethical guidelines.

Conclusions:

Machine learning is poised to significantly advance patient care and clinical decision support.
Overcoming challenges in data quality, ethics, and bias is essential for realizing ML's full potential.
Effective human-AI collaboration is vital for the responsible and successful integration of ML in medicine.