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image of Advances in Machine Learning Models for Healthcare Applications: A Precise and Patient-Centric Approach

Abstract

Background

Healthcare is rapidly leveraging machine learning to enhance patient care, streamline operations, and address complex medical issues. Though ethical issues, model efficiency, and algorithmic bias exist, the COVID-19 pandemic highlighted its usefulness in disease outbreak prediction and treatment optimization.

Aim

This article aims to discuss machine learning applications, benefits, and the ethical and practical challenges in healthcare.

Discussion

Machine learning assists in diagnosis, patient monitoring, and epidemic prediction but faces challenges like algorithmic bias and data quality. Overcoming these requires high-quality data, impartial algorithms, and model monitoring.

Conclusion

Machine learning might revolutionize healthcare by making it more efficient and better for patients. Full acceptance and the advancement of technologies to improve health outcomes on a global scale depend on resolving ethical, practical, and technological concerns.

© 2025 Bentham Science Publishers
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2025-02-11
2025-03-30

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/content/journals/cpd/10.2174/0113816128353371250119121315
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Advances in Machine Learning Models for Healthcare Applications: A Precise and Patient-Centric Approach
Bentham Science Publishers ; https://doi.org/10.2174/0113816128353371250119121315
/content/journals/cpd/10.2174/0113816128353371250119121315
/content/journals/cpd/10.2174/0113816128353371250119121315
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  • Article Type:     Review Article
Keywords: Machine learning ; clinical decision support systems ; patient monitoring ; data accuracy ; bias ; neural network ; electronic medical records

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