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

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.