Over the past decade, the integration of machinelearning (ML) and artificial intelligence (AI) into clinical medicine has accelerated dramatically, reshaping the ways in which clinicians collect, analyze, and interpret health data. Between 2021 and 2025, more than 53,000 publications have addressed AI, deep learning (DL), and ML in medical contexts across nearly all specialties—from ...
Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors.
Machine learning (ML) in health care relies on the collection of patient data. Using systems and tools designed to sort and categorize data, machine learning algorithms can discover patterns in data sets that allow medical professionals to identify new diseases and predict treatment outcomes.
Artificial Intelligence and MachineLearning in Health Care and Medical Sciences: Best Practices and Pitfalls is a comprehensive guide to how AI and ML techniques can best be applied in health care.
How Is MachineLearning Used in Healthcare? Machinelearning is applied in a wide range of healthcare use cases, and much of its promise begins with its ability to handle complex data.
Machinelearning in healthcare is a way for computers to learn patterns from clinical data and make predictions or recommendations that support care. Unlike traditional analytics, which follow fixed rules, machinelearning models adapt as they see more data, which makes them useful for complex tasks like risk scoring, imaging analysis, and ...
This article explores the potential of machinelearning (ML) in transforming healthcare. It provides an overview of ML and its various categories, highlighting.
Accurate and early diagnosis of diseases is still a challenge in healthcare. Recognizing medical conditions and their symptoms is a complex problem. AI can assist clinicians with its data processing capabilities to save time and improve accuracy. [23] Through the use of machinelearning, artificial intelligence can be able to substantially aid doctors in patient diagnosis through the analysis ...
Machinelearning (ML) has emerged as a transformative force in various sectors, and healthcare is no exception. The integration of machinelearning technologies into healthcare systems is revolutionizing how medical professionals diagnose, treat, and manage diseases.
In short, machinelearning is not just a helpful tool—it is becoming an essential partner in the modern practice of medicine. But what exactly is machinelearning? How is it being used across the healthcare spectrum? What are the benefits, limitations, and ethical challenges?