While machine learning-based systems promise to improve patient outcomes, current evidence is insufficient to support the clinical application of these tools. Beyond analytic accuracy and ...

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in clinical trial statistical programming has ...

In this digital era, machine learning transforms cardiovascular disease (CVD) management by enhancing prediction accuracy, ...

This project develops a machine learning-based decision support platform to assist students in making informed academic and career choices. By leveraging personalized recommendations and real-time ...

Feature selection is a critical process in ML that helps eliminate irrelevant or redundant variables, leading to better generalization and model efficiency. The study proposes a Hybrid Feature ...

In today's digital age, artificial intelligence (AI) is revolutionizing healthcare through improved diagnostic capabilities, ...

Despite making up half of the global population, women's health has often been sidelined by traditional health care systems.

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A distinguished pioneer in healthcare artificial intelligence, Shyamakrishna Siddharth Chamarthy has established himself as a ...

Recent advances in AI, including machine learning, natural language processing, and deep learning models, are transforming ...

A more efficient and data-driven approach to clinical asset evaluation is needed. This is where causal machine learning (ML) can step in, helping investors dive deeper into trial data insights to ...

Molecular Devices' CellXpress AI streamlines cell culture processes, reducing human error and improving efficiency in drug ...