Synthetic data are increasingly being recognized for their potential to address serious real-
world challenges in various domains. They provide innovative solutions to combat the data …

Technological innovations, such as artificial intelligence (AI) and machine learning (ML),
have the potential to expedite the goal of precision medicine, especially when combined …

Widespread interest in artificial intelligence (AI) in health care has focused mainly on
deductive systems that analyze available real-world data to discover patterns not otherwise …

ABSTRACT Generative Artificial Intelligence (GenAI) are algorithms capable of generating
original content. The ability of GenAI to learn and generate novel outputs alike human …

Artificial intelligence tools promise transformative impacts in drug development. Regulatory
agencies face challenges in integrating AI while ensuring reliability and safety in clinical trial …

In the last decades, clinical laboratories have significantly advanced their technological
capabilities, through the use of interconnected systems and advanced software. Laboratory …

Clinical research relies on high-quality patient data, however, obtaining big data sets is
costly and access to existing data is often hindered by privacy and regulatory concerns …

There has been growing interest in the use of real-world data (RWD) to address clinically
and policy-relevant (research) questions that cannot be answered with data from …

Abstract Introduction Artificial Intelligence (AI) algorithms, particularly Deep Learning (DL)
models are known to be data intensive. This has increased the demand for digital data in all …

Background: Synthetic patient data (SPD) generation for survival analysis in oncology trials
holds significant potential for accelerating clinical development. Various machine learning …