As AI promises to become integral to clinical data management, making thoughtful decisions about its use is critical. Here are ten focused questions to guide your thought process: How will AI improve the accuracy, speed, and efficiency

Understanding Large Language Models (LLMs) and Their Limitations A Large Language Model (LLM), such as ChatGPT, is an extraordinary leap forward in AI, trained to understand and generate human-like text. These models are widely recognized for their

In clinical research, data is our most valuable asset, yet the standards we rely on to manage it—CDISC’s SDTM, ADaM, and CDASH—are outdated and overcomplicated. It’s time to step back and rethink how we structure, store, and

In the ever-evolving world of clinical trials, automation has long been a dream—one that could streamline operations, reduce costs, and accelerate timelines. Yet, for years, traditional machine learning (ML) models promised much but delivered far too little.

The integration of machine learning-based & LLM based AI into clinical trial data management & clinical operations remains elusive, not just operational and technological challenges. The promise of AI transforming clinical trials is yet to unfold. Here

Auto-coding, the process of automatically assigning standardized codes to clinical trial data, has primarily relied on string matching techniques. When data terms match exactly with those in the dictionary such as MedDRA, coding is straightforward and accurate.

Remember when it seemed like clinical trials had to be slow and full of tedious paperwork? We all believed that manual data entry and constant human oversight were essential to ensure everything was accurate and safe. But

The adoption of Large Language Models (LLMs), is on the horizon in clinical trial, promising significant productivity improvements in both data management and clinical operations. While AI shows great potential, its integration into clinical workflows faces several