Synterex Blog

In the rush to adopt artificial intelligence, many teams are tempted by the promise of “one-click” solutions that claim to transform...

AI-enabled medical writing significantly streamlines the clinical documentation process, but common pitfalls can reduce its...

Recent research from Microsoft  offers one of the most comprehensive looks yet at how generative AI is already reshaping work activities...

Once Upon a Prompt  In academic and clinical research, time is a precious commodity—and so is clarity. With artificial intelligence...

Discover how AI and structured content management are transforming regulatory submissions—boosting speed, consistency, and compliance in health authority interactions.

The FDA’s recent Evaluation of Labeling Submissions with AI (ELSA) pilot program has generated both curiosity and skepticism in the regulatory community. Early responses from reviewers using ELSA have been mixed, with some finding it helpful for catching inconsistencies or typos, while others found it redundant or disconnected from their workflows. While the effort is laudable, the ELSA pilot highlights deeper limitations in how general-purpose AI tools perform in complex, re

Integrating technology-assisted content reuse into medical writing can reduce stress for writers by preventing unnecessary rework and maintaining consistent document quality and compliance. This ensures timely delivery of important new therapies to the patients who need them. 

Safety narratives serve as a critical component of clinical trial documentation, providing a comprehensive account of a patient’s...

The advent of AI has brought with it many ethical concerns from medical writers and other stakeholders.

Instead of focusing on shrinking teams, medical writing leaders should use AI to expand their department’s scope and strategic value.

When implemented thoughtfully, AI can enhance job satisfaction, improve digital employee experience (DEx), and foster a great workplace culture.

Zero-shot and few-shot learning are techniques that allow AI models to interpret, categorize, and apply information with minimal or no prior examples.

The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has transformed how medical technology is developed, evaluated, and deployed. This innovation calls for adherence to rigorous quality standards to ensure safety, efficacy, and compliance. While traditional Good Practice (GxP) guidelines—such as Good Manufacturing Practice (GMP), Good Clinical Practice (GCP), and Good Laboratory Practice.

As artificial intelligence (AI) continues to transform clinical trials, ensuring transparency, accountability, and compliance has become critical. Central to achieving these goals is the implementation of a robust audit trail system.

The U.S. Food and Drug Administration's (FDA) draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, offers a comprehensive framework for the integration of artificial intelligence (AI) in drug development. For regulatory professionals, understanding the various engagement options beyond traditional FDA meetings is crucial for effectively navigating AI applications in clinical trials.

The European Union’s Artificial Intelligence Act (EU AI Act) has become a significant milestone in the global regulation of artificial intelligence. As the world’s first comprehensive AI regulation, it introduces a risk-based framework designed to ensure the safe and ethical deployment of AI technologies. For industries relying heavily on innovation, like biopharma clinical trials, understanding this regulation is crucial.

In clinical trials and healthcare, data comprise the foundation of every decision, from designing study protocols to analyzing patient outcomes. As artificial intelligence (AI) increasingly shapes clinical operations, data governance has become a critical factor in ensuring data quality, compliance, and ethical AI deployment. Data governance involves the management of data availability, usability, integrity, and

In healthcare and biopharma, ensuring patient safety, product quality, and regulatory compliance has always been paramount. A Quality Management System (QMS) serves as the backbone for these priorities, providing a structured framework for risk management, process standardization, and continuous improvement.

Digital transformation in the biopharma industry isn't just about adopting the latest technologies—it's about fundamentally reimagining how clinical trials are designed, conducted, and analyzed. This shift involves integrating artificial intelligence (AI), cloud computing, and data analytics to streamline operations, accelerate drug development, and improve patient experiences.

In the evolving landscape of regulatory writing, it has become clear that creating clear and compliant documents is no longer enough. Building off the principles of lean authoring, regulatory writers must now consider holistically the experience of those who will review the documents.