Technology
Where It All Began For decades, automation and other traditional forms of artificial intelligence (AI) have surfaced within the medical writing scene as helpful tools, assisting medical writing teams in handling regulatory submissions, clinical trial documentation, and literature reviews. In recent years, the inspiration to further expedite manual processes using technology has been bolstered by…
Read MoreIntroduction: Expanding, Not Eliminating, Medical Writing Roles The conversation around AI in medical writing often centers on headcount reduction—a short-term, cost-cutting approach that overlooks AI’s true potential as well as the value—often times measured intangibly through outcomes like risk reduction—that an experienced medical writer can bring to a document. Instead of focusing on shrinking teams,…
Read MoreIntroduction: AI’s Impact Beyond Headcount Generative AI offers potential efficiencies in drafting various document types in medical writing. However, when facing the opportunity to adopt generative AI, heads of medical writing departments have reported being under immense organizational pressure to demonstrate immediate cost savings by reducing headcount. While AI can certainly streamline processes, focusing solely…
Read MoreArtificial intelligence has transformed technology-enabled medical writing, making complex documentation tasks faster and more efficient. But one of the lesser-known capabilities driving this revolution is zero-shot and few-shot learning—techniques that allow AI models to interpret, categorize, and apply information with minimal or no prior examples. At Synterex, our AI-driven solution, AgileWriter.ai, leverages these advanced learning…
Read MoreThe 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…
Read MoreThe U.S. Food and Drug Administration’s (FDA) draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, offers a comprehensive framework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…
Read MoreThe 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…
Read MoreIn 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…
Read MoreIn 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. As artificial intelligence (AI) becomes more embedded in clinical operations and drug development, traditional QMS…
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