The U.S. Chamber of Commerce Foundation (USCC) has named Synterex a 2026 Citizens Awards finalist in the Best Corporate Steward: Small and Middle-Market Business category.

Retrieval errors matter in regulatory document automation because the problem often begins before an AI system writes a sentence.

One of the most persistent risks in AI-assisted medical writing is not obviously incorrect content. It is content that appears confident, coherent, and professionally written while subtly distorting evidence, context, or interpretation.

Imagine assembling an IKEA piece of furniture. With the instruction manual written in legal jargon. In Swedish. Maybe you decide to call a friend, whose knowledge of Swedish engineering stops at perfectly constructed round meatballs, or maybe you give up and decide to just wing it. That’s often how patients feel when handed a traditional informed consent form: distressed, overwhelmed, and unsure if they’re putting all the pieces together right. Now imagine if that manual was

An effective AI medical writing validation process begins before validation activities, testing protocols, or performance metrics are discussed.

Synterex has been awarded a Gold medal by EcoVadis, one of the world's most trusted providers of business sustainability ratings.

What This Means for Medical Writers LinkedIn’s addition of a reporting option for “AI slop” is a useful signal for medical writers because it reflects a broader shift in how readers evaluate content quality. The concern is not limited to whether text was generated by AI. The more important question is whether the text is accurate, traceable, appropriately contextualized, and fit for purpose. In regulated environments, AI evaluation in medical writing is also an ethical respo

What This Means for Medical Writers Transformer models matter for regulatory writing because they make visible what expert writers already know: attention is limited, uneven, and highly dependent on structure. For AI-assisted document workflows in clinical ops, clear sectioning, consistent terminology, and deliberate emphasis help both AI systems and human reviewers focus on the information that matters most. When people talk about AI in writing, they tend to focus on spe

What AI Evaluation Means for Medical Writing AI evaluation in medical writing requires more than asking whether an output “looks good.” A strong AI medical writing validation process needs explicit criteria for accuracy, consistency, traceability, and defensibility before AI-assisted content can be considered acceptable. One of the quiet challenges in AI-assisted medical writing is evaluation. Not whether AI can produce usable text but how teams decide whether that text i

Many AI initiatives falter because organizations assume AI will fit neatly into existing processes. This plug-and-play mindset frames AI adoption in pharma as a technical fix, when the real constraints lie elsewhere. The limiting factors are rarely algorithms or data pipelines. They are legacy governance structures, entrenched review behaviors, and cultural resistance shaped by years of regulatory scrutiny. Without addressing how decisions are validated and documented, AI sim

In medical writing and regulatory environments, AI is often introduced as a visible innovation—something to demonstrate, showcase, or highlight in presentations. Yet the most durable value of enterprise-ready AI for medical writing does not come from attention-grabbing features. It comes from AI that is embedded so deeply into workflows that it fades from view. In regulated contexts, success is rarely associated with novelty. It is associated with reliability, traceability,

AI Adoption Is Advancing—But Not in the Way the Market Narrative Suggests The 2026 State of Validation study shows that AI is no longer speculative in validation and quality environments. Nearly 40% of respondents report that their organizations are already using or actively evaluating AI in GxP settings, and a clear majority expect AI to become standard practice within the decade. As adoption expands, the study also underscores the need for AI governance in validation and c