Blog
Perspectives on clinical trials, AI in research, and what makes trial work reliably.
Prompt or Skill? How to Tell What a Task Actually Needs
A saved prompt and a Skill can look similar from the outside — both are instructions you reuse instead of typing from scratch. The real difference isn't which one is fancier. It's how much consistency the task in front of you actually needs.
Agents, Skills, and Artifacts: What These AI Words Actually Mean
Three words keep showing up in AI tools, vendor pitches, and colleague conversations — Agent, Skill, and Artifact. They get used loosely enough to cause real confusion. No code, no engineering: just what each word means and why it matters for regulated clinical work.
The Invisible Error: A Real Case of Silent Data Loss in AI Output
An AI tool built a clean, complete patient visit tracker from two ordinary spreadsheets — and quietly reported visits that had actually happened as missing, with no error and no warning. A walkthrough of why silent failures are the dangerous kind, and how better prompting forces them into the open. Includes a downloadable test case (synthetic data).
Everyone is already using AI. The rules of use aren't clear yet. A Community Pulse
An anonymous survey among members of the clinical-research community in the AI era Looking at how AI is actually used in everyday work. The answers point to a pattern worth talking about: the tools are already part of the workflow, the boundaries and processes are not yet.
Before You Type: What to Do When Starting an AI Conversation in a Clinical Trial Setting
The AI you use for work is not a search engine. It may carry context from previous conversations into every new one, silently and automatically, without labelling it. For regulated clinical work, that is a data governance problem. Here is what to do about it.
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AI Is Already Helping Run Your Trials. A Few Things Worth Knowing Before You Lean on It.
AI tools are delivering real value in clinical operations. A handful of quiet details — mostly about how the AI reaches your data — shape what its output can and can't tell you. Knowing them is what lets you rely on it wisely.