TriTiCon Knowledgeworld · Free resources
Two new free checklists for AI in clinical development
A preview of our upcoming course, AI in Clinical Data Management
AI is already finding its way into regulated clinical work. For clinical development teams, the question is no longer whether to use it, but what has to be in place before it does real work, and what to expect from the vendors who supply it.
Today we are releasing two free checklists that answer those two questions: one for your own company, and one for your vendor. They are short, deliberately concrete and made to be used, not just read. Both are a first look at our upcoming course, AI in Clinical Data Management.
Two checklists, two sides of the same problem
Inside your company
Without a policy, an intake route and a named owner, even an excellent AI feature is hard to use safely.
At your vendor
A well governed company can still be let down by a feature that does not log what it does or leave an audit trail.
Checklist 1: Implementing AI in Clinical Development
The one-page checklist: what has to be in place before AI does regulated work.
Use it before, not after. Each of the six blocks is a precondition for AI contributing to regulated work, and the heavier the AI's contribution, the more of each block you need. Tick what exists, name an owner for what does not.
Core AI policy
Your company's position, not a copy of someone else's.
Intake and governance board
A defined route in, so use cases are decided, not discovered.
Approved tools and data rules
What may be used, and on what.
SOPs and the wrapping
Embed AI in the process SOPs you already have.
Validation and monitoring
Proportionate to the level and the risk.
People and suppliers
The part that is usually underestimated.
Six questions that tell you whether you are ready
- Can we say what this AI feature is for, in one sentence?
- Who approved the use case?
- Who approves each output?
- How do we know it still performs?
- Where is it written down?
- What happens when it goes wrong?
Checklist 2: AI vendor and technology requirements
What to ask a vendor, and what to specify to your own technology team.
Use it twice: as questions in a selection or qualification conversation, and as requirements in a specification. The questions are deliberately concrete, so a vendor who cannot answer them specifically has told you something useful.
Intended use and scope
Ask before anything else, as most later questions depend on the answer.
Input: prompts, configuration and data
What goes in, and who controls it.
Execution: logging and control
What the system records when it runs.
Output and human review
Where most designs fail.
Traceability and audit trail
The evidence layer.
Validation and evidence
What the vendor provides, and what remains yours.
Change and monitoring
What happens after go-live.
Regulatory and contractual
The paperwork that makes it usable.
Two answers worth listening for
- "The model just handles that." A feature without a stated intended use cannot be validated against one.
- "We can add that later." Logging, approval steps and audit trail entries are hard to retrofit. Ask for them in the specification.
What comes next
Both checklists are a free preview of AI in Clinical Data Management, our upcoming course in TriTiCon Knowledgeworld, which is coming soon.
In the meantime, the checklists stand on their own. Download them, put them in front of your team, and see where the gaps are.
Download the checklists
Both are free.
More free resources: triticon.com/pages/resources · triticon.com
