The Bottom Line
- Trust should be conditional, not binary.
- You need a repeatable rule for when to verify and when to move on.
- Use metacognitive sensitivity: track when AI is right/wrong in your domain.
Think like a clinician
You don’t ‘trust’ a symptom. You weigh it. Do the same with AI: treat outputs as a signal with variable reliability that depends on context.
The goal is not ‘never be wrong’. The goal is to be wrong less often and to know when you’re at risk. Trust calibration means building a verification habit that triggers automatically in high-stakes or high-uncertainty situations.
1
Define your ‘verify triggers’
High stakes (patient safety), unfamiliar topic, surprising claim, or anything that changes management—verify.
2
Ask for uncertainty explicitly
Prompt: ‘Give your answer + 2 plausible alternatives + what would change your mind.’
3
Keep a tiny calibration log
When AI is wrong, note the domain (e.g., drug interactions, obscure guidelines, rare conditions). Patterns emerge quickly.
4
Prefer sources over fluency
A smooth answer is not an accurate answer. Require references when the claim is specific or directive.
5
Run periodic ‘audit retests’
Once a week, take 10 AI-generated claims from your notes and verify them against a trusted source. This keeps calibration honest.
My trust-calibration defaults
1
2
3
4
5
Practice
Test your knowledge
Apply this concept immediately with a high-yield question block from the iatroX Q-Bank.
SourceMetacognitive sensitivity and trust calibration in human–AI decision-making (PNAS Nexus)
Open Link SourceSystematic review/meta-analysis: GenAI impacts on learning outcomes (2025)
Open Link