Who's Running QC on Artificial Intelligence?
Why we should stop treating AI as an information technology problem and start governing it like our other laboratory instruments
-
By
-
Caitlin Raymond
-
April 9, 2026
-
Clinical Scorecard: Who's Running QC on Artificial Intelligence?
At a Glance
| Category | Detail |
|---|
| Condition | Clinical AI Tools in Medicine |
| Key Mechanisms | AI tools process clinical data to inform decisions, similar to diagnostic tools in laboratories. |
| Target Population | Healthcare institutions deploying AI tools for clinical decision support. |
| Care Setting | Health systems and clinical environments. |
Key Highlights
- AI tools are being deployed without a coherent governance framework.
- Clinical AI tools require rigorous validation and ongoing performance monitoring.
- Model drift can lead to silent degradation of AI tool performance.
- The laboratory medicine framework should guide AI governance.
- Current regulatory guidance for AI in medicine is lagging.
Guideline-Based Recommendations
Diagnosis
- Establish analytical validation processes before AI tool deployment.
- Define conditions for reliable AI tool performance.
Management
- Implement ongoing quality control measures for AI tools.
- Define action thresholds for performance review or removal.
Monitoring & Follow-up
- Monitor AI tool performance regularly and investigate shifts.
- Ensure visibility into AI validation and performance for clinicians.
Risks
- Accountability for underperformance must be clearly defined.
- AI tools can produce authoritative but incorrect outputs.
Patient & Prescribing Data
Patients receiving care influenced by AI outputs.
AI tools can enhance decision-making but require rigorous oversight.
Clinical Best Practices
- Incorporate laboratory medicine principles into AI governance.
- Engage pathologists and laboratory professionals in AI oversight.
- Ensure transparency in AI tool validation and performance metrics.
References
