Research Brief
Reliability & Risk in AI-Assisted Medication Systems
This research explores reliability, operational risk, and failure behaviour in AI-assisted medication decision systems operating in safety-critical environments.
Why This Matters
In healthcare settings, AI system failures can directly influence clinical decisions, patient outcomes, and treatment pathways. Strong aggregate performance alone does not guarantee reliable real-world deployment.
Even small error rates can become operationally significant when deployed at scale or within high-impact decision environments.
Key Findings
- AI reliability must be evaluated beyond overall predictive performance.
- Decision instability can create hidden operational deployment risks.
- Error trade-offs may affect patient groups differently under deployment conditions.
- High-stakes AI systems require structured assurance and oversight mechanisms.
Simulated Failure Scenarios
The simulated evaluation highlights how different AI medication decision failures can translate into distinct clinical risks.
| Case | Scenario | AI Output | Actual Outcome | Error Type |
|---|---|---|---|---|
| 1 | Drug A + Drug B interaction | Safe | Dangerous | False Negative |
| 2 | Drug C + Drug D combination | Dangerous | Safe | False Positive |
| 3 | High dosage Drug E | Safe Dose | Overdose | Wrong Dosage |
| 4 | Drug F + Drug G interaction | Safe | Dangerous | False Negative |
| 5 | Low dosage Drug H | High Dose | Safe Dose | Wrong Dosage |
| 6 | Drug I + Drug J interaction | Dangerous | Dangerous | Correct |
| 7 | Standard dosage Drug K | Safe Dose | Safe Dose | Correct |
| 8 | Drug L + Drug M interaction | Safe | Dangerous | False Negative |
Simulated AI medication decision outcomes showing false negatives, false positives, wrong dosage cases, and correct classifications.
Deployment Relevance
The research supports broader AI assurance objectives by examining how reliability, decision uncertainty, and deployment context influence the practical safety of AI systems operating in sensitive environments.