Research Brief

Reliability & Risk in AI-Assisted Medication Systems

This research explores reliability, operational risk, and failure behaviour in AI-assisted medication decision systems through simulated safety-critical scenarios.

Why This Matters

In healthcare settings, AI-generated outputs may influence clinical decisions, treatment pathways, and ultimately patient outcomes when incorporated into decision-making. Strong aggregate performance alone does not establish reliable performance under real-world deployment conditions.

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.
Different failure types can carry different consequences in safety-critical decision contexts.
Simulation can support examination of failure modes relevant to assurance and oversight.

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.

Within these simulated scenarios, false negatives were the most frequent failure type, illustrating how undetected harmful medication interactions can form an important category of safety-critical error.

Deployment Relevance

The research supports broader AI assurance objectives by examining simulated failure behaviour, decision uncertainty, and deployment-relevant considerations that can inform safety and assurance review in sensitive environments.

View paper on arXiv View code on GitHub