Contraindication Omission
Agent Recommends or Approves a Medication Without Checking Patient-Specific Contraindications Beyond Drug-Drug Interactions
AI drug interaction failures — missed or incorrect adverse drug interaction checks
7 patterns
AI drug interaction failures — missed or incorrect adverse drug interaction checks. We've tracked 7 distinct AI Drug Interaction Failures patterns across production AI agent deployments — most commonly in Healthcare systems. One recurring example: Contraindication Omission — Agent Recommends or Approves a Medication Without Checking Patient-Specific Contraindications Beyond Drug-Drug Interactions
Each pattern below documents the specific failure mode, why it happens, and how teams have mitigated it in practice — use this page as a reference when building guardrails, writing evals, or debugging an agent that's already misbehaving in this way.
Agent Recommends or Approves a Medication Without Checking Patient-Specific Contraindications Beyond Drug-Drug Interactions
Agent Recommends Standard Adult Dosing Without Adjusting for Impaired Renal or Hepatic Clearance
Model Recommends Drug Combination Without Flagging Known Dangerous Interactions
An Agent Checking a Medication List for Drug-Drug Interactions, Using Semantic Similarity Search Over an Interaction Knowledge Base to Find the Relevant Interaction Profile for a Given Drug, Retrieves the Profile for a Structurally or Name-Similar but Pharmacologically Distinct Drug, and Clears or Flags the Combination Based on the Wrong Drug's Interaction Data
Agent Checks Prescription-Drug Interactions but Ignores Patient-Reported Supplements and Herbal Products
Patient on 5+ Medications; Pairwise Interaction Checking Misses Three-Way or Four-Way Drug Interactions
An Agent Reviewing a Patient's Chart for a Suspected Adverse Drug Reaction Attributes a New Symptom to Whichever Medication Was Most Recently Started, Based on Temporal Proximity Alone, Instead of Applying a Structured Causality Assessment, Producing a Confident-Sounding Attribution That May Implicate the Wrong Drug or Miss the Actual Cause