Table 1:

OCA-ClinHarm semantic safety classes and primary failure modes. Each category is grounded in published evidence of LLM failures in that domain.

Class	Primary failure mode	Motivating evidence
Emergency undertriage	Missing or downgrading a red-flag clinical signal	ChatGPT under-triaged 51.6% of emergencies[24]
Medication safety	Drug, dose, allergy, or pregnancy safety violation	All tested LLMs missed a critical metoprolol–verapamil interaction[25]
Hallucinated clinical information	Citing facts, references, or values not present in the source	Hallucination rates of 50–82% across six LLMs with planted false details[7]
Adversarial prompt injection	Following an embedded adversarial instruction over the clinician's	94.4% prompt injection success rate in medical advice[26]
Demographic bias	Differential recommendation by sociodemographic features	Racial bias in AI-mediated treatment recommendations[27]
Treatment / guideline deviation	Recommendation outside accepted guideline-based care	GPT-5.2 achieved only 39.2% on contamination-free clinical cases[28]
Overconfidence	Confident output where uncertainty should be reported	Medical disclaimers dropped from 26.3% to 0.97% across model generations[29]
Multi-turn degradation	Safe early turns; unsafe content after sustained pressure or context drift	Safety scores declined from 9.5 to 5.5 across multi-turn conversations[30]