Research Insights
Summary findings from the LLM reliability evaluation study.
No experiment data. Run an experiment first.
ConfigureThis study evaluates the reliability and hallucination behavior of large language models (LLMs) on a curated medical question answering (QA) benchmark. We assess multiple models using various prompting strategies across 0 clinically relevant questions spanning pharmacology, anatomy, microbiology, and clinical reasoning. Our evaluation framework measures accuracy, hallucination rate, and response consistency — metrics critical for safe AI deployment in healthcare contexts. Response correctness is assessed using both a keyword-overlap heuristic and an LLM-as-judge semantic comparison, with agreement between the two reported as a measure of scoring validity.
Accuracy: 0%
The model achieved 0% accuracy, below the recommended threshold for medical AI deployment. Retrieval-augmented generation should be explored.
Hallucination Rate: 0%
0 of 0 responses contained hallucinated content. This falls within acceptable bounds for a research tool, though clinical deployment requires a rate below 5% with validation.
Accuracy by Question Phrasing
Precise-phrasing questions (n=0) achieved 0% accuracy (0-0% CI), while ambiguous-phrasing questions (n=0) achieved 0% accuracy (0-0% CI).
Hallucination Pattern Analysis
The most common failure mode was factual error (0 cases), followed by fabricated information (0 cases).
Measurement Validity
No judged responses are available to assess agreement between scoring methods.
- →Implement retrieval-augmented generation (RAG) to ground responses in verified medical literature
- →Use chain-of-thought prompting for complex diagnostic or pharmacological questions
- →Deploy response validation layers before any clinical-facing application
- →Conduct larger-scale evaluation (500+ questions) covering rare diseases and edge cases
- →All AI outputs in clinical contexts must be reviewed by qualified medical professionals
The current evaluation highlights significant reliability limitations of this model on medical QA tasks. An accuracy of 0% and hallucination rate of 0% indicate that further development — including domain-specific fine-tuning, RAG integration, and confidence calibration — is required before deployment in any clinical context.
LLM Reliability Lab. (2026). Evaluating Large Language Model Reliability in Medical Question Answering. Research Prototype. Built with Next.js + Groq Cloud API.