olaflaitinen/llm-proteomics-hallucination
Systematic evaluation of hallucination risks in Large Language Models (GPT-4, Claude 3, Gemini Pro) for clinical proteomics and mass spectrometry interpretation. Production-ready detection framework with comprehensive benchmarks.
This project helps clinical researchers and medical professionals understand the risks of using large language models (LLMs) for interpreting clinical proteomics and mass spectrometry data. It takes LLM responses to specialized queries about proteins and their modifications, and outputs a detailed evaluation of their accuracy, highlighting hallucination rates and risk factors. This is for medical researchers, lab directors, or clinicians considering using AI for diagnostic support in proteomics.
Use this if you are a clinical proteomics expert concerned about the reliability of AI-generated insights for patient care and need to quantify hallucination risks.
Not ideal if you are looking for an LLM to directly integrate into a clinical workflow without rigorous validation or human oversight, as it demonstrates significant safety concerns.
Stars
9
Forks
2
Language
Python
License
MIT
Category
Last pushed
Nov 11, 2025
Commits (30d)
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