# From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT

arXiv: https://arxiv.org/abs/2502.14714 · HTML: https://mlsanity.com/paper/2502.14714/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 75 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health](https://mlsanity.com/paper/2306.10070/) (2023)
- [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://mlsanity.com/paper/2204.03905/) (2022)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Fact-checking information from large language models can decrease headline discernment](https://mlsanity.com/paper/2308.10800/) (2023)
- [News Verifiers Showdown: A Comparative Performance Evaluation of ChatGPT 3.5, ChatGPT 4.0, Bing AI, and Bard in News Fact-Checking](https://mlsanity.com/paper/2306.17176/) (2023)
- [PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence](https://mlsanity.com/paper/2307.09683/) (2023)
- [Learning from Fact-checkers: Analysis and Generation of Fact-checking Language](https://mlsanity.com/paper/1910.02202/) (2019)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [Fact-Checking of AI-Generated Reports](https://mlsanity.com/paper/2307.14634/) (2023)
- [Detection of ChatGPT Fake Science with the xFakeSci Learning Algorithm](https://mlsanity.com/paper/2308.11767/) (2023)
- [Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification](https://mlsanity.com/paper/2308.03929/) (2023)
- [Accelerating Complex Disease Treatment through Network Medicine and GenAI: A Case Study on Drug Repurposing for Breast Cancer](https://mlsanity.com/paper/2406.13106/) (2024)
- [Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responses](https://mlsanity.com/paper/2404.14454/) (2024)

## Built on by (most cited first, 4 of 4)

- [Autoformalization in the Era of Large Language Models: A Survey](https://mlsanity.com/paper/2505.23486/) (2025)
- [Towards Autoformalization of LLM-generated Outputs for Requirement Verification](https://mlsanity.com/paper/2511.11829/) (2025)
- [Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow](https://mlsanity.com/paper/2605.30400/) (2026)
- [MechAInistic: An LLM-guided Multi-Agent System for Reasoning over Genome-Scale Constraint-Based Metabolic Models](https://mlsanity.com/paper/2607.18249/) (2026)

## Abstract (arXiv)

The generative capabilities of LLM models offer opportunities for accelerating tasks but raise concerns about the authenticity of the knowledge they produce. To address these concerns, we present a computational approach that evaluates the factual accuracy of biomedical knowledge generated by an LLM. Our approach consists of two processes: generating disease-centric associations and verifying these associations using the semantic framework of biomedical ontologies. Using ChatGPT as the selected LLM, we designed prompt-engineering processes to establish linkages between diseases and related drugs, symptoms, and genes, and assessed consistency across multiple ChatGPT models (e.g., GPT-turbo, GPT-4, etc.). Experimental results demonstrate high accuracy in identifying disease terms (88%-97%), drug names (90%-91%), and genetic information (88%-98%). However, symptom term identification was notably lower (49%-61%), due to the informal and verbose nature of symptom descriptions, which hindered effective semantic matching with the formal language of specialized ontologies. Verification of associations reveals literature coverage rates of 89%-91% for disease-drug and disease-gene pairs, while symptom-related associations exhibit lower coverage (49%-62%).