# HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking

arXiv: https://arxiv.org/abs/2309.08503 · HTML: https://mlsanity.com/paper/2309.08503/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 70 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 30 of 35)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [Lessons from Natural Language Inference in the Clinical Domain](https://mlsanity.com/paper/1808.06752/) (2018)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [The Fact Extraction and VERification (FEVER) Shared Task](https://mlsanity.com/paper/1811.10971/) (2018)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [ExpertQA: Expert-Curated Questions and Attributed Answers](https://mlsanity.com/paper/2309.07852/) (2023)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [PMC-LLaMA: Towards Building Open-source Language Models for Medicine](https://mlsanity.com/paper/2304.14454/) (2023)
- [Explainable Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2011.03870/) (2020)
- [A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking](https://mlsanity.com/paper/1911.01214/) (2019)
- [Scientific Fact-Checking: A Survey of Resources and Approaches](https://mlsanity.com/paper/2305.16859/) (2023)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [Evidence Inference 2.0: More Data, Better Models](https://mlsanity.com/paper/2005.04177/) (2020)
- [CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking](https://mlsanity.com/paper/2206.11863/) (2022)

## Built on by (most cited first, 30 of 35)

- [What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification](https://mlsanity.com/paper/2402.01360/) (2024)
- [MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering](https://mlsanity.com/paper/2406.05845/) (2024)
- [Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem](https://mlsanity.com/paper/2409.07123/) (2024)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Can Large Language Models Match the Conclusions of Systematic Reviews?](https://mlsanity.com/paper/2505.22787/) (2025)
- [MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses](https://mlsanity.com/paper/2509.17436/) (2025)
- [Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations](https://mlsanity.com/paper/2601.00282/) (2026)
- [Free-text Rationale Generation under Readability Level Control](https://mlsanity.com/paper/2407.01384/) (2024)
- [Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers](https://mlsanity.com/paper/2506.10486/) (2025)
- [Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models](https://mlsanity.com/paper/2509.04304/) (2025)
- [HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models](https://mlsanity.com/paper/2512.02299/) (2025)
- [A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination](https://mlsanity.com/paper/2505.00008/) (2025)
- [Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine](https://mlsanity.com/paper/2506.20876/) (2025)
- [Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution](https://mlsanity.com/paper/2603.05308/) (2026)
- [Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs](https://mlsanity.com/paper/2312.13881/) (2023)
- [Evaluating Large Language Models for Evidence-Based Clinical Question Answering](https://mlsanity.com/paper/2509.10843/) (2025)
- [Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification](https://mlsanity.com/paper/2509.13888/) (2025)
- [Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation](https://mlsanity.com/paper/2512.16189/) (2025)
- [MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.06519/) (2026)
- [AdversaRiskQA: An Adversarial Factuality Benchmark for High-Risk Domains](https://mlsanity.com/paper/2601.15511/) (2026)
- [VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering](https://mlsanity.com/paper/2604.08549/) (2026)
- [Evidence Absence Is Not Evidence Insufficiency: Diagnosing NEI Construction Artifacts in Fact Verification](https://mlsanity.com/paper/2605.26663/) (2026)
- [Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study](https://mlsanity.com/paper/2508.09776/) (2025)
- [Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs](https://mlsanity.com/paper/2511.12817/) (2025)
- [Exploring Health Misinformation Detection with Multi-Agent Debate](https://mlsanity.com/paper/2512.09935/) (2025)
- [Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Dataset](https://mlsanity.com/paper/2601.07985/) (2026)
- [TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference](https://mlsanity.com/paper/2601.20032/) (2026)
- [MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment](https://mlsanity.com/paper/2601.22361/) (2026)
- [Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit](https://mlsanity.com/paper/2602.15504/) (2026)
- [When Verification Fails: How Compositionally Infeasible Claims Escape Rejection](https://mlsanity.com/paper/2604.10990/) (2026)

## Abstract (arXiv)

In the digital age, seeking health advice on the Internet has become a common practice. At the same time, determining the trustworthiness of online medical content is increasingly challenging. Fact-checking has emerged as an approach to assess the veracity of factual claims using evidence from credible knowledge sources. To help advance automated Natural Language Processing (NLP) solutions for this task, in this paper we introduce a novel dataset HealthFC. It consists of 750 health-related claims in German and English, labeled for veracity by medical experts and backed with evidence from systematic reviews and clinical trials. We provide an analysis of the dataset, highlighting its characteristics and challenges. The dataset can be used for NLP tasks related to automated fact-checking, such as evidence retrieval, claim verification, or explanation generation. For testing purposes, we provide baseline systems based on different approaches, examine their performance, and discuss the findings. We show that the dataset is a challenging test bed with a high potential for future use.