mlsanityData to 7 Oct 2026

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

arXiv 2309.08503 ↗First posted 15 Sep 2023cs.CL cs.AIcited by 35

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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.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 35 of its 70 references that are in the map. First posted on arXiv 15 Sep 2023.

Builds on

35 of 70 references.

References · 35

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23 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 14; 2026: 16.

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  1. 2024What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verificationcited by 10
  2. 2024MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answeringcited by 9
  3. 2024Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandemcited by 8
  4. 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systemscited by 8
  5. 2025Can Large Language Models Match the Conclusions of Systematic Reviews?cited by 7
  6. 2025MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responsescited by 6
  7. 2026Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanationscited by 5
  8. 2024Free-text Rationale Generation under Readability Level Controlcited by 4
  9. 2025Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Paperscited by 4
  10. 2025Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Modelscited by 4
  11. 2025HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Modelscited by 3
  12. 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucinationcited by 2
23 more
  1. 2025Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicinecited by 2
  2. 2026Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attributioncited by 2
  3. 2023Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphscited by 1
  4. 2025Evaluating Large Language Models for Evidence-Based Clinical Question Answeringcited by 1
  5. 2025Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verificationcited by 1
  6. 2025Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptationcited by 1
  7. 2026MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generationcited by 1
  8. 2026AdversaRiskQA: An Adversarial Factuality Benchmark for High-Risk Domainscited by 1
  9. 2026VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answeringcited by 1
  10. 2026Evidence Absence Is Not Evidence Insufficiency: Diagnosing NEI Construction Artifacts in Fact Verificationcited by 1
  11. 2025Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Studycited by 0
  12. 2025Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphscited by 0
  13. 2025Exploring Health Misinformation Detection with Multi-Agent Debatecited by 0
  14. 2026Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Datasetcited by 0
  15. 2026TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inferencecited by 0
  16. 2026MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessmentcited by 0
  17. 2026Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Redditcited by 0
  18. 2026When Verification Fails: How Compositionally Infeasible Claims Escape Rejectioncited by 0
  19. 2026iPOE: Interpretable Prompt Optimization via Explanationscited by 0
  20. 2026When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verificationcited by 0
  21. 2026Generating Biomedical Fact-Checking Reports with RL-Enhanced Agentic Searchcited by 0
  22. 2026MedSNIP: Building and Benchmarking Snippet-Level Granularity for Medical Fact Verificationcited by 0
  23. 2026OpenFC: Learning Verification Policies towards Open-Search Fact Checkingcited by 0