Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine
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Abstract (arXiv)
Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in adopting these systems for public health and medicine has grown due to the high-stakes nature of medical decisions and challenges in critically appraising a vast and diverse medical literature. Evidence-based medicine connects to every individual, and yet the nature of it is highly technical, rendering the medical literacy of majority users inadequate to sufficiently navigate the domain. Such problems with medical communication ripen the ground for end-to-end fact-checking agents: check a claim against current medical literature and return with an evidence-backed verdict. And yet, such systems remain largely unused. In this position paper, developed with expert input, we present the first study examining how clinical experts verify real claims from social media by synthesizing medical evidence. In searching for this upper-bound, we reveal fundamental challenges in end-to-end fact-checking when applied to medicine: Difficulties connecting claims in the wild to scientific evidence in the form of clinical trials; ambiguities in underspecified claims mixed with mismatched intentions; and inherently subjective veracity labels. We argue that fact-checking should be approached as an interactive communication problem, rather than an end-to-end process.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 59 references that are in the map. First posted on arXiv 25 Jun 2025.
Builds on
26 of 59 references.
References · 26
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- 2021A Survey on Automated Fact-Checkingcited by 378
- 2020Explainable Automated Fact-Checking for Public Health Claimscited by 211
- 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
- 2023WiCE: Real-World Entailment for Claims in Wikipediacited by 120
- 2023Complex Claim Verification with Evidence Retrieved in the Wildcited by 110
- 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
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- 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
- 2024CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Modelscited by 54
- 2023FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Modelscited by 46
14 more
- 2024Molecular Facts: Desiderata for Decontextualization in LLM Fact Verificationcited by 44
- 2023Analyzing Dataset Annotation Quality Management in the Wildcited by 41
- 2024A Closer Look at Claim Decompositioncited by 41
- 2021AmbiFC: Fact-Checking Ambiguous Claims with Evidencecited by 38
- 2024Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance?cited by 36
- 2023HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checkingcited by 35
- 2023The Perils & Promises of Fact-checking with Large Language Modelscited by 31
- 2022CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweetscited by 29
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
- 2024OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMscited by 28
- 2022Human and technological infrastructures of fact-checkingcited by 16
- 2022RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Mediacited by 13
- 2024Document-level Claim Extraction and Decontextualisation for Fact-Checkingcited by 6
- 2024An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Modelscited by 4
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Citing papers per year: 2026: 2.
2 papers.