Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine

arXiv cs.CL / 4/30/2026

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Key Points

  • The paper studies end-to-end medical fact-checking systems that would verify medical claims by checking current literature and returning evidence-backed verdicts, noting that such approaches have seen limited real-world use.
  • Using expert input, it presents an early study on how clinical experts themselves verify real claims from social media by synthesizing medical evidence, establishing an “upper-bound” on verification behavior.
  • The authors identify core validity challenges for end-to-end fact-checking in medicine, including difficulty linking real-world claims to relevant clinical trials, handling ambiguous under-specified claims, and reconciling mismatched intents.
  • They also argue that fact-checking in this domain is inherently subjective, making label veracity difficult to define and evaluate in a purely automatic end-to-end pipeline.
  • Overall, the work proposes reframing medical fact-checking as an interactive communication problem rather than a fully automated end-to-end system.

Abstract

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.