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

2025-06-25

AI Summary

This study addresses the limited practical applicability of end-to-end medical fact-checking systems, identifying three core challenges: (1) mapping social media medical claims to clinical trial evidence, (2) ambiguity in claim intent, and (3) subjectivity in ground-truth labeling. Through qualitative empirical research—including in-depth interviews and behavioral analysis of clinical experts synthesizing evidence for real-world social media claims—the study uncovers fundamental construct validity deficiencies in existing systems. It demonstrates that medical fact-checking is inherently a human-AI collaborative, interactive communication process—not an automated binary classification task. Consequently, the study proposes a paradigm shift in evaluation: replacing static truth labels with progressive, interpretable, and dialogue-based verification mechanisms. These findings offer both a novel theoretical framework and a practical methodology for advancing trustworthy health information dissemination.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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 ripens 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. To understand this, 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 and evaluated as an interactive communication problem, rather than an end-to-end process.

Problem

• Challenges in connecting social media claims to clinical trial evidence
• Ambiguities in underspecified claims with mismatched intentions
• Inherent subjectivity in medical fact-checking veracity labels

Innovation

• End-to-end fact-checking in medicine
• Synthesizing medical evidence for claims
• Interactive communication over end-to-end process