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Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial

2026-05-05 · Lisa C. Adams, Linus Marx, Erik Thiele Orberg, Keno Bressem, Sebastian Ziegelmayer, Denise Bernhardt, Markus Graf, Marcus R. Makowski, Stephanie E. Combs, Florian Matthes, Jan C. Peeken arxiv

Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, increase clinician trust compared to traditional explainability approaches? Findings: In this randomized trial of 356 clinicians generating 7,476 trust ratings, atomic fact-checking produced a large effect on trust (Cohen's d = 0.94), increasing the proportion of clinicians expressing trust from 26.9% to 66.5%. Traditional transparency mechanisms showed a dose-response gradient of improvement over baseline (d = 0.25 to 0.50). Meaning: Decomposing AI recommendations into individually verifiable claims linked to source guidelines produces substantially higher clinician trust than traditional explainability approaches in high-stakes clinical decisions.

📄 PDF Abstract BibTeX arXiv:2605.03916

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Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking

2023-10-11 · Gustav Nikopensius, Mohit Mayank, Orchid Chetia Phukan, Rajesh Sharma

Fact-checking is a crucial task as it ensures the prevention of misinformation. However, manual fact-checking cannot keep up with the rate at which false information is generated and disseminated online. Automated fact-c…

Fact Checking Misinformation reinforcement-learning Reinforcement Learning+1

Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs

2025-05-30 · Juraj Vladika, Annika Domres, Mai Nguyen, Rebecca Moser 외

Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and regulatory compliance. Current methods, suc…

Fact Checking Hallucination Long Form Question Answering Medical Question Answering+2

That is a Known Lie: Detecting Previously Fact-Checked Claims

2020-05-12 · ACL 2020 6 · Shaden Shaar, Giovanni Da San Martino, Nikolay Babulkov, Preslav Nakov

The recent proliferation of "fake news" has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives. As a result and over time, a large number of fact-checked claims have b…

Fact Checking Learning-To-Rank Retrieval

Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration

2025-05-11 · Honglong Yang, Shanshan Song, Yi Qin, Lehan Wang 외

Generalist Medical AI (GMAI) systems have demonstrated expert-level performance in biomedical perception tasks, yet their clinical utility remains limited by inadequate multi-modal explainability and suboptimal prognosti…

Benchmarking Descriptive Diagnostic Question Answering+2

Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation

2023-09-15 · Aman Rangapur, Haoran Wang, Ling Jian, Kai Shu

Fact-checking in financial domain is under explored, and there is a shortage of quality dataset in this domain. In this paper, we propose Fin-Fact, a benchmark dataset for multimodal fact-checking within the financial do…

Explanation Generation Fact Checking Misinformation