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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.03916 |
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| _version_ | 1866913091340468224 |
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| author | Adams, Lisa C. Marx, Linus Orberg, Erik Thiele Bressem, Keno Ziegelmayer, Sebastian Bernhardt, Denise Graf, Markus Makowski, Marcus R. Combs, Stephanie E. Matthes, Florian Peeken, Jan C. |
| author_facet | Adams, Lisa C. Marx, Linus Orberg, Erik Thiele Bressem, Keno Ziegelmayer, Sebastian Bernhardt, Denise Graf, Markus Makowski, Marcus R. Combs, Stephanie E. Matthes, Florian Peeken, Jan C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03916 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial Adams, Lisa C. Marx, Linus Orberg, Erik Thiele Bressem, Keno Ziegelmayer, Sebastian Bernhardt, Denise Graf, Markus Makowski, Marcus R. Combs, Stephanie E. Matthes, Florian Peeken, Jan C. Computation and Language Artificial Intelligence 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. |
| title | Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.03916 |