Papers with Evidence-based medicine
Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine (2026.findings-acl)
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Sebastian Antony Joseph, Lily Chen, Barry Wei, Michael Mackert, Iain James Marshall, Paul Pu Liang, Ramez Kouzy, Byron C Wallace, Junyi Jessy Li
| Challenge: | Evidence-based medicine connects to every individual, yet the nature of it is highly technical . e-fact-checking systems that connect to medical decisions are largely unused . we examine how clinical experts verify real claims from social media . |
| Approach: | They propose that fact-checking should be approached as an interactive communication problem . they argue that social media and AI have made medical knowledge accessible . |
| Outcome: | The proposed method is based on the work of a clinical expert on social media . it reveals that the method is difficult to connect claims to clinical trials . |
Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence (2024.lrec-main)
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| Challenge: | Evidence-based medicine is the practice of making medical decisions that adhere to the latest, and best known evidence available. |
| Approach: | They propose a system that can generate synthetic medical claims to aid each of these tasks and a dataset that demonstrates an improvement in all comparable metrics. |
| Outcome: | The proposed system improves on core tasks and shows that it is more flexible and holistic. |
Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review (2025.findings-acl)
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| Challenge: | Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. |
| Approach: | They propose to investigate the use of Natural Language Processing (NLP) techniques to identify, appraise, synthesize, apply, and disseminate evidence in EBM. |
| Outcome: | The proposed methods support the five fundamental steps of EBM—Ask, Acquire, Appraise, Apply, and Assess. |