Question answering techniques have mainly been investigated in open domains. However, there are particular challenges in extending these open-domain techniques to extend into the biomedical domain. Question answering focusing on patients is less studied. We find that there are some challenges in patient...
论文:Visual question answering in the medical domain based on deep learning approaches: A comprehensive study 代码(暂无) Pattern Recognition Letters(CCF-C) 作者 Jordan University of Science and Technology, Irbid, Jordan Duquesne University, Pittsburgh, PA, USA University of Manchester, Manchester, UK...
The advent of transformers4and LLMs5,6has renewed interest in the possibilities of AI for medical question-answering tasks—a long-standing ‘grand challenge’7,8,9. A majority of these approaches involve smaller language models trained using domain-specific data (BioLinkBert10, DRAGON11, PubMed...
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a ‘passing’ score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical que
OpenMedLM: prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language modelsJenish Maharjan, Anurag Garikipati, Navan Preet Singh, Leo Cyrus, Mayank Sharma, Madalina Ciobanu, Gina Barnes, Rahul Thapa, Qingqing Mao & Ritankar Das ...
[2] JIN Q, DHINGRA B, LIU Z, et al. Pubmedqa: A dataset for biomedical research question answering [J]. arXiv preprint arXiv:190906146, 2019. [3] CARLINI N, TRAMER F, WALLACE E, et al. Extracting training data from large language models; proceedings of the 30th USENIX Security Sym...
The retrieval-augmented generation (RAG) approach is used to reduce the confabulation of large language models (LLMs) for question answering by retrieving and providing additional context coming from external knowledge sources (e.g., by adding the context to the prompt). However, injecting incorrect...
ZhaoyiSun, ...YifanPeng, inJournal of Biomedical Informatics, 2023 3.3Visual question answering In the clinical domain,Visual Question Answering(VQA) represents a computer-assisted diagnostic technique that offers clinical decision-making support for image analysis[53]. ...
Visual question answering in medical domain (VQA-Med) exhibits great potential for enhancing confidence in diagnosing diseases and helping patients better understand their medical conditions. One of the challenges in VQA-Med is how to better understand a
NQG has been used for various purposes, such as data augmentation for question-answering systems (QAS), drafting quizzes for educational purposes, evaluating factual consistency in automatic summarization, developing conversational agents, and information-seeking. In this review, we focus on the use ...