ArticlePloS one2025
Advancing medical question answering with a knowledge embedding transformer.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Application of artificial intelligence CNN model in emotional recognition of instrumental music.Scientific reports · 2025Article
- Empowering people with intellectual disabilities using integrated deep learning architecture driven enhanced text-based emotion classification.Scientific reports · 2025Article
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Authors and funding
4 authors.
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Abstract
Efficient medical question answering is essential for better patient care. Despite progress since Eliza (1966), even advanced LLMs (e.g., GPT-4) struggle with medical data. This study presents a system combining knowledge embedding and transformers. It includes a knowledge understanding layer and an answer generation layer. Tested on the MedQA dataset, it achieved 82.92% accuracy, outperforming GPT-4's 71.07%. The results demonstrate the system's ability to deliver accurate and ethical answers. This integrated method improves response speed and quality. Future work will enhance precision, support patient interaction, and integrate multimodal data for improved healthcare query processing.
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