Medical intelligent question-answering (QA) systems have become important tools for improving the efficiency of healthcare services, and recent research has increasingly emphasized performance optimization and multimodal integration. However, existing systems still face several challenges in intent recognition, entity extraction, and multimodal knowledge fusion, particularly reduced accuracy in multi-label classification, heavy reliance on large-scale annotated data, and limited support for cross-modal retrieval. To address these issues, this study proposes a medical intelligent QA framework that integrates a dual-layer attention mechanism, a large language model, and a multimodal medical knowledge graph to improve system understanding and response generation in complex clinical scenarios. Specifically, we develop a text-based intent recognition model with a dual-layer attention architecture, in which a global contextual attention module is introduced to capture long-range semantic dependencies and improve multi-label classification performance. In addition, an instruction-tuned large language model is employed for zero-shot medical entity recognition, thereby reducing dependence on manually annotated datasets. Building on this foundation, we construct a multimodal medical knowledge graph comprising more than 15,000 associated medical images and develop a visualization-oriented retrieval interface using Flask and ECharts. Experimental results show that the proposed intent recognition model achieves a peak Micro-F1 of 94.42% on multiple benchmark datasets, outperforming several baseline methods. The LLM-based entity recognition module achieved competitive recall in medical entity extraction, demonstrating strong capability in identifying medical entities. User evaluation results further indicate that the system is effective and practical across a variety of medical query types. This study provides a feasible framework for advancing medical QA systems through improved intent recognition, low-resource entity extraction, and multimodal knowledge integration.
Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated data and the high computational demands of existing large vision-language models. We propose MedFG-VQA, a lightweight framework that leverages a memory bank to augment DCT-based low-frequency features and employs graph-enhanced cross-attention for effective visual-textual alignment. Specifically, our approach features two key components: Frequency-Memory Fusion (FMF), which enhances low-frequency features by retrieving from a learnable memory bank built on DCT decomposition, and Graph-Aware Cross-Attention (GACA), which aligns visual-textual features via cross-attention and refines them through graph-convolutional aggregation. To address data scarcity, we construct SynMed-VQA, a large-scale synthetic dataset comprising over 2 million question-answer pairs across 9 imaging modalities and 10 major organs, generated with GPT-4o. Extensive experiments on SynMed-VQA and three other standard biomedical VQA benchmarks demonstrate that MedFG-VQA achieves competitive or superior performance compared to much larger models while maintaining significantly lower computational costs, highlighting its efficiency and potential for clinical deployment.
Hao-Wen Gu, Gensheng Pei, Ze-Ren Sun et al.· 1 citation
The proposed framework offers a practical and scalable approach to mitigating hallucinations without requiring task-specific fine-tuning, highlighting the potential of retrieval-augmented approaches for trustworthy artificial intelligence (AI)-assisted healthcare applications.
Initial experiments on heart-failure-focused clinical question answering show that CGX improves evidence retrieval quality and perceived answer reliability over conventional retrieval methods, while reducing total graph construction time by 69.7% under the same input corpus and hardware setting.
Dat Nguyen, Anh N Le, Binh T. D. Trinh et al.· Journal of Biomedical Inform...· 0 citations
Mental health detection from social media has gained increasing attention due to the spontaneous expression of psychological states on online platforms. However, accurate interpretation remains challenging because social media language is highly contextual, ambiguous, and often includes sarcasm, metaphors, or non-clinical expressions, which can lead to unreliable predictions. Existing approaches based on machine learning, deep learning, transformers, and large language models (LLMs) primarily rely on text-driven patterns and often lack explicit ontology-guided grounding, structured knowledge validation, and interpretability. Although LLMs improve contextual reasoning, their predictions can be inconsistent and prone to hallucination when not supported by structured domain knowledge and a unified cross-component validation mechanism. To address these limitations, this paper proposes an ontology-guided framework that integrates a Knowledge Graph (KG), an Ontology-Informed Retrieval Classifier (ORC), and a Large Language Model for interpretable mental health detection from social media text. The framework leverages the Human Phenotype Ontology (HPO) to guide symptom extraction and Knowledge Graph construction, normalizing informal linguistic expressions into ontology-grounded representations. A key design contribution is an explicit KG–ORC cross-validation consistency gate, which requires both the Knowledge Graph and the ORC module to independently reach agreement before a prediction is committed reducing erroneous inferences from either component alone. The LLM is deliberately restricted to a post-validation role, generating human-readable explanations and supportive recommendations only from validated outputs, thereby reducing hallucination risks and preserving knowledge-consistency. The proposed framework is evaluated on the Dreaddit dataset, a large-scale, multi-domain Reddit corpus for stress and mental health analysis. Empirical evaluation on the HPO guided symptom extraction confirms differential detection rates of 56.04% in stress positive posts versus 31.11% in stress negative posts, validating the signal relevance of the ontology-driven extraction. End-to-end binary classification on the held-out test split achieves an accuracy of 74.82% with an F1-score of 75.64%. Crucially, on the 66.55% of instances where the KG and ORC modules reach consensus, the consistent subset accuracy rises to 78.84% (F1 = 78.28%), compared to 63.03% for the Knowledge Graph alone. The KG–ORC agreement rate across the full test set is 82.02%. These results demonstrate that the KG–ORC cross-validation gate measurably improves predictive reliability over single component baselines, and that ontology-guided, knowledge-grounded reasoning offers a principled path toward interpretable and knowledge-consistent mental health analysis from social media.
Amina Tahir, Ghulam Mustafa, Muhammad Tanvir Afzal et al.· Social Network Analysis and...· 0 citations
In clinical decision support systems, finding the suitable medical literature for complex patient scenarios is critical. Although dense vector-based models offer high accuracy, the global vector index requirement creates scalability problems, while word-based search engines remain insufficient due to vocabulary mismatch. In this study, an Autonomous Filter-and-Refine structure operating on standard text search infrastructures is proposed. The developed architecture overcomes the vocabulary mismatch by applying entity extraction and Hypothetical Document Embedding with a large language model, and then determines the most relevant articles with a context-aware ranker. In the experiments conducted on the PMC-Patients dataset; the proposed Autonomous Doctor Agent architecture exhibited equivalent results with vector-based models in top-ten level performance metrics; whereas in the top-three article band, it achieved superiority by leaving existing systems behind.
Doruk Üzgün, Ilkay Öksüz, Mecit Yüzkat et al.· Signal Processing and Commun...· 0 citations
Extractive Question Answering (EQA) in the biomedical domain presents significant challenges due to complex medical terminology and intricate relationships between entities. Existing systems often perform suboptimally compared to their counterparts in general domains. Current biomedical QA models struggle with accurately identifying named entities and extracting meaningful relationships within medical contexts, thereby limiting their practical utility. This study introduces a novel transformer-based approach for biomedical EQA that incorporates Named Entity Recognition (NER) to identify key medical terms, Relation Extraction (RE) to understand their interconnections, and a BiLSTM layer to enhance contextual comprehension. These components are integrated within an ensemble framework that leverages pretrained biomedical language models. Our methodology systematically evaluated six transformer encoder models and identified Bio+ClinicalBERT as the optimal foundation for developing three progressively advanced biomedical EQA models. The final model—Bio+ClinicalBERT_NER_RE_BiLSTM—incorporating ensemble learning techniques, demonstrated substantial performance improvements: an F1 score of 91.69%, exact match of 88.35%, and lenient accuracy of 0.84, marking a 5.45% performance gain over baseline methods. These results highlight the model's superior ability to navigate complex biomedical language and extract accurate information from medical texts. The developed system directly benefits biomedical researchers, clinical practitioners, medical students, and healthcare professionals by delivering accurate and rapid access to precise information within extensive medical literature. By bridging the gap between intricate medical texts and effective information retrieval, this work significantly advances automated biomedical knowledge extraction and supports evidence-based medical decision-making.
Ahmed Ajmine Nehal, M. M. Hasan, Farhana Elias et al.· Vietnam Journal of Computer...· 0 citations