Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model
ObjectiveNeurosurgical intelligent question-answering (Q&A) systems offers a novel paradigm to enhance perceptual intelligence—simulating human-like cognitive processing for contextual understanding and emotion interaction. While retrieval-based models lack perceptual adaptability to rare cl...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1606038/full |
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| author | Ting Pan Jiang Shen Man Xu |
| author_facet | Ting Pan Jiang Shen Man Xu |
| author_sort | Ting Pan |
| collection | DOAJ |
| description | ObjectiveNeurosurgical intelligent question-answering (Q&A) systems offers a novel paradigm to enhance perceptual intelligence—simulating human-like cognitive processing for contextual understanding and emotion interaction. While retrieval-based models lack perceptual adaptability to rare clinical scenarios, and generative LLMs, despite fluency, fail to ground outputs in domain-specific neurosurgical knowledge or doctor expertise. Hybrid frameworks struggle to emulate clinician perceptual workflows (e.g., contextual prioritization, empathy modulation). These present challenges for further improving the semantic understanding, memory integration, and trustworthiness of intelligent Q&A systems in neurosurgery.ApproachTo address these challenges, we propose a Multi-Task Knowledge Graph-Augmented Answer Generation model (MT-KGAG), designed to enhance perceptual fidelity. It uses a hybrid attention mechanism to introduce neurosurgical knowledge graph and doctor features in the answer generation model to prioritize clinically salient information akin to human perceptual workflows. Simultaneously, the model employs a multi-task learning framework, jointly optimizing answer generation, candidate answer ranking, and doctor recommendation tasks aligning machine outputs with clinician decision-making patterns while embedding safeguards against hallucination or inappropriate emotional mimicry. Experiments utilize real-world data from a Chinese online health platform, validated through perceptual coherence metrics and ethical robustness assessments.ResultsThe MT-KGAG model outperformed all baselines. It achieved an Embedding Average of 0.9439, DISTINCT-2 of 0.2681, and a medical entity density of 0.2471. Medical experts rated patient safety at 4.02/5 and health outcomes at 3.89/5. Additionally, it attained MRR scores of 0.6155 for candidate answer ranking and 0.6169 for doctor recommendation, confirming its multi-task synergy.DiscussionMT-KGAG pioneers perception-aware AI in neurosurgery, where LLMs transcend text generation to simulate clinician-like contextual reasoning and ethical judgment. By fusing LLM’s generative adaptability with domain-specific knowledge graphs, the model navigates complex trade-offs between empathetic interaction and perceptual safety—delivering responses that are both contextually nuanced and ethically constrained. This work highlights the transformative potential of perceptual intelligence in medical AI, enabling systems to “interpret” patient needs, “recall” specialized knowledge, and “prioritize” clinical relevance while mitigating risks of anthropomorphic overreach. |
| format | Article |
| id | doaj-art-ee4a8a124aad4930b5c71ec888a3b2f7 |
| institution | OA Journals |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj-art-ee4a8a124aad4930b5c71ec888a3b2f72025-08-20T01:52:18ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-05-011910.3389/fnins.2025.16060381606038Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation modelTing Pan0Jiang Shen1Man Xu2College of Management and Economy, Tianjin University, Tianjin, ChinaCollege of Management and Economy, Tianjin University, Tianjin, ChinaBusiness School, Nankai University, Tianjin, ChinaObjectiveNeurosurgical intelligent question-answering (Q&A) systems offers a novel paradigm to enhance perceptual intelligence—simulating human-like cognitive processing for contextual understanding and emotion interaction. While retrieval-based models lack perceptual adaptability to rare clinical scenarios, and generative LLMs, despite fluency, fail to ground outputs in domain-specific neurosurgical knowledge or doctor expertise. Hybrid frameworks struggle to emulate clinician perceptual workflows (e.g., contextual prioritization, empathy modulation). These present challenges for further improving the semantic understanding, memory integration, and trustworthiness of intelligent Q&A systems in neurosurgery.ApproachTo address these challenges, we propose a Multi-Task Knowledge Graph-Augmented Answer Generation model (MT-KGAG), designed to enhance perceptual fidelity. It uses a hybrid attention mechanism to introduce neurosurgical knowledge graph and doctor features in the answer generation model to prioritize clinically salient information akin to human perceptual workflows. Simultaneously, the model employs a multi-task learning framework, jointly optimizing answer generation, candidate answer ranking, and doctor recommendation tasks aligning machine outputs with clinician decision-making patterns while embedding safeguards against hallucination or inappropriate emotional mimicry. Experiments utilize real-world data from a Chinese online health platform, validated through perceptual coherence metrics and ethical robustness assessments.ResultsThe MT-KGAG model outperformed all baselines. It achieved an Embedding Average of 0.9439, DISTINCT-2 of 0.2681, and a medical entity density of 0.2471. Medical experts rated patient safety at 4.02/5 and health outcomes at 3.89/5. Additionally, it attained MRR scores of 0.6155 for candidate answer ranking and 0.6169 for doctor recommendation, confirming its multi-task synergy.DiscussionMT-KGAG pioneers perception-aware AI in neurosurgery, where LLMs transcend text generation to simulate clinician-like contextual reasoning and ethical judgment. By fusing LLM’s generative adaptability with domain-specific knowledge graphs, the model navigates complex trade-offs between empathetic interaction and perceptual safety—delivering responses that are both contextually nuanced and ethically constrained. This work highlights the transformative potential of perceptual intelligence in medical AI, enabling systems to “interpret” patient needs, “recall” specialized knowledge, and “prioritize” clinical relevance while mitigating risks of anthropomorphic overreach.https://www.frontiersin.org/articles/10.3389/fnins.2025.1606038/fullneurosurgery careintelligent question and answering systemknowledge graphmulti task learningmedical answer generation |
| spellingShingle | Ting Pan Jiang Shen Man Xu Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model Frontiers in Neuroscience neurosurgery care intelligent question and answering system knowledge graph multi task learning medical answer generation |
| title | Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model |
| title_full | Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model |
| title_fullStr | Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model |
| title_full_unstemmed | Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model |
| title_short | Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model |
| title_sort | enhancing the performance of neurosurgery medical question answering systems using a multi task knowledge graph augmented answer generation model |
| topic | neurosurgery care intelligent question and answering system knowledge graph multi task learning medical answer generation |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1606038/full |
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