Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model.

OBJECTIVE

Neurosurgical 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.

APPROACH

To 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.

RESULTS

The 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.

DISCUSSION

MT-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.