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

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.