User Intent Prediction for Intelligent Healthcare Question Answering Using Large Language Models
Keywords:
large language models; user intent prediction; healthcare question answering; clinical artificial intelligence; governance; fairness; system architectureAbstract
Large language models are increasingly positioned as core computational substrates for intelligent healthcare question answering systems. However, accurate answer generation depends not only on biomedical knowledge encoded in model parameters, but also on the reliable prediction of user intent under conditions of linguistic ambiguity, clinical urgency, and heterogeneous health literacy. This paper presents a system-level analysis of intent prediction for healthcare question answering using large language models. It examines architectural foundations, contextual grounding, knowledge integration, deployment trade-offs, robustness, fairness, governance, and sustainability. The discussion emphasizes that healthcare intent prediction is a socio-technical inference problem rather than a purely linguistic classification task. User intent must be reconstructed across incomplete queries, longitudinal patient contexts, and evolving clinical workflows. The paper further considers how retrieval augmentation, memory-based user modeling, fine-tuning, and prompting strategies influence system behavior and operational risk. It argues that effective deployment requires institutional governance mechanisms, adversarial robustness assessment, fairness auditing, and sustainability planning. Policy implications are discussed with attention to privacy, accountability, and clinical oversight. The analysis integrates perspectives from natural language processing, medical informatics, human-computer interaction, and health systems engineering. In doing so, it provides a research agenda for building healthcare question answering systems that are not only accurate but also safe, equitable, and institutionally sustainable.
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