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September 5, 2025ElectronicsOpen Access

Improving GPT-Driven Medical Question Answering Model Using SPARQL–Retrieval-Augmented Generation Techniques

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Authors

AAAbdulelah AlgosaibiASAbdul Rahaman Wahab Sait

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Overview

This model demonstrates an 87.26% accuracy in medical question-answering, highlighting reduced hallucinations and improved query processing.

Key Points

  • The model achieved a generalization accuracy of 87.26%, indicating a high level of performance.
  • A minimal hallucination rate of 0.16 suggests significant improvement over traditional models.
  • SPARQL and retrieval-augmented generation integration enhances the handling of complex medical queries.
  • Deep learning techniques enable the model to adapt to rapid changes in healthcare information.

Cite This Study

Algosaibi et al. (2025) studied this question.

synapsesocial.com/papers/68bb4d206d6d5674bcd00d4ahttps://doi.org/10.3390/electronics14173488
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