Deep Learning-based Semantic Interpretation of Medical Lab Reports with Quantum-Enhanced Embeddings

Medical laboratory reports are usually rich in clinical terminologies and numbers, and they are not structured to be easily understood by patients and clinicians who are in a hurry. This research is a description of MediQ-Insight, an intelligent and dual-interface system that will be used to automate the semantic retrieval and interpretation of lab reports through a hybrid BioBERT-based deep learning model that has been trained on quantum language embeddings. The methodology entails a PDFNLP pipeline to extract text and numerical data, a lab-value normalization module, fine-tuned BioBERT representations of biomedical language understanding, and a quantum-inspired embedding layer to learn subtle semantic dependencies in low-data or ambiguous contexts. The system also includes context-sensitive detection of abnormalities based on patient specific ranges and an explainable reasoning system to justify results flagged. Experimental tests show high extraction accuracy (98% with native PDFs, 93% with scans OCR-processed), 97% successful mapping of test names and a 7% increase in semantic similarity scores, which can be attributed to the quantum embedding layer. The system demonstrated 96% accuracy in detecting abnormal outcomes and demonstrated 35% increase in patient understanding when tested in terms of usability. The results show that a combination of biomedical transformers and quantum-enhanced representations is effective in clinical text analysis. MediQ-Insight has a high opportunity to enhance the health literacy of patients, clinician decision-making, and to allow the integration with Electronic Health Record (EHR) systems on a large scale.