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MediSummarizer uses a fine-tuned transformer-based language model (e.g., a BERT or GPT variant) trained on pairs of clinical documents and patient-friendly explanations. The model can take as input electronic health records (EHRs), radiology reports, or lab results and output layman’s summaries while preserving clinical accuracy. It integrates medical ontologies like SNOMED CT and UMLS to ensure consistency and correct interpretation of terms. The target users include hospitals, clinics, and healthtech apps aiming to improve patient comprehension, reduce anxiety, and encourage informed decision-making.