Abstract

Artificial intelligence-based automatic medical report creation has accelerated significantly since the introduction of cross-modal learning, sophisticated transformer structures, and knowledge-enhanced pretraining methods. The detector attention modules, adapter tuned vision language models, and graph-guided hybrid strategies are integrated to propose framework for automated medical report generation. Utilizing topic wise separable retrieval, hierarchical cross-modal alignment, and phrase-level augmentation, the proposed MedXGen confront semantic inconsistency, hallucination and redundancy. Memory-guided transformers and semi- supervised learning is used to enhance interpretability and adaptability. The suggested framework offers a practical implementation of clinical diagnostic support systems and it supports medical language creation and visual perception.

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