MedXGen: LLM leveraged Framework for Automated Clinical Coherent Medical Report Generation

Artificial intelligence-based automatic medical report creation has accelerated significantly since the introduction of crossmodal 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 semisupervised 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.