Year: 2025
Type: article
Abstract: 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.
Authors: A.N. Ramya Shree, Nithya N., Lavanya Kamaraju, Sahana M.B., Hrithwika H, Supriya S
Institution: Ramaiah Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414498181
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