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Deep learning systems are being developed to generate medical reports automatically to reduce doctor workload.

Using attention mechanisms to identify the most relevant parts of an image for a specific description.

There is a critical need to bridge the "visual-pathological gap," as many standard models lack the ability to accurately describe pathological locations. 126287

This review provides a systematic and comprehensive analysis of how deep learning models translate visual content into human language, with a particular focus on both general and medical applications. 🔬 Core Components of the Review

The extraction of visual information using models like CNNs or Vision Transformers. Deep learning systems are being developed to generate

Experts and researchers emphasize the practical difficulties and recent breakthroughs in applying these deep reviews to real-world medical data.

Newer models like JAGAN (Joint Attention Generative Adversarial Nets) are introduced to ensure that the generated text maintains a professional "clinical language style". 📊 Key Challenges & Metrics This review provides a systematic and comprehensive analysis

Translating those visual features into coherent text using architectures like RNNs, LSTMs, and Transformers. 🏥 Focus on Medical Report Generation