The field of radiology report generation and analysis is rapidly evolving, with a focus on improving the accuracy and reliability of automated report generation systems. Recent developments have highlighted the importance of incorporating clinical context and uncertainty quantification into these systems. Researchers are exploring new methods for fact-checking and error detection in generated reports, as well as developing more robust evaluation metrics that align with real-world clinical judgment. Noteworthy papers in this area include: Random Direct Preference Optimization for Radiography Report Generation, which introduces a model-agnostic framework for enhancing report generation accuracy. Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports, which presents a novel fact-checking model for detecting errors in generated reports. Clinical Uncertainty Impacts Machine Learning Evaluations, which argues for the importance of accounting for annotation uncertainty in machine learning evaluations. LatXGen: Towards Radiation-Free and Accurate Quantitative Analysis of Sagittal Spinal Alignment, which proposes a generative framework for synthesizing realistic lateral spinal radiographs. Accurate Cobb Angle Estimation via SVD-Based Curve Detection and Vertebral Wedging Quantification, which presents a novel deep learning framework for AIS assessment. Uncertainty Quantification for Regression using Proper Scoring Rules, which introduces a unified framework for uncertainty quantification in regression. ReEvalMed: Rethinking Medical Report Evaluation, which proposes a clinically grounded meta-evaluation framework for assessing report generation quality. Automated Structured Radiology Report Generation with Rich Clinical Context, which incorporates rich clinical context into automated report generation. Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring, which identifies error-prone CXR cases using an augmentation-sensitivity risk scoring framework. Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using GPT-4o, which utilizes a self-correction loop with structured output framework to improve accuracy.