放射影像学专业英语

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.

The automated generation of free-text radiology reports is crucial for improving diagnosis and treatment in clinical practice. The latest chest X-ray report generation models utilize large vision language model (LVLM) architectures, which demand a higher level of interpretability for clinical deployment. Uncertainty estimation scores can assist clinicians in evaluating the reliability of these model outputs and promoting broader adoption of automated systems. In this paper, we conduct a comprehensive evaluation of the correlation between 16 LLM uncertainty scores and 6 radiology report evaluation metrics across 4 state-of-the-art LVLMs for CXR report generation. Our findings show a strong Pearson correlation, ranging from 0.4 to 0.6 on a scale from -1 to 1, for several models. We provide a detailed analysis of these uncertainty scores and evaluation metrics, offering insights in applying these methods in real clinical settings. This study is the first to evaluate LLM-based uncertainty estimation scores for X-ray report generation LVLM models, establishing a benchmark and laying the groundwork for their adoption in clinical practice.