Journal of the American College of Radiology : JACR最新文献

There are many challenges associated with the safe use of fluoroscopy. These challenges include but are not limited to highly variable regulatory requirements, scope of practice concerns, inconsistent education and training, and lack of staff empowerment. Challenges are further compounded by the increasing use of fluoroscopy across a wide range of medical specialties. To facilitate consensus on how to address the issues, the ACR convened the multidisciplinary Blue Ribbon Panel on Fluoroscopy Safety (BRP-FS), with 32 organizations represented. The goal of the BRP-FS is to establish multi- and interspecialty consensus standards for the safe use of fluoroscopy in health care, including minimum and uniform standards for the education and training of fluoroscopy users that apply across geographic and professional boundaries, for the benefit of all patients and health care providers. Recommendations are made for local practices, professional organizations, industry, regulatory agencies, and accreditation bodies. Foundational to the recommendations of the BRP-FS are the personnel training and procedure classification frameworks in National Council on Radiation Protection and Measurement Commentary No. 33.

Objective: To evaluate whether a deep learning-based AI system with suspected nodule indexing and malignancy risk stratification improves radiologist performance in detecting pulmonary nodules on CT, using a dataset enriched with challenging early-stage lung cancers.

Methods: The study comprised a standalone AI sensitivity-specificity analysis and a two-arm crossover reader study with 16 American board-certified radiologists. Each reader interpreted 340 CT scans with and without AI, separated by a one-month washout. The dataset included 209 screening and 131 non-screening cases: 133 with lung cancer, 61 with benign non-calcified nodules ≥4 mm, and 146 normal. To enrich subtle lesions, 64 of 91 (70.3%) small cancer cases were drawn from early-round NLST CT scans. Localization-specific ROC (LROC) analysis was used to assess radiologist performance.

Results: Standalone AI achieved a sensitivity of 0.804 at 1.37 false positives per case. With AI assistance, radiologists' LROC AUC improved cancer detection (0.761 vs. 0.652; ΔAUC = 0.109, 95% CI: 0.067, 0.152) and for all nodules (0.830 vs. 0.734; ΔAUC = 0.096, 95% CI: 0.059, 0.133). Mean sensitivity increased from 0.585 to 0.727, while specificity remained essentially unchanged (0.918 vs. 0.913). Interpretation time decreased by 12.9%, from a mean of 133 to 115.9 seconds (Difference = -17.1 seconds (95% CI: -26.7, -9.0)). AI alerts enabled detection of early-stage cancer detection previously missed in NLST interpretations.

Discussion: The AI system significantly improved radiologist's performance in pulmonary nodule detection, with consistent benefits across nodule types, screening contexts, and experience levels; supporting its integration into routine chest CT interpretation workflows.

Background: Large Language Models (LLMs) are increasingly being evaluated for their ability to answer official radiology board-style examination questions. Understanding their accuracy, limitations, and potential applications in education is essential for assessing their utility in the field.

Material and methods: A scoping review was conducted in October 2025 across PubMed, Scopus, and Web of Science, following PRISMA guidelines. Studies were included if they evaluated LLMs on official radiology board-style examination questions. After screening 205 unique records, 29 studies met the inclusion criteria. Data were extracted on study characteristics, including LLM type and version, input modality, language, examination type, answer format, comparison with humans, and reported outcomes.

Results: The reviewed studies evaluated multiple LLMs, predominantly GPT-based models (GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o), as well as Claude, Gemini, LLaMA 3, and Mixtral. Text-only evaluations generally yielded higher accuracy (≈65-90%) compared to multimodal tasks (45-89%). GPT-4 and its variants consistently outperformed earlier versions, occasionally exceeding average human performance. Open-source models such as LLaMA 3 70B and Mixtral achieved comparable results to proprietary models, offering advantages in local deployment and privacy. Few studies directly compared LLM performance with human radiologists.

Conclusions: LLMs demonstrate promising performance in answering text-based radiology board-style exam questions, particularly GPT-4-based models. Nevertheless, significant limitations persist in multimodal tasks and complex reasoning scenarios.