Toward Foundation Models in Radiology? Quantitative Assessment of GPT-4V's Multimodal and Multianatomic Region Capabilities.

Background Large language models have already demonstrated potential in medical text processing. GPT-4V, a large vision-language model from OpenAI, has shown potential for medical imaging, yet a quantitative analysis is lacking. Purpose To quantitatively assess the performance of GPT-4V in interpreting radiologic images using unseen data.

Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study.

Background: Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development.

Objective: This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date.

Limited capability of MRI radiomics to predict primary tumor histology of brain metastases in external validation.

Background: Growing research demonstrates the ability to predict histology or genetic information of various malignancies using radiomic features extracted from imaging data. This study aimed to investigate MRI-based radiomics in predicting the primary tumor of brain metastases through internal and external validation, using oversampling techniques to address the class imbalance.

Methods: This IRB-approved retrospective multicenter study included brain metastases from lung cancer, melanoma, breast cancer, colorectal cancer, and a combined heterogenous group of other primary entities (5-class classification).

SARS-CoV2 evokes structural brain changes resulting in declined executive function.

Background: Several research has underlined the multi-system character of COVID-19. Though effects on the Central Nervous System are mainly discussed as disease-specific affections due to the virus' neurotropism, no comprehensive disease model of COVID-19 exists on a neurofunctional base by now. We aimed to investigate neuroplastic grey- and white matter changes related to COVID-19 and to link these changes to neurocognitive testings leading towards a multi-dimensional disease model.

Volume-Assisted Estimation of Remnant Liver Function Based on Gd-EOB-DTPA Enhanced MR Relaxometry: A Prospective Observational Trial.

In the context of liver surgery, predicting postoperative liver dysfunction is essential. This study explored the potential of preoperative liver function assessment by MRI for predicting postoperative liver dysfunction and compared these results with the established indocyanine green (ICG) clearance test. This prospective study included patients undergoing liver resection with preoperative MRI planning.