Correction: Peisen et al. Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors? 2024, , 2669.

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A dataset and benchmark for hospital course summarization with adapted large language models.

Objective: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.

The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pediatric chest radiographs and to assess its diagnostic performance. Chest radiographs from patients under 19 years old, collected between March and November 2021, were divided into test and exploring sets. A commercial adult-oriented AI software was utilized to detect lung lesions, including pneumothorax, consolidation, nodule, and pleural effusion, using a standard operating point of 15%.

Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT-A Multi-Center, Multi-Observer Reading Study.

Objectives: To evaluate the performance of a custom-made convolutional neural network (CNN) algorithm for fully automated lesion tracking and segmentation, as well as RECIST 1.1 evaluation, in longitudinal computed tomography (CT) studies compared to a manual Response Evaluation Criteria in Solid Tumors (RECIST 1.1) evaluation performed by three radiologists.