Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and monitor the tumor's response. Recent AI advances in neuroimaging have promising applications in neuro-oncology, including guiding clinical decisions and improving patient management. However, the lack of clarity on how AI arrives at predictions has hindered its clinical translation.

Differentiating Benign from Malignant Causes of Splenomegaly: Is Acoustic Radiation Force Impulse Elastography Helpful?

Purpose: To evaluate the ability of acoustic radiation force impulse (ARFI) elastography in differentiating benign from malignant etiologies of splenomegaly based on differences in splenic stiffness.

Materials And Methods: Between September 2020 and November 2022, we evaluated 40 patients with splenomegaly-defined by a splenic long axis greater than 13 cm and/or a short axis greater than 6 cm, without visible focal or infiltrative mass lesions-using abdominal ultrasound at our university hospital. Each patient also underwent a standardized ARFI elastographic assessment of the enlarged spleen, with data collected prospectively.

SHAPE: A visual computing pipeline for interactive landmarking of 3D photograms and patient reporting for assessing craniosynostosis.

3D photogrammetry is a cost-effective, non-invasive imaging modality that does not require the use of ionizing radiation or sedation. Therefore, it is specifically valuable in pediatrics and is used to support the diagnosis and longitudinal study of craniofacial developmental pathologies such as craniosynostosis - the premature fusion of one or more cranial sutures resulting in local cranial growth restrictions and cranial malformations. Analysis of 3D photogrammetry requires the identification of craniofacial landmarks to segment the head surface and compute metrics to quantify anomalies.

A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support.

Human involvement remains critical in most instances of clinical decision-making. Recent advances in AI and machine learning opened the door for designing, implementing, and translating interactive AI systems to support clinicians in decision-making. Assessing the impact and implications of such systems on patient care and clinical workflows requires in-depth studies.