Does metacognitive training for psychosis (MCT) improve neurocognitive performance? A systematic review and meta-analysis.

Background: Metacognitive training for psychosis (MCT) offers benefits for addressing hallmark deficits/symptoms in schizophrenia spectrum disorders including reductions in cognitive biases and positive/negative symptoms as well as improvements in social cognition and functioning. However, differing results exist regarding the relationship between MCT and neurocognition. A comprehensive understanding of the nature of this relationship would significantly contribute to the existing literature and our understanding of the potential added value of MCT as a cognitive intervention for psychosis.

Consideration of T-Cell Profile in the Examination of Statin Efficacy in Inflammatory Diseases, Neurodegeneration, and Neurocognitive Performance.

Statins are a cornerstone in the medical management of cardiovascular disease, yet their efficacy varies greatly between individuals. In this commentary, we outline the evidence for the role of CD4+CD28null T-cell expansion as a critical moderator of the effects of statins in preventing cardiovascular events via the reduction of pathological inflammation. Given this relationship, we argue that T-cell profiles should be considered as a patient characteristic in clinical and preclinical studies examining statin efficacy in other age- and inflammation-related pathologies.

The Impact of an Artificial Intelligence Certificate Program on Radiology Resident Education.

Rationale And Objectives: The objective of this study was to evaluate the effectiveness of a pilot artificial intelligence (AI) certificate program in aiding radiology trainees to develop an understanding of the evolving role and application of artificial intelligence in radiology. A secondary objective was set to determine the background of residents that would most benefit from such training.

Materials And Methods: This was a prospective pilot study involving 42 radiology residents at two separate residency programs who participated in the Radiological Society of North America Imaging AI Foundational Certificate course over a four-month period.

New Frontiers in Breast Cancer Imaging: The Rise of AI.

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients.

Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation.

Purpose: Breast ultrasound suffers from low positive predictive value and specificity. Artificial intelligence (AI) proposes to improve accuracy, reduce false negatives, reduce inter- and intra-observer variability and decrease the rate of benign biopsies. Perpetuating racial/ethnic disparities in healthcare and patient outcome is a potential risk when incorporating AI-based models into clinical practice; therefore, it is necessary to validate its non-bias before clinical use.