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Chronic heart diseases are a leading cause of death worldwide. This paper examines recently published articles on the use of AI and machine-learning to detect chronic heart diseases. The main findings of these papers are summarised to assist researchers in developing new technology using AI and machine learning. The summary includes information on the technologies used in each research paper, the year of publication, the type of heart disease, the machine learning techniques employed and the advantages and limitations of each approach. The research followed the Preferred Reporting Items for Sy...

Objective To examine and summarise evidence from meta-analyses of cohort studies that evaluated the predictive associations between baseline cardiorespiratory fitness (CRF) and health outcomes among adults. Design Overview of systematic reviews. Data source Five bibliographic databases were searched from January 2002 to March 2024. Results From the 9062 papers identified, we included 26 systematic reviews. We found eight meta-analyses that described five unique mortality outcomes among general populations. CRF had the largest risk reduction for all-cause mortality when comparing high versus lo...

A meta-analysis in the journal Nutrition, Metabolism and Cardiovascular Diseases finds that ancient grains like oats, brown rice, and millet may improve lipid profiles in type 2 diabetes patients, but variations in study designs call for further research to confirm these benefits.

A comprehensive study published in The Lancet Digital Health analyzed the efficacy and challenges of AI in clinical settings, revealing a critical need for real-world testing and diverse research methodologies to ensure AI's reliable integration into healthcare.

A study in JAMA Network Open examined the acute adverse effects of therapeutic doses of psilocybin for treating depression and anxiety, focusing on its safety profile by assessing the occurrence of immediate side effects like headache, nausea, and elevated blood pressure among others.