Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography-a validation study.

Background: Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs.

Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing: validation in a low-risk to intermediate-risk outpatient clinic cohort.

Objectives: Predicting the presence or absence of coronary artery disease (CAD) is clinically important. Pretest probability (PTP) and CAD consortium clinical (CAD2) model and risk scores used in the guidelines are not sufficiently accurate as the only guidance for applying invasive testing or discharging a patient. Artificial intelligence without the need of additional non-invasive testing is not yet used in this context, as previous results of the model are promising, but available in high-risk population only.

Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care.

Heart failure (HF) is one of the most complex chronic disorders with high prevalence, mainly due to the ageing population and better treatment of underlying diseases. Prevalence will continue to rise and is estimated to reach 3% of the population in Western countries by 2025. It is the most important cause of hospitalisation in subjects aged 65 years or more, resulting in high costs and major social impact.

A new non-invasive diagnostic tool in coronary artery disease: artificial intelligence as an essential element of predictive, preventive, and personalized medicine.

Background: Known coronary artery disease (CAD) risk scores (e.g., Framingham) estimate the CAD-related event risk rather than presence/absence of CAD.

Challenges in personalised management of chronic diseases-heart failure as prominent example to advance the care process.

Chronic diseases are the leading causes of morbidity and mortality in Europe, accounting for more than 2/3 of all death causes and 75 % of the healthcare costs. Heart failure is one of the most prominent, prevalent and complex chronic conditions and is accompanied with multiple other chronic diseases. The current approach to care has important shortcomings with respect to diagnosis, treatment and care processes.

Primary care providers' attitudes toward prostate cancer risk factors at a Veterans Affairs health care facility.

Objective: We asked what factors influence primary care providers' decision to screen patients for prostate cancer.

Methods: A survey completed by 175 Veterans Affairs primary care providers queried whether patient anxiety, family history, race, and other assorted risk factors increased their likelihood of screening for prostate cancer. Subsequent questions assessed the degree to which various factors, such as age, comorbidities, and lack of interest, decreased their likelihood of screening.