The complex relationships between continuously monitored health signals and therapeutic
regimens can be modelled via machine learning. However, the clinical implementation of …

Artificial intelligence (AI) digital health systems have drawn much attention over the last
decade. However, their implementation into medical practice occurs at a much slower pace …

Artificial intelligence in health care has experienced remarkable innovation and progress in
the last decade. Significant advancements can be attributed to the utilization of artificial …

The 'precision medicine (systems medicine)'concept promises to achieve a shift to future
healthcare systems with a more proactive and predictive approach to medicine, where the …

Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical
artificial intelligence (AI). The number of applications of clinical AI is increasing, which …

One of the principal goals in medicine is to determine and implement the best treatment for
patients through fastidious estimation of the effects and benefits of therapeutic procedures …

Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need
for real-world evaluations for effectiveness and unintended consequences. The complexity …

Telehealth and remote patient monitoring have expanded the reach of traditional clinical
practice by removing geographical barriers as well as clinical limitations. Will this lead to …

Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly
driven by the emergence of increasingly accurate machine learning models. However, the …

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited
by the unpredictability of cardiovascular events, the intermittent nature of ambulatory …