Clinical trials

The study purpose is to construct and validate the effects of "intelligent parent-child bonding intervention" on the physical, psychological and social health of parents of premature infants.

This trial will prospectively evaluate the impact of integrating AI-ECG within the pay-for-performance program on improving the diagnosis, treatment, and clinical outcomes of occlusion myocardial infarction patients by promoting accurate and timely diagnoses through financial incentives.

This prospective multicenter observational study will validate the accuracy of a previously developed artificial intelligence-enabled single-lead electrocardiogram (AI-ECG) model for identifying low ejection fraction and other structural heart disease phenotypes. Adult participants receiving a model-compatible single-lead electrocardiogram (ECG) (Apple Watch and QOCA ECG102D) and transthoracic echocardiography at five hospitals in Taiwan will be enrolled between March 1, 2026 and June 30, 2026. Model predictions will be compared with echocardiographic reference standards obtained within 30 days after the index ECG.

This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP. We hypothesize that the AI-ECG model can early identify patients with pulmonary hypertension in high-risk patients, prompting further evaluation through echocardiography, potentially resulting in improving cardiovascular outcomes.

This study aims to develop, optimize, and evaluate the effectiveness of a family resilience intervention centered on intelligent parent-infant connection for parents of preterm infants. Using a longitudinal randomized controlled trial design, it examines the intervention's impact on parental psychosocial health, parenting efficacy, family resilience, and parent-infant interaction from NICU hospitalization to six months post-discharge.

Hyperkalemia is a common and potentially life-threatening electrolyte disorder, yet there is limited evidence guiding the optimal timing of potassium-lowering therapy in routine clinical practice. Although electrocardiographic (ECG) abnormalities are recommended to inform treatment decisions, such findings are often subtle and difficult to recognize consistently by clinicians. This study aims to emulate a target trial to evaluate the association between the timing of potassium-lowering therapy (timely versus delayed initiation) and short-term mortality among patients with laboratory-confirmed hyperkalemia presenting to the emergency department. In addition, the study examines whether artificial intelligence-enabled ECG (AI-ECG) stratification identifies patient subgroups that may differentially benefit from earlier treatment. Using observational electronic health record data from multiple healthcare systems, including publicly available critical care databases and institutionally governed hospital datasets, treatment strategies are compared using causal inference methods designed to approximate randomized assignment. The primary outcome is 90-day all-cause mortality. The results of this study are intended to inform clinical decision-making regarding treatment timing in hyperkalemia and to evaluate the potential role of AI-ECG as a risk stratification tool in real-world settings.

The goal of this clinical trial is to learn whether an artificial intelligence-enhanced electrocardiogram (AI-ECG) strategy improves timely intervention of patients requiring cardiac implantable electronic devices (CIEDs), compared with standard clinical care.

Our study aimed to use an AF-predict AI-ECG alert system to help physicians identify patients who need to wear a continuous cardiac rhythm monitor for new diagnoses of atrial fibrillation (AF), atrial flutter (AFL), or atrial arrhythmia with high AF risk, including premature atrial complexes (PAC) ≥ 500/24hr, burst PACs \> 20 beats, non-sustained AF/AFL.

The goal of this clinical trial is to learn if an artificial intelligence-powered electrocardiogram (AI-ECG) can help detect moderate or severe valvular heart diseases (VHD) in adults. The main question it aims to answer is: ．Can AI-ECG screening identify patients with significant heart valve diseases who may benefit from early echocardiography? Researchers will compare the rate of moderate or severe VHD detection between the experimental group and the control group to see if AI-ECG improve the detection rate of significant VHD. Participants will: * Be classified as high- or low-risk for VHD using an AI-ECG system * In the experimental group, high-risk participants will receive echocardiography based on AI-ECG results * In the control group, usual clinical care will be provided without routine echocardiography for AI-ECG high-risk results.

This study will implement a smartphone chatbot in medical-surgical nursing education to enhance nursing students' learning motivation and academic performance. T

This study aims to develop and evaluate an AI-driven Personalized Exercise Feedback Program (AI-PEF) to enhance exercise adherence and health outcomes in mTBI patients. Methods: AI-PEF integrates the transtheoretical model and self-determination theory with machine learning algorithms to provide real-time, personalized feedback. A phased randomized controlled trial will be conducted: Phase I evaluates feasibility and acceptability through Delphi methods with expert consensus and patient feedback; Phase II validates preliminary outcomes with 30 participants in a 2-arm randomized trial; and Phase III assesses the program's impact on adherence, sleep quality, depressive symptoms, and quality of life with 90 participants in a 3-arm randomized trial.

This study aims to examine the effects of iTBS on cognitive function in individuals with MCI or mild AD, with a secondary objective of exploring prefrontal TBS mechanisms for cognitive function and the effect of iTBS on BDNF.