View clinical trials related to Artificial Intelligence.
Filter by:A variety of diseases in the Department of Rheumatology, Immunology, Nephrology, and Gastroenterology can cause eye lesions, and medications can also bring various adverse reactions, which can seriously reduce the quality of patients' daily life, bring additional economic burdens, and even threaten the lives of patients. This study aims to recruit patients from the aboved-mentioned departments and conduct a cross-sectional and cohort study. On one hand, we plan to compare the epidemiological characteristics of ocular lesions of systemic diseases and eye adverse drug effects in patients with rheumatology, immunology, nephrology and gastroenterology, and summarized some epidemiological indices such as prevalence, high-risk factors, etc. On the other hand, we plan to develop an artificial intelligence model after collecting certain case data. By selecting risk factors related to the occurrence of ocular lesions, we aim to train models that can predict the ocular manifestations of systemic diseases and medications.
Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. There is considerable need in the western countries to allocate healthcare resources accurately and give the population detailed and reliable health information enabling to take greater responsibility for their health. Intelligent patient flow management system (IPFM, product name Klinik Frontline) is developed to meet these needs. In practice, IPFM is used for decision support in the triaging and diagnostic processes as well as automatizing the management of inflow of the patients. The core of the IPFM is a clinical artificial intelligence (AI), which utilizes a comprehensive medical database of clinical correlations generated by medical doctors. The study population of this research consists of patients from the Paediatric Emergency Clinic of Turku University Hospital (TUH). Data will be gathered during 6 months of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 500 patients, with objective to be 1 000. When attending to the hospital, patients or their guardians will report their demographics, background information and symptoms using structured IPFM online form. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making. The data obtained from IPFM online form and clinical data from the emergency department and TUH will be analysed after the data collection. The main aim of the research is to validate the use of IPFM by evaluating the association of IPFM output with 1) urgency and severity of the conditions; and 2) actual diagnoses diagnosed by medical doctors. The main hypotheses of the research are that 1) IPFM is safe and sensitive in evaluating the urgency of the conditions of arriving patients at the emergency department and that 2) IPFM has sufficient correlation of differential diagnosis with actual diagnosis made by medical doctor.
The present study is trying to find out whether artificial intelligence assisted follow-up strategy will improve secondary prevention in CABG patients. In addition, we will test whether rural patients may have more benefits under the new follow-up strategy based on the artificial intelligence device compared with urban patients.
To improve the quality of mental health services, we will develop a robot that includes disease screening, diagnosis, treatment, and follow-up. The effectiveness of robots will be verified in a prospective, randomized, multi-center clinical trial. We assume that the robot will reduce the differences in the experience of doctors of different years and will improve mental health care across the country, and improve the uneven distribution of mental health resources through remote resource sharing.
This study; It will be carried out with the aim of developing the artificial intelligence method, which allows automatic determination of comfort levels of newborns.
Difficult airway is a major reason of anesthesia related injuries with latent life threatening complications. Foresee difficult airway in the preoperative period is vital for the patient's safety. The aim of this study is to develop a computer algorithm that can detect whether the patient is a difficult airway based on photographs form six aspects. This method will be decreased potential complication related to difficult airway and increased patient safety.