BACKGROUND: Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development.
BACKGROUND: Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient popul...
BACKGROUND: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk populatio...
BACKGROUND: The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing re...
BACKGROUND: Infusion failure may have severe consequences for patients receiving critical, short-half-life infusions. Continued interruptions to infusions can lead to subtherapeutic therapy.
BACKGROUND: Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may prov...
BACKGROUND: Artificial intelligence (AI)-based cancer detectors (CAD) for mammography are starting to be used for breast cancer screening in radiology departments. It is important to understand how AI CAD systems react to benign lesions, especially t...
BACKGROUND: Extractive question-answering (EQA) is a useful natural language processing (NLP) application for answering patient-specific questions by locating answers in their clinical notes. Realistic clinical EQA can yield multiple answers to a sin...
BACKGROUND: The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source.
BACKGROUND: Nearly one-third of patients with diabetes are poorly controlled (hemoglobin A≥9%). Identifying at-risk individuals and providing them with effective treatment is an important strategy for preventing poor control.