Latest AI and machine learning research in information technology for healthcare professionals.
OBJECTIVES: Electronic health record (EHR) data discontinuity, defined as receiving care outside of a particular EHR system, may cause misclassification of study variables. We aimed to: (1) quantify misclassification across levels of EHR data discontinuity and identify an optimal continuity threshold, (2) develop a machine learning (ML) model to predict EHR continuity and optimize fairness across ...
Radiology is rapidly evolving from a service that produces images into a data-centric clinical platform that supports prevention, early diagnosis, and personalized care. This shift is accelerated by the convergence of digital health, artificial intelligence (AI), and quantitative imaging approaches such as radiomics. However, the clinical impact of these innovations depends less on algorithms alon...
BACKGROUND: The digital transformation in healthcare has led to an increase in the use of telemedicine and artificial intelligence (AI)based applicati...
Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectromet...
Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applic...
OBJECTIVES: Develop and validate subtype-specific, fairness-aware machine learning (ML) models that integrate clinical and social determinants of heal...
PURPOSE OF REVIEW: Early-onset type 2 diabetes (EOT2D), defined as a diabetes diagnosis before 40 years of age, is rising globally and associated with...
Natural enzymes often fail to meet industrial demands for catalytic efficiency, stability, and substrate specificity, creating a critical bottleneck i...
Research and innovation are foundational to the transformation of professional case management and utilization review (CM/UR). Over the past decade, C...
The use of artificial intelligence (AI) is anticipated to transform mental health care. However, the rapid research growth in this field has outpaced ...
Age-related cognitive dysfunction, including mild cognitive impairment and dementia, underscores the need for scalable and personalized predictive mod...
BACKGROUND: Machine learning (ML), deep learning (DL) and other predictive modelling approaches are increasingly applied to predict antiretroviral the...
BACKGROUND: Cardiovascular disease (CVD) diagnosis using multimodal health care data remains a major challenge due to the heterogeneity of clinical an...
Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder worldwide, with projections exceeding 25 million people by 2050. Its burden...
BACKGROUND: Manual chart abstraction from electronic health records is a critical step in clinical outcomes research but is time-intensive and prone t...
Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electro...
PURPOSE: To introduce and evaluate OphthoChat, a Health Insurance Portability and Accountability Act-compliant, artificial intelligence (AI)‑powered n...
AIMS: Rising healthcare demand is increasingly outpacing available outpatient capacity in cardiology, where follow-up is often scheduled at fixed inte...
OBJECTIVES: Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room (OR) resources and affe...
The rising adoption of cryptocurrencies has been paralleled by the emergence of cryptojacking malware, malicious software that covertly hijacks comput...