Latest AI and machine learning research in information technology for healthcare professionals.
In recent years, progress in medical informatics and machine learning has been accelerated by the availability of openly accessible benchmark datasets. However, patient-level electronic medical record (EMR) data are rarely available for teaching or methodological development due to privacy, governance, and re-identification risks. This has limited reproducibility, transparency, and hands-on traini...
Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in multimodal foundation models have improved performance on various clinical tasks, most existing models remain static, opaque, and poorly aligned with real-world clinical workflows. We present Cerebra, an interactive multi-agent AI team that coordinates s...
Systems powered by large language models are widely used for health information and advice, yet robust evidence for their safety and effectiveness in ...
India's national research capacity and infrastructure are unevenly distributed across states and union territories (UTs), contributing to geographic v...
Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive b...
Background Snakebite envenoming is a significant global health crisis that has been long neglected as a global health priority. It is a huge problem f...
Electronic health records (EHRs) contain rich multimodal data but remain underutilized for populating clinical registries due to the time and cost of ...
Early diagnosis of lung cancer is challenging due to biological uncertainty and the limited understanding of the biological mechanisms driving nodule ...
Early identification of patients at risk for clinical deterioration in the intensive care unit (ICU) remains a critical challenge. Delayed recognition...
Background: Longitudinal measurement of depression severity in outpatient psychiatric care is limited by infrequent standardized assessments. Although...
Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk predict...
The growing adoption of electronic health record (EHR) systems has provided unprecedented opportunities for predictive modeling to guide clinical deci...
Objective. Healthcare machine learning models trained on patient data must comply with the General Data Protection Regulation (GDPR) right to erasure ...
Clinical research depends on high quality data that is standardized, accessible and interoperable. Yet evolving data standards over time and variation...
Objective To develop and evaluate a scalable and reproducible natural language processing (NLP) approach using large language models (LLM), to identif...
Predicting hospital outcomes for patients with severe acute respiratory infections is critical for risk stratification and resource planning, yet hete...
Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundrai...
In recent years, Artificial Intelligence has become a powerful partner for complex tasks such as data analysis, prediction, and problem-solving, yet i...
Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, imp...
Background Personalized medicine promises to tailor treatments to the individual, but it carries a hidden risk: mistaking statistical noise for action...