Latest AI and machine learning research in primary care for healthcare professionals.
Modern vision-language models (VLMs) can act as generative OCR engines, yet open-ended decoding can expose rare but consequential failures. We identify a core deployment misalignment in generative OCR. Autoregressive decoding favors semantic plausibility, whereas OCR requires outputs that are visually grounded and geometrically verifiable. This mismatch produces severe errors, especially over-gene...
Objective: To develop and validate a multivariable prediction model and clinically actionable risk score for vaginal birth after cesarean (VBAC) success using machine learning, and to integrate neonatal morbidity outcomes into a decision-analytic framework for trial of labor after cesarean (TOLAC) counseling. Methods: We performed a retrospective cohort study of 1,418 consecutive TOLAC cases at a ...
Background Scalable, non invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We e...
Persistent infection with high-risk human papillomavirus (HPV) is the primary cause of cervical cancer and other HPV-related malignancies. Effective s...
Abstract Objective To systematically evaluate the diagnostic performance of large language models (LLMs) in automated medical literature screening and...
Objective Aiming at the core problems prevalent in biomedical research, including the "translational distance", the difficulty in aligning cross-scale...
Managing diabetes-related conditions is time-intensive and cognitively demanding for patients and caregivers, requiring ongoing glucose monitoring, di...
Phenotypic characterization is essential for understanding heterogeneity in chronic diseases and for guiding personalized interventions. Long COVID, a...
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...
Objectives Patients with osteoarthritis (OA) affecting multiple joints have poorer health outcomes than those without, yet most research examines isol...
Charcot-Marie-Tooth disease type 2A (CMT2A) is a genetic disease characterized by autosomal dominant MFN2 mutations and dysregulated mitochondrial tra...
Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to exp...
Breast cancer remains a leading cause of cancer-related mortality worldwide. Longitudinal mammography risk prediction models improve multi-year breast...
Mental health related problems in adolescents are not always properly evaluated because of incomplete evaluation methods that do not combine biologica...
Background: A critical radiologist shortage exists in India, leading to delayed chest radiograph (CXR) interpretation. This leads to disease progressi...
Diabetic retinopathy screening traditionally relies on fundus photography, requiring specialized equipment and expertise often unavailable in primary ...
Hyperkalemia is a life-threatening electrolyte disorder that is common in patients with chronic kidney disease and heart failure, yet frequent monitor...
Background and Objective: Increasing screening volumes, combined with global shortage of radiologists and a high proportion of normal mammograms, chal...
Regular monitoring of glycemic status is essential for diabetes management, yet conventional blood-based testing can be burdensome for frequent assess...
The use of wearables in medicine and wellness, enabled by AI-based models, offers tremendous potential for real-time monitoring and interpretable even...