Primary Care

Integrating Genomics into Multimodal EHR Foundation Models

This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships betwee...

AI-Driven and 3D-Bioprinted New Approach Methodology (NAM) Identifies NEO100 as Potent Ultrasound-Activated Therapeutic for Primary and Metastatic Brain Tumors

Primary and metastatic brain tumors are among the deadliest and treatment-resistant cancers, mainly because of their inherent resistance to chemoradiation and limited drug delivery across the blood–brain barrier (BBB). Identifying molecules that can cross the BBB and serve as sonosensitizers is crucial for developing noninvasive, targeted therapies such as sonodynamic therapy (SDT). To overcome th...

Ultrahigh throughput screening to train generative protein models for engineering specificity into unspecific peroxygenases

Enzyme engineering is central to developing biocatalysts with improved activity and specificity, yet traditional approaches are often limited by the s...

Decoding Diabetes: Harnessing AI to Accurately Predict Real-Time and Future Blood Glucose Levels for Diabetes Management Using Diet, Exercise, Insulin Intake, and Heart Rate Variability

Continuous glucose monitoring (CGM) systems play a crucial role in diabetes care. Yet, they focus solely on blood glucose levels (BGL), neglect diet, ...

A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome

Esophageal squamous cell carcinoma (ESCC) is a disease with limited tools for early screening and a poor prognosis. Symptoms typically appear late, an...

HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody Discovery

Antibody discovery remains constrained by resource-intensive experimental screening approaches that offer limited control over critical properties. He...

CitriBEiTNet: A Hybrid CNN-Transformer Architecture Combining MobileNetV2 with BEiT’s Global Attention for Automated Citrus Leaf Disease Diagnosis

Citrus farming plays an essential role in agriculture; however, diseases like canker, greening, black spot, and melanose significantly reduce yield an...

The FERM Guild: A Differentially Correlated Microbial Module Drives Hypertension via Metabolic Flux Perturbations

Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition and function being closely associated with...

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges...

Integration of artificial intelligence and high-content screening enabled identification of drugs for long-term treatment of cerebral cavernous malformation disease

Adults and children with cerebral cavernous malformations (CCMs) are at risk of experiencing lifelong complications such as hemorrhagic strokes, neuro...

Machine learning prediction algorithms for 2- , 5- and 10-year risk of Alzheimer’s, Parkinson’s and dementia at age 65: a study using medical records from France and the UK General Practitioners

Leveraging machine learning on electronic health records offers a promising method for early identification of individuals at risk for dementia and ne...

Study Research Protocol for Phenome India-CSIR Health Cohort Knowledgebase (PI-CHeCK): A Prospective multi-modal follow-up study on a nationwide employee cohort

Predicting individual health trajectories based on risk scores can help formulate effective preventive strategies for diseases and their complications...

Deep Learning Chest X-Ray Age, Epigenetic Aging Clocks and Associations with Age-Related Subclinical Disease in the Project Baseline Health Study

Chronological age is a cornerstone of medical decision-making but is limited because individuals age at different rates. We recently released an open-...

Retinal vascularization rate predicts retinopathy of prematurity and remains unaffected by low-dose bevacizumab treatment

To assess the rate of retinal vascularisation derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as predictor...

Predicting Dementia in People with Parkinson’s Disease

Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...

Predicting Hypertension Among HIV Patients on Antiretroviral Therapy in Rural Eastern Cape, South Africa Using Machine Learning

Hypertension continues to be a major challenge in developing countries like South Africa, as it significantly contributes to the cardiovascular diseas...

Impact of Mydriasis on Image Gradability and Automated Diabetic Retinopathy Screening with a Handheld Camera in Real-World Settings

Diabetic retinopathy (DR) screening in low- and middle-income countries (LMICs) faces challenges due to limited access to specialized care. Portable r...

EpiPathAI: Using Large Language Models to Explore Mechanisms of Life Course Exposure-Outcome Associations

Large language models (LLMs) enhanced with Graph Retrieval-Augmented Generation (GRAG) are promising for life-course epidemiology, which typically dep...

Trust in large language model-based solutions in healthcare among people with and without diabetes: a cross-sectional survey from the Health in Central Denmark cohort

Large language models have gained significant public awareness since ChatGPT’s release in 2022. This study describes the perception of chatbot-assiste...

Hazard-aware adaptations bridge the generalization gap in large language models: a nationwide study

Despite growing excitement in deploying large language models (LLMs) for healthcare, most machine learning studies show success on the same few limite...