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A two-layered RxCUI ingredient and ATC framework harmonized 214,080 discharge medication records from older adults into standardized representations, achieving 100% initial mapping via deterministic crosswalks to support transportable managed care AI tools.

A pharmacist-overseen AI system for provider organizations could reduce prescriber workload and increase first-pass approval rates by automating routine data extraction and submissions while routing complex cases to pharmacists.

AI models improve cardiovascular prevention by providing more precise, dynamic and personalized risk stratification than traditional scores and by enabling early detection of subclinical atrial fibrillation, left ventricular dysfunction and coronary disease through AI-enabled ECG and opportunistic imaging.

AI-based support systems could enhance safety and make xenotransplantation more reproducible when combined with gene-edited donors and refined immunosuppression.

District-wide implementation of an AI-enabled wound app with virtual command centre produced high patient satisfaction and perceived benefit, including improved communication and self-management confidence among app users.

Machine learning-supported body composition analysis applied to CT imaging quantifies radiologic sarcopenia and enables risk stratification for survival after first-line immunochemotherapy in newly diagnosed DLBCL.

Deep learning models for Hirschsprung disease histopathology achieved over 90% ganglion cell detection and cut diagnostic time by 50-95%, increasing accuracy and accelerating clinical decision-making.

The integrated ML-based Yield Gap Vulnerability framework provides a data-driven decision-support tool that can support sustainable agricultural management, spatial planning and risk reduction by identifying vulnerability hotspots for region-specific measures in India.

AI models Gemini and FaceApp generated post-denture facial images rated as esthetically comparable to actual clinical outcomes by patients and experts.

Automated pulmonary AI matched persisting lung nodules across 3-month LDCT scans with 83.5% success, reaching 91.8% for single-nodule cases and leaving only 1.5% of persisting findings needing manual correction, indicating potential to reduce manual tracking workload.

Machine learning can map potentially toxic element concentrations from environmental covariates and produce exceedance-probability maps aligned with regulatory thresholds for soil-contamination management.

Unsupervised machine learning applied to bloodstream infections identifies reproducible clinical subphenotypes with different mortality, supporting bedside tools for rapid phenotype assignment and personalized antimicrobial therapy.

Using a machine learning model to measure CT body composition at L3 identified myosteatosis as an independent predictor of recurrence in stage II-III colon cancer.

AI integration in echocardiography workflows reduces examination time and automates measurements, enabling more comprehensive data collection while reducing sonographer fatigue.

Naive CD4+ TCR repertoire features enabled moderate machine-learning classification of celiac disease status and high-accuracy prediction of HLA-DQ2.5 status by publication date.