Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers developed and tested an informatics framework to convert heterogeneous discharge medication identifiers from EHRs of adults 65 and older at Buffalo General Medical Center between 2020 and 2024 into standardized RxCUI ingredient and ATC class codes. Of 214,080 records, 53% were nonstandardized Multum IDs requiring string-based reconciliation, and the team measured mapping success and correction needs after deterministic crosswalks and expert validation.
Researchers developed a machine-learning risk stratification tool using routine EHR data from five pediatric emergency departments to predict need for vasoactive medication after two-bolus fluid resuscitation in suspected sepsis. Among 341 children meeting analytic criteria, 25.8% received vasopressors, and a Random Forest model achieved AUROC 0.827 and AUPRC 0.661 with four risk tiers.
Researchers developed and externally validated an interpretable machine learning framework to predict in-hospital mortality in acute ischemic stroke using high-granularity bedside data from the first 24 hours. Using 5,014 patients from three tertiary centers between 2019-2023, the best CatBoost model with 22 features achieved AUC-ROC 0.917 internally and 0.891 externally, outperforming traditional ICU scores.
Researchers tested a no-pre-mapping workflow using the AI-based CARTOSOUND FAM module to build a three-dimensional left atrial shell from intracardiac echocardiography acquired in the right atrium, then used that shell alone to guide pulsed field ablation with a Variable Loop Circular Catheter. In 210 patients, including 76 with concomitant left atrial appendage occlusion, all pulmonary vein isolations were completed with frequent additional posterior wall and superior vena cava lesions.
From February to December 2025, researchers developed a 5-attribute rubric clarity, content, certainty, tone, verbosity to grade AI-generated patient-friendly radiology reports, using survey-workshop cycles with 19 participants and testing with ChatGPT-4.1 and Claude-4.0 outputs from public radiology impressions. Evaluation involved six research-team members and 111 additional participants, plus AI evaluation with ChatGPT-5, comparing rubric grades to prespecified reference standards and to subjective decisions about withholding unsafe reports.
A 2026 peer-reviewed paper analyzes artificial intelligence in medical education, noting that AI is rapidly changing training by offering faster workflows and richer learning resources, while also quietly reshaping how future clinicians think and act.
As of the 2026-10-01 publication, the article describes that in medicine robots and AI machines are used that could be programmed to appear to express positive emotions such as joy, compassion and hope, despite not having emotions.
This peer-reviewed review examined 26 studies from 2021 to 2024 on machine learning and deep learning for autism spectrum disorder prediction, focusing on data augmentation and feature selection methods. It categorized augmentation into conventional transformations and GAN-based synthetic generation, and feature selection into filter, wrapper, and embedded approaches, evaluating study quality with the Prediction Model Risk of Bias Assessment Tool.
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