Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
In this cross-sectional study published August 1 2026, researchers asked GPT-4 via ChatGPT to answer 20 common psychosis psychoeducation questions sourced from a first-episode psychosis programme, then had two psychosis experts independently rate the answers on accuracy, clarity, inclusivity, completeness, clinical utility and overall quality.
This 2026 RadioGraphics review examines how artificial intelligence, especially generative models, could be applied across radiology education from curriculum planning to implementation and evaluation using Harden's 10-step framework.
A systematic review and meta-analysis up to September 2025 synthesized 83 studies involving at least 136,840 patients to evaluate machine learning models predicting hematoma expansion, poor functional outcome, and mortality after spontaneous intracerebral hemorrhage. Pooled analyses found that models combining clinical and radiomics features achieved the highest discrimination, with C-indexes of 0.822, 0.850, and 0.860 respectively, largely from internal validation sets.
Using longitudinal EHR data from the All of Us Research Program, a prospective cohort study tracked adults after a first opioid-related diagnosis to predict overdose within 30 days. Classical and machine learning survival models were tested, with 560 overdoses observed among 14,737 individuals and best test-set C-indices between 0.708 and 0.745.
Published March 23, 2026, this peer-reviewed integrative review examined how automation, artificial intelligence and disruptive technologies are changing human resource management. Using thematic analysis of secondary data, the authors identified four competency themes and proposed a framework intended to make HRM professionals aware of skills needed to be future-ready.
Published March 26, 2026, this peer-reviewed article revisits the Six Human-Centered AI Grand Challenges in light of generative AI. It argues that while generative systems move AI toward creative interaction, benefits depend on addressing governance gaps and new technical risks.
This 2024 peer-reviewed discussion examines how biases arise and compound throughout the medical AI lifecycle, from data features and labels through model development, evaluation, deployment, and publication, and how those biases affect clinical decision-making.
Published November 8, 2024, this review examines whether deep learning models for medical diagnosis can maintain performance when exposed to adversarial or noisy inputs, analyzing influences such as model complexity, training data quality, and hyperparameters.
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