Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published September 1, 2026 in Journal of Managed Care & Specialty Pharmacy, this viewpoint proposes a pharmacist-overseen, AI-enabled prior authorization model for provider organizations and health systems. It outlines a conceptual workflow where AI automates routine data extraction and submissions and routes complex cases to pharmacists for clinical verification.
This narrative review examined recent evidence on breast arterial calcifications found incidentally on screening mammography as a potential biomarker of systemic cardiovascular risk in women. It covered observational studies, cohorts, meta-analyses, and studies using artificial intelligence for automated quantification of calcific burden.
Researchers proposed forest kernel balancing as a way to choose which features to balance in observational causal inference. The approach uses kernels implicitly estimated by random forests and Bayesian additive regression trees from co-occurrence in the same terminal leaf node, then balances a summary of that kernel to indirectly learn important nonlinearities and interactions.
In patients with newly diagnosed diffuse large B-cell lymphoma from the PETAL trial, investigators used machine learning-supported body composition analysis of CT imaging to measure skeletal muscle mass. Those in the lowest tertile had inferior survival after adjustment for established risk factors, with cause-specific analyses pointing to nonrelapse mortality rather than lymphoma-specific death.
Researchers eye-tracked 100 cytotechnologists diagnosing 30 digital cytology images and then tracked 28 students before and after a 3-month training program. They found years of experience did not predict accuracy, while shorter fixation on the low-power field main object did, and students markedly improved time to first target fixation and reduced background attention after training.
By August 2026, peer-reviewed discussion in Giornale Italiano di Cardiologia described AI as entering hypertension care, able to give simple and well-documented answers to management questions for practicing physicians, while also being explored in research to identify secondary hypertension and predict future hypertension and complications such as heart failure.
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.
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