Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
This PRISMA systematic review evaluated 33 peer-reviewed studies (2019-2026) comprising 50 deep learning models that predict knee osteoarthritis progression from medical imaging. It extracted AUC as primary outcome, categorized nine different progression definitions, and assessed bias with PROBAST-AI, finding median internal AUCs of 0.87 for surgery, 0.78 for structural, and 0.79 for symptomatic endpoints.
Researchers developed a hybrid framework that couples the STICS biophysical crop model with machine learning to predict apple fruit maturity dates across China. Using phenology records from 24 sites and weather data from 250 stations for 1991-2020, they found a random forest integration improved prediction accuracy and interpretability at regional scales.
Researchers retrospectively analyzed 306 patients who had microscopic root canal treatment with rubber dam isolation between May and November 2025, defining willingness to reuse at 1-week follow-up as the outcome, with 246 willing and 60 unwilling. They trained six models on 26 variables and found the LightGBM model retained 12 predictors and achieved the highest exploratory AUCs of 0.939 and 0.983.
Researchers tested whether large language models could interpret 12-lead ECG images to distinguish left- versus right-sided origins of premature ventricular contractions in 157 patients who had undergone successful catheter ablation. By August 2026 they reported a staged extraction framework that produced a traceable stepwise process and a continuous score whose discrimination was numerically similar to a CNN baseline.
Researchers conducted an environmental scan through September 2025 of 23 US national EHR networks that aggregate patient-level data, ranging from under 1 million to over 200 million patients, and reviewed 34 ML/AI studies built on them. Most networks used common data models, yet few models were prospectively evaluated or integrated into clinical workflows.
On 2026-05-06 a peer-reviewed survey study reported results from 220 university students in Qatar about AI in mental health support. Students reported low-to-moderate awareness and trust, said they were prepared to use AI for stress management but did not want it to replace face-to-face therapy, and cited benefits of cost reduction and 24/7 accessibility alongside concerns about human interaction, overreliance and diagnostic accuracy.
On 2026-02-11, authors reported a systematic review of 35 healthcare AI implementation frameworks from 2019-2024, identifying seven critical governance domains. They used those findings to develop HAIRA, a five-level maturity model from Level 1 Initial/Ad Hoc to Level 5 Leading with benchmarks across the domains.
Published May 9, 2026, this scoping review assessed OSINT, SOCMINT, and NLP tools for hybrid-threat detection against operational requirements drawn from Russian and Chinese military tradecraft and European operational experience. It found individual disciplines technically advanced but defensive systems siloed, identifying a persistent semantic gap in cross-domain and cross-language reasoning.
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