Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
A retrospective study of 2293 ADNI brain MRIs plus 270 external NACC scans tested whether SubtleHD, an FDA-cleared deep learning enhancement tool, could improve downstream Alzheimer's classification when applied to already diagnostic-quality 1.5T T1-weighted images. ResNet34 and DenseNet121 models trained on enhanced images outperformed those trained on standard-of-care images on internal and external tests.
On 2026-08-07, researchers reported a urine-based test for urothelial bladder cancer that combines solvent extraction, GC-MS profiling, and machine learning. In 100 participants, an XGBoost model using an 8-metabolite panel achieved AUROC 0.869, improving on classical statistics at 0.752, with 85% balanced sensitivity and specificity.
By August 2026, researchers published a systematic review and meta-analysis of AI combined with digital cholangioscopy for indeterminate and malignant biliary strictures. The analysis pooled five studies totaling 675 lesions and 2,685,674 images, finding pooled sensitivity of 95%, specificity of 88%, and SROC accuracy of 97% for AI-assisted diagnosis.
A peer-reviewed study published August 6, 2026 proposes a deep learning framework for dynamic mental health assessment that fuses text and visual modalities. The model uses Bi-LSTM for text and CNN for images, trained on a jointly annotated dataset labeled with self-assessment questionnaires and expert annotations.
Published September 17, 2024, this scoping review in The Lancet Digital Health examined ethical discussions surrounding generative AI in health care, including ChatGPT and other models used to synthesise data such as images for research and practical purposes. The authors found that ethical concerns have been widely noted but not translated into operational solutions.
Published March 17, 2026, this peer-reviewed review in BioNanoScience examines how artificial intelligence and machine learning are used to design and characterize nanoparticles for medical use. It describes AI models that predict physicochemical attributes, optimize synthesis conditions, and analyze characterization data to improve targeted therapeutics.
As of the July 2026 publication date, the authors describe AI being embedded in clinical trial infrastructure and argue that opaque models risk amplifying disparities. They propose embedded transparency as a structural prerequisite for equitable trials and outline governance recommendations.
As of the July 2 2026 publication date, this peer-reviewed survey summarized the state of computational pathology foundation models that use self-supervised learning on unlabeled whole-slide images to support pathology tasks. It reported that these uni-modal and multi-modal models have shown promise for segmentation, classification, and biomarker discovery, while focusing its review on datasets, adaptation strategies, and evaluation tasks.
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