Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
This peer-reviewed review from August 2026 examines how machine learning, deep learning, NLP, and generative modeling are being used across medicinal chemistry, including target discovery, virtual screening, property prediction, de novo design, fragment optimization, ADMET assessment, and clinical trial design, with emphasis on multimodal data fusion and human-AI collaboration.
This discursive paper from the Journal of Advanced Nursing analyzes AI integration in nursing through the Fundamentals of Care framework. It reports that AI offers benefits for workflow optimization and clinical precision via predictive analytics and automated documentation, and argues that reducing administrative burden could theoretically release time for relational care.
A retrospective study in the Maryland Suicide Data Warehouse tested whether social determinants of health improve machine learning suicide prediction. Using 1214 suicide deaths and 815,544 living patients linked to census tract data, three algorithms were trained and validated across demographic, clinical, and individual and geographic SDoH inputs.
Researchers developed an unsupervised, interpretable AI framework to define tissue-agnostic cellular morphometric biomarkers that capture conserved tumor microenvironment organization across gastrointestinal organs. Discovered in colorectal cancer slides and validated in gastric and esophageal cancers in a 2,602-patient multi-center cohort, a 13-marker signature showed prognostic value and enabled risk stratification of precancerous lesions and early-stage cancers.
As of July 28 2025, this peer-reviewed review synthesized machine learning and statistical approaches for forecasting and classifying water quality, focusing on hybrid models that combine multiple methods. It assessed their application to rivers in Malaysia facing pollution from industrialisation, agriculture, and urban expansion, and reviewed standards and interpretability techniques.
Published August 25, 2025, this peer-reviewed review synthesized current machine learning methods for healthcare fraud detection, covering supervised, unsupervised, deep learning, and hybrid approaches like SMOTE-ENN, explainable AI, federated learning, and ensemble learning, and noted Medicare, LEIE, and Kaggle as common evaluation datasets.
A systematic review of 43 studies from 2020-2025 examined how artificial intelligence is transforming government decision-making, finding benefits in efficiency and data-driven service delivery alongside drawbacks including bias and transparency deficits.
A systematic literature review published August 27, 2025 analyzed 38 publications on generative AI in digital art, documenting how text-to-image models that produce high-quality art in seconds are reshaping the ecosystem and prompting responses from artists, consumers, galleries, and policymakers.
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