Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers built stacked deep learning ensembles to detect periapical lesions and stratify them by radiographic size on intraoral radiographs. Using 146 cropped and augmented images, five CNN backbones were combined with MLR and XGBoost meta-learners and evaluated on an internal hold-out test set.
Researchers adapted the individualized polysocial risk score, originally built for type 2 diabetes, to a disease-agnostic cohort of 17,857 adults at University of Florida Health. Using 13 individual-level social determinants of health, they trained XGBoost and logistic regression models to predict all-cause hospitalization within one year, testing multiple fine-tuning levels and sampling strategies.
By the publication date of 2026-08-25, a pilot study had collected breath samples from 17 melioidosis patients and 8 febrile controls and used two-dimensional gas chromatography mass spectrometry combined with machine learning feature selection to identify volatile signatures of infection and treatment course.
Researchers compared a domain-specific EfficientNet-B0 multi-task deep learning classifier trained on about 700 annotated optical microscope images against a zero-shot Claude Vision API augmented with expert human-in-the-loop guidance. Both were tested on the same independent test set for predicting microplastic shape/type, color, and surface texture, with the DL model reaching F1-scores of 91.2%, 88.5% and 85.1% and the VLM improving from 72-81% to 84-89% after refinement.
As of July 2026, a scoping review of literature from January 2022 to January 2026 identified only eight studies describing LLM-based chatbots that support multiturn dialogue for patients with cancer and informal caregivers. Most were prototype systems using ChatGPT-based models, some with retrieval-augmented generation, designed for information provision or emotional support.
This peer-reviewed conceptual analysis examines why small and medium-sized enterprises struggle to adopt AI despite its transformative potential. Using the technology-organization-environment framework combined with diffusion of innovations attributes, it identifies ten critical challenges across data access, skills, culture, infrastructure, and governance, and pairs them with context-sensitive solutions.
As of its July 2025 publication, this peer-reviewed review examined how trust in AI systems can be systematically measured, analyzing frameworks including the NIST AI Risk Management Framework, the AI Trust Framework and Maturity Model, and ISO/IEC standards around fairness, transparency, privacy and security.
This peer-reviewed review from July 2025 examines how Digital Twins that are continuously updated by real-world data, when coupled with Artificial Intelligence, are being applied to healthcare, with emphasis on movement rehabilitation over the past seven years. It reports that this combination is reshaping care by streamlining diagnostic workflows, improving disease management, and enabling experimentation and predictive modeling without direct patient risk.
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