Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers built an 18-feature model across basic information, training, fitness and lifestyle dimensions for 800 Chinese university football players from a Kaggle dataset, comparing 10 algorithms and finding SVM best at 95.6% accuracy with SHAP highlighting stress, sleep and balance as key predictors.
On 2025-12-19, a peer-reviewed study in Communications Medicine described AIPatient, a simulated patient system powered by six LLM-based agents and a knowledge graph derived from MIMIC-III. Testing reported 94.15% accuracy on EHR-based medical QA, high validity, accessible readability scores, and stable performance across robustness tests, with a medical student user study finding high fidelity and educational value.
In a study published June 22, 2024, two hip preservation surgeons graded ChatGPT 3.5 answers to ten common hip arthroscopy questions drawn from patient education sites, using an A-to-D scale and readability scores FRES and FKGL.
Researchers assessed an AI-driven hierarchical medical system for chronic disease management in China using 2024 National Health Commission monitoring data and 12,468 patient follow-up records from three provinces, structured around data collection, decision intervention, resource scheduling, and outcome feedback.
From October 5 to 11, 2025, researchers tested five large language models on 20 pulmonary tuberculosis questions spanning five themes, generating 100 responses and rating them with C-PEMAT-P, GQS, and seven readability measures. GPT-5 ranked highest on C-PEMAT-P followed by Doubao, GQS was similar across models, and models differed significantly on several readability indices.
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.
Published February 20, 2026, this peer-reviewed survey in Cognitive Computation reviews XAI-driven data mining for self-defending IoT systems. It describes how IoT expansion in smart cities, healthcare, and industrial automation creates need for real-time, scalable security, and how XAI methods aim to detect anomalies and support automated decisions with transparent reasoning.
ace