Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers described FedMediFormer-XAI, a framework that combines federated learning, multimodal transformers, diffusion-based data augmentation, graph neural networks for drug recommendation, and explainable AI to address fragmented diabetes data, privacy concerns, and lack of personalized guidance. It was tested on diverse inputs including clinical records, glucose monitoring, retinal fundus images, wearable sensors, and pharmacological information.
On September 9, 2026, a peer-reviewed comparative study reported testing ChatGPT, Gemini, and Microsoft Copilot on 20 radiological cases split between congenital anomalies and tumors. Each system received the same questions and images and was scored for diagnostic accuracy and explanatory completeness, with ChatGPT scoring 19 correct, Gemini 17, and Copilot 16.
By September 7, 2026, researchers reported developing an AI-assisted ensemble model to detect skull fractures in neonates and infants from plain radiographs. Using a retrospective set of 1,184 patients from 2010-2021, they preprocessed images with CLAHE and trained three CNNs, with DenseNet-121 performing best, then combined AP and lateral views into an ensemble that achieved 0.938 AUC and 91.6% accuracy on an external set of 460 images.
On 2026-09-08, a peer-reviewed study reported an intelligent analytical model combining surface-enhanced Raman spectroscopy with machine learning to identify seven clinically common Nocardia species from cultured clinical isolates. Using 46 strains and 64 spectra per strain, the team compared nine models and found the support vector machine achieved 99.47% accuracy.
Researchers developed TrialTriage, a semiautonomous prescreening workflow on the n8n platform that uses large language model extraction from clinical narratives and investigator email replies plus a 7-criterion deterministic rule engine to classify phase I oncology trial eligibility, automatically emailing investigators when information is missing and reclassifying after reply capture.
A systematic review and meta-analysis to February 9, 2025, evaluated artificial intelligence for diabetic retinopathy assessment using ultra-widefield color fundus images, which capture a larger retinal area without pupil dilation. Of 527 records, 17 studies were reviewed and four were meta-analyzed, all using Optos software, to estimate sensitivity and specificity for AI-driven screening.
On 2025-05-13, a peer-reviewed framework was described for evaluating LLMs that automate summarising consultations into clinical notes. It combines an error taxonomy, iterative experimental comparisons, a clinical safety harm assessment, and the CREOLA interface, tested across 18 configurations with 12,999 clinician-annotated sentences.
On 2025-10-04, a peer-reviewed review in Foods synthesized 25 studies selected from 124 Scopus records from 2005-2025 to map machine learning use for quality control in food production. It organized findings into six domains covering quality applications, defect detection and visual inspection, ingredient optimization, packaging sensors and predictive QC, supply chain traceability, and Industry 4.0 models.
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