Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
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.
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.
In a January 2019 peer-reviewed survey, researchers described two persistent barriers for AI: data siloed as isolated islands and tightening privacy and security requirements. They proposed a comprehensive secure federated-learning framework that includes horizontal, vertical, and transfer variants, and surveyed existing work on definitions, architectures, and applications.
In work published August 20, 2021, researchers built on the CASP14-era DeepMind approach to protein folding. They developed RoseTTAFold, a three-track network that processes sequence, distance, and coordinate information simultaneously, achieving accuracies approaching those of DeepMind.
Researchers developed NA-DyCNN, a lightweight noise-aware dynamic convolutional network for OCT-based retinal disease classification that explicitly models post-acquisition speckle variability during training to improve cross-scanner robustness.
This review from August 2026 summarizes how artificial intelligence applied to standard electrocardiograms has been tested in pediatric and congenital heart disease. It reports that deep learning models have been shown to identify arrhythmias, ventricular dysfunction, and CHD, and are being extended to predict future risk and to analyze wearable and telemetry data.
A peer-reviewed review published August 13, 2026 examined how AI-enabled medical devices challenge traditional safety-risk management. Drawing on 19 academic and regulatory sources, it found ISO 14971, AAMI CR34971 and the EU AI Act each cover parts of device safety and algorithmic governance but remain fragmented in practice.
A peer-reviewed survey study from January to April 2025 asked 52 US dermatology and dermatopathology professionals to rate AI-simplified versions of six fictitious dermatopathology reports. One version used Basic ChatGPT-4.0 with a simple prompt and the other used a custom DermDecoder GPT with a structured 489-word prompt, evaluated for factualness, completeness, and potential harm.
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