Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published September 23 2025 as a peer-reviewed review, the article surveys how AI is being applied across healthcare, from analyzing electronic health records and medical imaging to supporting drug discovery, predictive analytics, telemedicine and wearable biosensors, with emphasis on low-resource and remote settings.
On 2025-11-05, Applied Sciences published a comprehensive review of machine learning for financial fraud detection. The authors surveyed supervised, unsupervised and hybrid approaches across credit card, financial statement, insurance and money laundering fraud, reviewed datasets and metrics, and included two case studies applying supervised models to real-world banking data.
On 2026-01-13, a peer-reviewed integrative review in Diseases synthesized 2015-2025 literature on cardiovascular diseases as an interconnected continuum, examining how multi-omics data combined with AI-enabled imaging and digital tools are applied across seven major condition clusters.
Researchers reported a self-optimizing automated workflow for materials design that couples crystal structure prediction with an attention-coupled neural network interatomic potential. The system samples local minima of the potential energy surface and iteratively refines itself to improve generalization to unknown structures while reducing manual intervention.
This comparative study analyzed China and South Korea's distinct approaches to governing AI journalism and algorithmic news curation, examining policy documents and evidence from Toutiao and Naver to assess how each balances fairness and accountability.
A May 2026 review in Graefe's Archive describes AI combined with multimodal retinal imaging as a non-invasive approach to detect and monitor systemic vascular and neurodegenerative conditions. It outlines how fundus photography, OCT, OCTA and metabolic-sensitive imaging capture retinal vascular and nerve changes that reflect cardiovascular, metabolic and neurological disease, analyzed with deep learning and multimodal fusion.
This peer-reviewed analysis from May 2026 examines how AI and robotics ecosystems are entering the operating room, using multimodal data from patients, staff, robots and the environment for workflow recognition, performance benchmarking and decision support, while robots evolve toward autonomous systems with human-in-the-loop control.
A systematic review of 27 studies including more than 22,000 participants across 12 countries examined barriers and facilitators to using LLM-based conversational agents in mental healthcare. Using CFIR, the authors found 24/7 availability was the most reported facilitator in 26 of 27 studies, while inadequate crisis detection was the most reported barrier in 21 of 27 studies.
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