Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published January 2024, this IEEE Access survey reviews how machine learning, deep learning and reinforcement learning are applied to cybersecurity tasks such as malware detection, intrusion detection and vulnerability assessment, including evaluation of ChatGPT-like tools on both defensive and offensive sides.
In a retrospective single-centre pilot, investigators developed a YOLOv11-based model to automatically detect and segment three key landmarks during robot-assisted single-port transvesical enucleation of the prostate using 611 annotated frames from 37 procedures performed by one expert surgeon.
A multisite retrospective validation study in a US tertiary health system compared four automated EMR retrieval methods to manual chart adjudication for ischaemic stroke/TIA, MI, HF exacerbation/hospitalisation and composite MACE in 2258 patients treated with immune checkpoint inhibitors and 1426 patients who underwent TAVR. The zero-shot LLM workflow achieved the highest AUCs for most outcomes, while ICD-based retrieval remained competitive.
On 2026-08-13, researchers reported developing an AI reinforcement learning model using Swedish population data to determine cost-effective diagnostic pathways for chronic breathlessness. The model evaluated 16 clinically relevant conditions with associated tests and costs, generating tailored sequences for subgroups defined by sex and smoking exposure.
A systematic literature review published August 27, 2025 analyzed 38 publications on generative AI in digital art, documenting how text-to-image models that produce high-quality art in seconds are reshaping the ecosystem and prompting responses from artists, consumers, galleries, and policymakers.
As of August 2025, this peer-reviewed mixed-method review synthesized existing literature on AI in healthcare, finding that AI-driven decision support systems reshape clinical practice by offering enhanced decision-making while simultaneously being linked to deskilling and upskilling inhibition among medical professionals.
This peer-reviewed survey from September 2025 provides a comprehensive overview of Explainable Artificial Intelligence, covering foundational concepts, terminology, taxonomy of methods and application domains including healthcare, finance, law and autonomous systems.
By September 2025, a peer-reviewed review in Food Science & Nutrition described an emerging model that combines continuous glucose monitors, AI-driven meal planning, and mobile health apps to tailor nutrition for diabetes and obesity based on genetic, epigenetic, microbiome, and real-time metabolic data.
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