Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
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.
In 76 eyes treated with silicone oil endotamponade for rhegmatogenous retinal detachment, researchers used an automated OCT segmentation tool and a random forest classifier to track retinal layer changes between oil insertion and removal and to predict categorical best-corrected visual acuity change.
Between August 2023 and July 2024, four radiologists interpreted 4577 screening and diagnostic mammograms in a prospective alternating-month design where a commercial AI system was shown or hidden. Reading times from PACS logs, cancer detection rates, and abnormal interpretation rates were compared between AI-assisted and non-AI-assisted months, with reading time analysis restricted to 2917 cases under five minutes.
On 2026-08-12, a peer-reviewed study described CURL-AID, a fully automated framework that segments the posterior mitral annulus and left ventricular wall on parasternal long-axis echocardiograms, tracks tissue motion, and classifies posterior systolic curling using nine kinematic features in 100 retrospectively analyzed patients.
This peer-reviewed review from August 2024 examines the evolution of geoscience inquiry from traditional physics-based numerical models to modern data-driven ML and DL approaches enabled by advances in AI and data collection. It describes how data-driven models leverage large geoscience datasets and how hybrid models that embed domain knowledge aim to improve efficiency and reduce training data needs.
On 2024-08-09, a peer-reviewed paper in Informatics reported a systematic review of 37 sources on generative AI ethics, identifying concerns spanning privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities, with particular attention to convincing deepfakes and synthetic media.
Published September 2024, this peer-reviewed study interviewed ten academic and commercial deepfake developers and ethics representatives to understand what values guide professional development of synthetic audio-visual media and how incentives shape their sense of agency.
A September 2024 peer-reviewed study examined AI adoption for academic purposes in a developing country using a UTAUT model extended with trust and privacy, surveying 310 teachers, researchers, and students who use AI.
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