Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published 2026-09-07 as a peer-reviewed protocol, the study outlines validation of Dynamic Assessment of Dyspnoea and Ventilation on Exercise (DyVe-X), an AI-based software that continuously assesses dyspnoea intensity and mechanical-ventilatory reserve depletion during incremental cardiopulmonary exercise testing in 1161 tobacco-exposed subjects.
This peer-reviewed review examined 26 studies from 2021 to 2024 on machine learning and deep learning for autism spectrum disorder prediction, focusing on data augmentation and feature selection methods. It categorized augmentation into conventional transformations and GAN-based synthetic generation, and feature selection into filter, wrapper, and embedded approaches, evaluating study quality with the Prediction Model Risk of Bias Assessment Tool.
On 2026-09-08, a study in Physical Chemistry Chemical Physics reported machine learning models that predict band gap energies across various halide perovskite types from atomic and structural properties. The work tested ensemble tree-based algorithms including random forest, gradient boosted trees, and XGBoost and examined feature importance to link descriptors to band gaps.
A 2026 conceptual review in Medical Education examined how artificial intelligence used to generate, score and interpret assessments affects validity. Using Kane's four inferences, the authors mapped threats such as prompt instability and domain shift and noted that AI assessment is advancing without formal scrutiny comparable to clinical AI.
Researchers retrospectively reviewed 125 orthodontic claims approved for compensation by the Danish Dental Compensation Association from September 2019 to August 2024. They applied Eindhoven incident analysis and AI-assisted qualitative analysis using a large language model to identify root causes and rate perceived avoidability on a 6-point scale.
A 2026 perspective in AI and Ethics examines multimodal AI that fuses images, speech, behavior, physiological signals and text into unified representations for cross-modal inference and synthesis in biomedicine. The authors note potential clinical benefits while warning that inferred data can be materialized as images or clinical text and inserted into records without provenance.
A mixed-methods study examined how 19 professional writers and 30 avid readers understood authenticity in writing produced with AI assistance. Writers completed short writing tasks using both personalized and non-personalized GPT-4 suggestions, while readers evaluated passages written independently or with either form of AI support.
By June 2026, a peer-reviewed paper analyzed how AI and AIGC are being integrated into newsrooms, from data mining to co-creation of news products, increasing efficiency and output volume while prompting questions about human professionalism and editorial control.
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