Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
On 2026-08-04, a peer-reviewed study reported development of a ferroptosis- and lipid metabolism-related prognostic signature for colorectal cancer using transcriptomic data from TCGA-COAD and GSE39582, WGCNA, and a machine learning pipeline of forward stepwise Cox regression combined with Random Survival Forest. The RiskScore was an independent predictor of overall survival and stratified immune features, and CRY2 emerged as a key signature gene validated by knockdown experiments.
Published 4 August 2026 in Internal Medicine Journal, this peer-reviewed perspective examines rural mental health inequity in Australia and argues AI could help with earlier identification of distress and safer, more timely triage when used with telehealth and clinical decision support.
This October 2025 scoping review synthesized 65 empirical studies up to mid-April 2025 on AI-mediated informal language learning, defined as self-directed out-of-class L2 learning with AI tools. It found a nascent, fast-growing field after ChatGPT's release, concentrated in East Asia and dominated by cross-sectional designs with limited theory.
This scoping review mapped evidence published up to March 2026 on AI in healthcare in Sub-Saharan Africa, focusing on marginalised populations. Searching four databases and grey literature, the authors included 23 sources and synthesised them thematically.
In August 2024 the EU Artificial Intelligence Act entered into force as a legally binding framework governing AI development and use across the EU. The peer-reviewed overview published September 7, 2024 explains that healthcare is a top deployment sector and that the Act will significantly reform national policies and practices on health technology by imposing new obligations on tech developers, healthcare professionals, and public health authorities.
On September 2, 2024, a peer-reviewed commentary in Behaviour & Information Technology brought together nine experts to assess generative AI and large language models in education. The authors noted capabilities to enhance learning design, regulation of learning, and automated content, feedback, and assessment, while also flagging limitations, disruptions, ethical consequences, and misuses.
Published September 28, 2024, this peer-reviewed review in npj Digital Medicine examined 142 studies of human evaluation methods for large language models in healthcare. The authors assessed how evaluations are designed and conducted, including dimensions evaluated, sample characteristics, evaluator recruitment, metrics, and analysis, and found systematic gaps in reliability, generalizability, and applicability.
This peer-reviewed paper from October 2024 examines why AI-driven decision-making systems becoming instrumental in the public sector often fail to translate predictive accuracy into better decisions, analyzing five challenges including distribution shifts, label bias, past decisions shaping data, competing objectives, and human-in-the-loop effects.
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