Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
On 2026-08-13, a peer-reviewed study reported machine-learning-based phenomapping of 106,490 keratinocyte carcinoma patients from the Danish Skin Cancer Registry (2014-2022). The model derived seven clusters ranging from young, well-educated, high-income, medically noncomplex females with low-risk BCCs to highly comorbid patients with more SCCs and immunosuppressive drug exposure.
Researchers built a multi-stage video-based framework for signalized intersections that combines YOLOv8 detection with OC-SORT tracking to extract vehicle trajectories, uses a dynamic scaling Conflict Region of Interest to reduce data volume, and predicts conflicts with a Spatio-Temporal Graph Attention Network followed by causal forest interpretation. Tested on field video from an intersection in Nanning, China, the ST-GAT model outperformed existing deep learning architectures on training and testing sets.
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.
Published 19 November 2025, this peer-reviewed review in Administrative Sciences consolidates recent literature on technology-driven change in human resource management. It examines AI, automation and data analytics as drivers and assesses their impact on talent acquisition, development and retention and on organizational design.
Published November 11, 2025, this peer-reviewed paper examines how AI, RPA, blockchain, and immersive technologies are redefining strategic human resource management. Drawing on a systematic literature review, institutional reports, and illustrative cases from IBM, Walmart, Unilever, and UiPath, it argues human capital has shifted from passive input to strategic enabler and proposes a conceptual model linking emerging technologies, SHRM practices, and competitiveness.
A Viewpoint published April 23, 2024 in The Lancet Digital Health examines ethical and regulatory challenges of large language models in medicine, arguing their architecture and emergent abilities set them apart from prior AI and NLP tools.
Published April 8 2024, this peer-reviewed scoping review examines how AI is being adopted in recruitment and selection to enhance HR efficiency, and how that adoption raises concerns about algorithmic decision-making for job seekers.
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