September 10, 2026

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09/03/2026

research article

Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance

Blood glucose prediction is a critical component of next-generation diabetes technologies, such as artificial pancreas systems, where reliable performance is essential for safety and effectiveness. Although deep learning methods have achieved promising advances in this area, a critical gap remains in understanding the reproducibility and generalizability of these methods

Image credit: Jane Korsak

Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance

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09/03/2026

research article

Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial

The authors developed and deployed a real‑time, electronic health record‑integrated machine learning phenotype to identify emergency department patients with opioid use disorder for prospective clinical trial screening and buprenorphine initiation. They conducted a multi‑phase study across three emergency departments in a single health system from 2014-2025.

Image credit: Susan Wilkinson

Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial

09/02/2026

research article

Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms

Using over five million ECGs from diverse healthcare cohorts, the authors found that the learned representations capture not only clinically relevant cardiac information but also characteristics of the populations and healthcare systems from which the data originated.

Image credit: Joachim Schurle

Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms

08/10/2026

review

State of abdominal CT datasets: A critical review of bias, clinical relevance, and real-world applicability

AI has the potential to make analyzing abdominal CT images faster and more reliable, however AI systems are only as good as the datasets they are trained on. The authors reviewed 45 publicly available abdominal CT datasets, covering 47,049 scans, to understand how well they support real-world medical applications.

Image credit: Danaei et al

07/10/2026

research article

Evaluating the quality, reliability and readability of digital and artificial intelligence resources for adults with cancer who have significant caregiving responsibilities for children

Families often report searching the internet for guidance on how best to support children when a significant adult has cancer.

Image credit: Pawel Czerwinski

07/09/2026

research article

Large language model-generated clinical summaries in emergency departments: A blinded comparison study

In this blinded study of 99 ED encounters, emergency physicians evaluated paired LLM- and physician-authored summaries on accuracy, completeness, and clinical utility, indicating their overall preference with free-text explanation. LLM-generated one-liner summaries were produced using k-nearest-neighbor few-shot prompting.

Image credit: Pawel Czerwinski

07/09/2026

research article

Doctors vs. Algorithms: Physicians, too, struggle to learn from evidence that contradicts AI suggestions

Despite their widespread adoption, Artificial Intelligence-based Patient Classification Systems sometimes rely on incorrect, outdated, or incomplete data, which can lead to inaccurate outputs.

Image credit: Praewthida K.

06/18/2026

research article

Sunscreen is overwhelmingly promoted on TikTok, but content with misinformation exhibits proportionally high levels of audience interaction

This study examined the presence, engagement levels, and characteristics of...

Image credit: Onela Ymeri

06/11/2026

research article

A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data

The authors introduce the FEDE (high FidElity Digital brain modEl) pipeline, generating anatomically accurate brain digital twins from imaging data.

Image credit: Fabbrizzi et al

06/11/2026

review

Clinical artificial intelligence applications of vision-language foundation models

Vision-language models (VLMs) represent a transformative advance in generative artificial intelligence (AI), using multimodal data processing to enhance clinical decision-making and workflow efficiency.

Image credit: Maria Maximova

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