Latest AI and machine learning research in clinical trials for healthcare professionals.
Decoding neural states from pediatric EEG in naturalistic settings remains challenging due to signal noise, motion artifacts, and intersubject variability. This paper introduces Enriched Topological Features (ETF), a new approach integrating multiscale persistent homology (ℍ0/ℍ1), time-aggregated entropy via sliding windows, and Takens’ phase-space embeddings to classify gameplay versus resting st...
Clinical decision-making generates vast unstructured data that remain underexploited for trial recruitment. We present Patient2Sentence (P2S), a framework that transforms electronic health records into language-based representations to enable automated eligibility screening for oncology trials. Using synthetic patient records derived from three completed breast cancer studies (KATHERINE, MONARCH, ...
Artificial intelligence (AI)-based mobile health (mHealth) smartphone apps for skin cancer detection are increasingly available to the general populat...
Large language models (LLMs) are increasingly used in randomized clinical trial (RCT) screening, but their potential for sociodemographic bias remains...
Large language models (LLMs) are entering clinical workflows, yet their effect on clinical decisions and potential for harm are uncertain. We measured...
Despite the agricultural sector’s consistently high injury rates, formal reporting is often limited, leading to sparse national datasets that hinder e...
Clinicians rely on evidence from randomized controlled trials (RCTs) to decide on medical treatments for patients. However, RCTs often lack the granul...
As part of routine practice and documentation, emergency department (ED) clinicians routinely construct “one-liner” summaries—brief, information-rich ...
Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accurac...
Threatened miscarriage represents one of the most prevalent obstetric emergencies globally. Nevertheless, women experiencing first-trimester bleeding ...
Early identification of dementia risk is essential for preventive care and timely enrolment into disease-modifying interventions. Current approaches r...
Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been sh...
The rapid integration of Large Language Models (LLMs) into healthcare raises critical questions regarding their safety and reliability. While models o...
Large language models (LLMs) are evolving into diagnostic co-pilots, yet current benchmarks fail to test the integrated, stepwise reasoning required i...
Assistants incorporating large language models are increasingly applied in the context of health care, where they represent a promising means of expan...
To quantify the amount and certainty of evidence in Cochrane systematic reviews of interventions, and to describe how this evidence has evolved over t...
Intubation and mechanical ventilation are associated with high mortality. Accurately predicting which patients are at the highest risk of intubation c...
Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized tria...
Artificial Intelligence (AI) voice applications have the potential to address the unmet treatment needs among patients with depression and anxiety, bu...
LLMs have encoded a vast array of medical knowledge and are being integrated into clinical settings as decision-support tools to improve physician per...