Latest AI and machine learning research in surveys for healthcare professionals.
Estimating causal effects in observational health data is challenging due to confounding by indication. Traditional approaches such as inverse probability of treatment weighting (IPTW) rely on correct model specification, which is difficult in high-dimensional settings. We implemented an offset-based double machine learning (Offset-DML) practical framework for estimating binary treatment effects o...
Multimorbidity poses significant healthcare challenges globally. Current assessment methods rely primarily on structured electronic health record (EHR) data, potentially missing valuable information contained in unstructured clinical notes. Natural language processing (NLP) techniques offer promising solutions for extracting comprehensive multimorbidity data from these unstructured sources. To ide...
Mild Cognitive Impairment (MCI) affects up to 20% of older adults and often progresses to dementia. While brief cognitive screening tools like the Mon...
Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While th...
Large language model (LLM)-assisted early warning system may help overcome existing barriers to timely depression diagnosis in patients with cardiovas...
To quantify the adoption pattern of an LLM-based clinical decision support system across private primary health facilities in Kenya (operated by Penda...
Artificial Intelligence models are increasingly used in healthcare, yet global performance metrics can mask variations in reliability across individua...
Long COVID is a well-documented post-viral syndrome, while post-vaccination syndrome (PVS) remains poorly characterized. Understanding their similarit...
Continuously and unobtrusively monitoring work-related stress may help combat its detrimental effects on mental and physical health. For work in offic...
Bias assessment is a crucial step in evaluating evidence from randomized controlled trials. The widely adopted Cochrane RoB 2, designed to identify th...
Large language models (LLMs) have demonstrated a unique ability to generate clinically accurate responses to patient questions, in some cases outperfo...
Small open-source medical large language models (LLMs) offer promising opportunities for low-resource deployment and broader accessibility. However, t...
The emergence of Large Language Models (LLMs) like ChatGPT presents significant opportunities for healthcare, yet raises concerns about accuracy, espe...
Machine learning (ML) algorithms are increasingly used to estimate propensity score with expectation of improving causal inference. However, the valid...
Large language models (LLMs) show promise for improving clinical reasoning, but they also risk inducing automation bias, an over-reliance that can deg...
The indicator cell assay platform (iCAP) is a novel next-generation approach for blood-based diagnostics that uses standardized cells as biosensors to...
Identifying attachment styles is important for clinical psychologists interested in better understanding their patients. Traditional methods for ident...
Structured recording of key information such as diagnoses is essential for safe, efficient patient care, but is currently done incompletely because it...
With a goal of unveiling mechanisms by which vaccines can provide protection against HIV-1 acquisition, several studies have explored correlates of ri...
Adolescent mental health represents a global public health crisis, yet traditional surveillance methods lack the scalability and predictive power need...