Latest AI and machine learning research in surveys for healthcare professionals.
Intralesional corticosteroid injections (ILCS) are a common treatment for keloid lesions; however, many patients exhibit resistance, and some experience worsening of their keloids following treatment. To develop a machine learning (ML) tool capable of identifying factors that predict response to ILCS. A keloid-specific survey database was accessed in May 2024. Various clinical and demographic fact...
Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood-level factors significantly influence PND risk, particularly among women of color, but current machine learning models using electronic medical records (EMRs) rarely incorporate neighborhood characteristics. To determine whether integrating neighbor...
Patient satisfaction feedback is crucial for hospital service quality, but manual reviews are not possible due to their time-consumption, and traditio...
The most effective way to reduce mortality and morbidity among current smokers is to quit smoking. Although about half of smokers attempted to quit, o...
Artificial intelligence chatbots (AICs) are advanced systems capable of generating and processing human-like text, and are being increasingly integrat...
To assess the value of an AI-powered conversational agent in supporting diabetes self-management among adults with diabetic retinopathy and limited ed...
Large language models (LLMs) demonstrate human-level performance in three key domains: linguistic understanding, knowledge-based reasoning, and comple...
Delays in the diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) contribute to the significant morbidity of the condition, especially in the ...
Cognitive biases are an important source of clinical errors. Large language models (LLMs) have emerged as promising tools to support clinical decision...
Information overload in electronic health records (EHRs) hampers clinicians’ ability to efficiently extract and synthesize critical information from a...
This study addresses the critical implementation science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We d...
The unintended biases introduced by optimization and machine learning (ML) models are a topic of great interest to medical researchers and professiona...
Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...
This study explores clinician understanding and perception at site lead level towards machine learning (ML) decision support tools for paediatric rela...
Large Language Models (LLMs) show significant promise in medicine but are typically evaluated using neutral, standardized questions. In real-world sce...
Machine learning (ML) models are widely used to predict body mass index (BMI), yet their fairness across socioeconomic and caste groups remains uncert...
There is great potential for artificial Intelligence (AI) and machine learning (ML) to support decision making in emergency departments (ED), however ...
Natural language processing (NLP) can identify cohorts of patients with inflammatory bowel disease (IBD) from free text. However, limited sharing of c...
Prompt engineering has the potential to enhance large language models’ (LLM) ability to solve tasks through improved in-context learning. In clinical ...
Data extraction is a critical but resource-intensive step of the evidence review process. Whilst there is evidence that artificial intelligence (AI) a...