AI Medical Compendium Topic:
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Nostalgia encourages exploration and fosters uncertainty in response to AI technology.

The British journal of social psychology
The burgeoning progress of cutting-edge technology paradoxically evokes nostalgia. How does this emotion influence responses to innovative technology, such as Artificial Intelligence (AI)? We hypothesized that two pathways operate concurrently. First...

Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured elec...

Implications of An Evolving Regulatory Landscape on the Development of AI and ML in Medicine.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The rapid advancement of artificial intelligence and machine learning (AI/ML) technologies in healthcare presents significant opportunities for enhancing patient care through innovative diagnostic tools, monitoring systems, and personalized treatment...

Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning cu...

Predicting Suicides Among US Army Soldiers After Leaving Active Service.

JAMA psychiatry
IMPORTANCE: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions.

Physician Opinions on Artificial Intelligence Chatbots In Dermatology: A National Online Cross-Sectional Survey of Dermatologists.

Journal of drugs in dermatology : JDD
BACKGROUND: Artificial intelligence chatbots (AIC) have sharply risen in popularity. Dermatology, heavily involving visual, clinical, and pathological pattern-recognition techniques, will be impacted by AIC. Thus, this study aims to categorize the at...

AHRQ's digital healthcare research program: 20 years of advancing innovation and discovery.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To reflect on the achievements of the Agency for Healthcare Research and Quality's (AHRQ) Digital Healthcare Research Program over the past 20 years, evaluate its impact on US healthcare quality and safety, and outline current and future ...

Tailoring Risk Prediction Models to Local Populations.

JAMA cardiology
IMPORTANCE: Risk estimation is an integral part of cardiovascular care. Local recalibration of guideline-recommended models could address the limitations of existing tools.

Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of t...