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The goal of this study was to investigate the association of perceived discrimination with health outcomes and disparities.

This article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs).

To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors that can impact their adoption, including overall performance, calibration, fairness, and resilience to privacy protections that reduce data fidelity.

Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated with false positives. This study aims to evaluate the impact of confounders on the specificity of single-outcome prediction models and assess the effectiveness of a multi-class architecture in mitigating outcome conflation.

This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

(1) Describe the evolution of Health Information Exchanges (HIEs) into Health Data Utilities (HDUs); (2) Provide motivation for HDUs as a public strategic investment target.

Although efforts to effectively govern AI continue to develop, relatively little work has been done to systematically measure and include patient perspectives or expectations of AI in governance. This analysis is designed to understand patient expectations of healthcare AI.

Direct electronic access to multiple electronic health record (EHR) systems through patient portals offers a novel avenue for decentralized research. Given the critical value of patient characterization, we sought to compare computable evaluation of health conditions from patient-portal EHR against the traditional self-report.

This study aimed to assess the efficacy of a biomedical informatics boot camp with regard to improving the skill sets of its participants.The University at Buffalo hosts a free, virtual biomedical informatics boot camp annually. Lectures covering various subject matters are offered, for example, general programming, machine learning, natural language processing, and clinical decision support. Once the 2023 boot camp had concluded, an anonymous voluntary survey was distributed.Seventy percent of [...]