Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of
improving patient outcomes and optimizing healthcare delivery. By harnessing machine …

Objective Although artificial intelligence (AI) has demonstrated promise in enhancing breast
cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various …

Objectives Leveraging artificial intelligence (AI) in conjunction with electronic health records
(EHRs) holds transformative potential to improve healthcare. However, addressing bias in …

Objectives: There is growing attention to health equity in health informatics research.
However, the literature lacks a comprehensive framework outlining critical considerations for …

Background The adoption of predictive algorithms in health care comes with the potential for
algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been …

Global access to statistically and clinically representative patient health data holds potential
for advancing disease research, enhancing patient care, and accelerating drug …

Objectives: Artificial intelligence (AI) applications utilizing electronic health records (EHRs)
have gained popularity, but they also introduce various types of bias. This study aims to …

Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for
the development of digital health applications. Traditionally done via manual abstraction …

Rural health disparities are well-documented and continue to jeopardize the long-term
health and wellness for the millions of individuals who live in rural America. The disparities …

Artificial intelligence models represented in machine learning algorithms are promising tools
for risk assessment used to guide clinical and other health care decisions. Machine learning …