Background Racial bias is a key concern regarding the development, validation, and
implementation of machine learning (ML) models in clinical settings. Despite the potential of …

Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

In August 2022 the Department of Health and Human Services (HHS) issued a notice of
proposed rulemaking prohibiting covered entities, which include health care providers and …

Automatically summarizing patients' main problems from daily progress notes using natural
language processing methods helps to battle against information and cognitive overload in …

Background Substance misuse is a heterogeneous and complex set of behavioural
conditions that are highly prevalent in hospital settings and frequently co-occur. Few hospital …

Objective Accurate and rapid phenotyping is a prerequisite to leveraging electronic health
records for biomedical research. While early phenotyping relied on rule-based algorithms …

Historically, researchers and consumers have noticed a decrease in quality when applying
NLP tools to minority variants of languages (ie Puerto Rican Spanish or Swiss German), but …

Abstract Healthcare Artificial Intelligence (AI) readiness is defined as adequate knowledge,
attitudes, and skills such that health professionals are prepared for AI use in prevention …

Background: There is increasing concern for the potential impact of health care algorithms
on racial and ethnic disparities. Purpose: To examine the evidence on how health care …

Algorithm discrimination becomes a serious problem. Its far-reaching implications have
attracted the interest of many studies in a variety of disciplines, including computer science …