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Deidentification of personally identifiable information in free-text clinical data is fundamental to making these data broadly available for research. However, there exist gaps in the deidentification landscape with regard to the functionality and flexibility of extant tools, as well as suboptimal tradeoffs between deidentification accuracy and speed. To address these gaps and tradeoffs, we develop a new Python-based deidentification software, pyDeid.

To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).

To pilot a digital health technologies ecosystem known as project SingaporeWALK (Wearables and Apps for Community Living and Knowledge) that build capacity in older adults, senior center managers, health coaches, and caregivers in using health technologies (eg, wearables, apps, exergames) collaboratively in a gamified way for active aging.

The resurgence of syphilis in the United States presents a significant public health challenge. Much of the information needed for syphilis surveillance resides in electronic health records (EHRs). In this manuscript, we describe a surveillance platform for automating the extraction of EHR data, known as SmartChart Suite, and the results from a pilot.

This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.

Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communication in home healthcare settings to develop an artificial intelligence-based screening tool for early detection of cognitive decline.

The rapid advancement of artificial intelligence (AI) has led to significant transformations in health and healthcare. As AI technologies continue to evolve, there is an urgent need to establish a unified framework that guides the design, implementation, and evaluation of AI-driven interventions across individual and population health contexts.

The phenome-wide association study (PheWAS) systematically examines the phenotypic spectrum extracted from electronic health records (EHRs) to uncover correlations between phenotypes and exposures. This review explores methodologies, highlights challenges, and outlines future directions for EHR-driven PheWAS.

The automatic detection of stance on social media is an important task for public health applications, especially in the context of health crises. Unfortunately, existing models are typically trained on English corpora. Considering the benefits of extending research to other widely spoken languages, the goal of this study is to develop stance detection models for social media posts in Spanish.

Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.