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To understand the landscape of privacy preserving record linkage (PPRL) applications in public health, assess estimates of PPRL accuracy and privacy, and evaluate factors for PPRL adoption.

Medical practitioners analyze numerous types of data, often using archaic representations that do not meet their needs. Pneumologists who analyze lung function exams must often consult multiple exam records manually, making comparisons cumbersome. Such shortcomings can be addressed with interactive visualizations, but these must be designed carefully with practitioners' needs in mind.

There are significant disparities in access and utilization of patient portals by age, language, race, and ethnicity.

This study experimentally evaluated how well lay individuals could interpret and use 4 types of electronic health record (EHR) patient-facing immunization visualizations.

To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices.

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models.

Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression.

We sought to analyze interactive visualizations and animations of health probability data (such as chances of disease or side effects) that have been studied in head-to-head comparisons with either static graphics or numerical communications.

This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical [...]

Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.