Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining
momentum: the increased attention comes partly with the view that participation opens the …

Background Artificial intelligence (AI) is often heralded as a potential disruptor that will
transform the practice of medicine. The amount of data collected and available in health …

Despite the growing consensus that stakeholders affected by AI systems should participate
in their design, enormous variation and implicit disagreements exist among current …

Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but
clinician acceptance remains a critical barrier. We developed a novel decision support …

Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early
diagnosis, reducing the medical administrative burden, aiding drug development …

The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …

Abstract Machine learning-based clinical decision support tools for sepsis create
opportunities to identify at-risk patients and initiate treatments at early time points, which is …

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in
many aspects of our daily lives, but their implementation in healthcare is still limited. This …

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist
with the early recognition of sepsis may improve outcomes, but relatively few studies have …

In this viewpoint article we discuss algorithmic decision-making systems (ADMS), which we
view as organizational sociotechnical systems with their use in practice having …