ACM Transactions on Computer-Human Interaction

We review HCI history from both the perspective of its 1980s split with human factors and its nature as a discipline. We then revisit human augmentation as an alternative to user friendliness that seems particularly relevant in the areas of inclusive ...

The future of Human-computer interaction (HCI) communication requires researchers to develop a strong understanding of the factors that influence design practitioners. As a step towards building that understanding, based on interviews conducted with ...

Picture interactions are key to daily and long-term social connections between families and communities, especially through reminiscence. Across the nearly 200-year history of domestic photography, this social reminiscence has been accomplished largely ...

When studying identity transitions, interview participants can find it difficult to reflect on their transitions and recall specific details related to past experiences. We present a new approach to enable participant reflection on past identity ...

This article describes the successful collaboration “in the wild” between Clinical Documentation Integrity Specialists (CDIS) and an Artificial Intelligence (AI)-embedded software to conduct knowledge work. CDIS review patient charts in near real-time to ...

Recent advances in AI and machine learning (ML) promise significant transformations in the future delivery of healthcare. Despite a surge in research and development, few works have moved beyond demonstrations of technical feasibility and algorithmic ...

Recent developments in AI have provided assisting tools to support pathologists’ diagnoses. However, it remains challenging to incorporate such tools into pathologists’ practice; one main concern is AI’s insufficient workflow integration with medical ...

In the development of robotics and Artificial Intelligence (AI) for healthcare, human-centered approaches seek to meet the requirements of healthcare practice and address social and ethical aspects proactively. In this work, an important but neglected ...

Bringing AI technology into clinical practice has proved challenging for system designers and medical professionals alike. The academic literature has, for example, highlighted the dangers of black-box decision-making and biased datasets. Furthermore, end-...

Artificial Intelligence (AI) in medical applications holds great promise. However, the use of Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely difficult to introduce clinician-facing ML-based systems in practice, ...