Abstract
This paper discusses some recent developments in collaborative healthcare research considering settings where human clinicians collaborate through or interact with artificial intelligence (AI)-enabled systems to enhance clinical diagnosis, treatment procedures, and decision-making practices. Through a detailed examination of the potential gaps, implications, and challenges for health professionals and patients, this work explores typical AI-based collaborative clinical workflows and infrastructures that involve tasks such as patient data analysis, medical imaging, and event prediction. A brief synopsis of published research reveals inherent sociotechnical barriers concerning interoperability, data scarcity, bias amplification, trust, and transparency. It also highlights risks related to inadequate model and interface design, the oversimplification of clinical processes (e.g., lack of shared situational awareness), institutional misalignment (e.g., cultural norms and practices shaping how clinicians coordinate their efforts and make decisions based on AI recommendations), and commercial data manipulation that threatens patient care. © 2025 IEEE.

Abstract
[No abstract available]

Abstract
Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

Abstract
Microtasks have become increasingly popular in the digital labor market since they provide easy access to a crowd of people with varying skills and aptitudes to perform remote work tasks that even the most capable algorithmic systems are unable to complete in a timely and efficient fashion. However, despite the latest advancements in crowd-powered and contiguous interfaces, many crowd workers still face some accessibility issues, which ultimately deteriorate the quality of the work produced. To mitigate this problem, we restrict attention to the development of two different web-based mini-games with a focus on cognitive personalization. We have conducted a pilot gamified experience, with six participants with autism, dyslexia, and attention deficit hyperactivity. The results suggest that a web-based mini-game can be incorporated in preliminary microtask-based crowdsourcing execution stages to achieve enhanced cognitive personalization in crowdsourcing settings.

Abstract
This study discusses the intricate relations between generative artificial intelligence (AI) and music composers. Based on a previous rapid review of recent literature, it reinforces a gap and suggests the need to develop human-centered generative AI design strategies prioritizing cultural artistic (and non-artistic) aspects. We posit that AI-based music generation solutions should resonate with the cultural diversity of stakeholders who are impacted by these systems in practice. The paper highlights the significance of metaphorical design as an effective method in human-AI music co-creation by leveraging familiar interfaces and features that are rooted in everyday objects and cognitive models derived from real-world settings. Our insights illustrate possible ways of (re)framing human-AI metaphorical design to shape perceptions and facilitate seamless interactions between humans and intelligent systems in music co-creativity, particularly at the compositional level. At the heart of this research is the alignment of AI-driven music creation systems with user needs, values, and expectations that vary from culture to culture and thus require a continuous and transparent adaptation of the technology in use to accommodate individual preferences and the socio-algorithmic specificities underlying musicians’ activities.