Abstract
With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.

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.

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[No abstract available]

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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
A sincronização online de ginástica potencia novas possibilidades para melhorar o bem-estar físico e social das pessoas com restrições de deslocação. A nossa proposta passa pela criação de uma plataforma 3D - Online Gym - que permita que os utilizadores interajam e participem em sessões online de ginástica em grupo através do Microsoft Kinect. No presente artigo, com vista à concretização desta proposta, analisamos três alternativas tecnológicas para a implementação de serviços de deteção de movimentos que possam ser integrados em plataformas gráficas multiutilizador. Nos resultados, expõem-se as características de cada uma e o impacte respetivo para sua utilização na concretização desta proposta ou outras iniciativas similares.