This experiment systematically examines the team dynamics (i.e. team emergent states and processes) in human-human-AI teams compared to pure human teams.

IntroductionArtificial Intelligence (AI) chatbots, which generate human-like responses based on extensive data, are becoming important tools in healthcare by providing information on health conditions, treatments, and preventive measures, acting as virtual assistants. However, their performance in aligning with clinical practice guidelines (CPGs) for providing answers to complex clinical questions on lumbosacral radicular pain is still unclear. We aim to evaluate AI chatbots' performance against CPG recommendations for diagnosing and treating lumbosacral radicular pain.MethodsWe performed a cross-sectional study to assess AI chatbots' responses against CPGs recommendations for diagnosing and treating lumbosacral radicular pain. Clinical questions based on these CPGs were posed to the latest versions (updated in 2024) of six AI chatbots: ChatGPT-3.5, ChatGPT-4o, Microsoft Copilot, Google Gemini, Claude, and Perplexity. The chatbots' responses were evaluated for (a) consistency of text responses using Plagiarism Checker X, (b) intra- and inter-rater reliability using Fleiss' Kappa, and (c) match rate with CPGs. Statistical analyses were performed with STATA/MP 16.1.ResultsWe found high variability in the text consistency of AI chatbot responses (median range 26%–68%). Intra-rater reliability ranged from “almost perfect” to “substantial,” while inter-rater reliability varied from “almost perfect” to “moderate.” Perplexity had the highest match rate at 67%, followed by Google Gemini at 63%, and Microsoft Copilot at 44%. ChatGPT-3.5, ChatGPT-4o, and Claude showed the lowest performance, each with a 33% match rate.ConclusionsDespite the variability in internal consistency and good intra- and inter-rater reliability, the AI Chatbots' recommendations often did not align with CPGs recommendations for diagnosing and treating lumbosacral radicular pain. Clinicians and patients should exercise caution when relying on these AI models, since one to two-thirds of the recommendations provided may be inappropriate or misleading according to specific chatbots.

IntroductionWith the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals.MethodsThe aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field.ResultsThrough network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date.DiscussionWe propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems.

Greta Thunberg est une jeune activiste suédoise qui lutte contre le réchauffement climatique et œuvre à la sensibilisation aux questions environnementales. Elle a lancé en 2018 la « grève scolaire pour le climat » pendant laquelle des collégiens et lycéens sont invités à ne pas assister aux cours pour participer à des manifestations dénonçant l'inaction des pouvoirs publics face aux dérèglements climatiques. Greta Thunberg est par ailleurs intervenue lors de conférences et sommets mondiaux pour le climat pendant lesquels elle s'est adressée aux chefs d'État de grandes puissances mondiales. Ce graphique ci-contre interroge ainsi la notoriété de cette jeune militante scandinave en France. On observe ainsi que trois quarts des Français déclaraient connaître, ne serait-ce que de nom, Greta Thunberg.

Campylobacter jejuni is a major bacterial cause of human diarrheal diseases worldwide. Despite its sensitivity to environmental stresses, C. jejuni ubiquitously distributes throughout poultry production chains. Biofilm formation mediated by quorum sensing is suggested to be critical to the survival of C. jejuni in agroecosystem. C. jejuni possesses LuxS, the enzyme involved in the production of autoinducer-2 (AI-2) signaling molecules. In this study, two fatty acids, namely decanoic acid and lauric acid, were identified to be effective in inhibiting AI-2 activity of C. jejuni. Both decanoic acid and lauric acid at 100 ppm inhibited ∼90% AI-2 activity (P < 0.05) of C. jejuni without bacterial inactivation. The biofilm biomass of two C. jejuni strains was reduced by 10–50% (P < 0.05) after treatment by both fatty acids, while increased biofilm formation was observed for one C. jejuni strain. In addition, both fatty acids effectively reduced the motility of all tested C. jejuni strains. These findings can aid in developing alternative C. jejuni control strategies in agri-food and clinical settings.

Fatturato per gli ultimi anni, elenco utili/perdita, costo dipendenti, soci esponenti e contatti per GRETA SRL in COLOGNOLA AI COLLI (VR)

Fatturato per gli ultimi anni, elenco utili/perdita, costo dipendenti, soci esponenti e contatti per AI 100 ACRI DI FABRELLO GRETA AZIENDA AGRICOLA in MARANO VICENTINO (VI)