Algorithmic reconfiguration of mental health care: risk, trust and vulnerability in Italian professionals’ accounts

This study evaluated levels and risk factors of burnout in a sample of mental health professionals employed in nonhospital psychiatric residential facilities of northern Italy. Nurses, nurse assistants, and educators completed a questionnaire evaluating demographic variables, burnout (Maslach Burnout Inventory), job characteristics (Job Diagnostic Survey), workload, relationships with colleagues, and support from supervising coordinators. A total of 202 (83% response rate) questionnaires were analyzed. Logistic linear regressions were used to estimate predictors of burnout dimensions. Burnout risk was widespread. Low feedback about job performance, poor support from coordinators, and young age predicted emotional exhaustion. Low feedback about job performance predicted feelings of depersonalization. Low task identity and young age predicted reduced feelings of personal accomplishment. Interventions to prevent burnout among employees should be developed. These include providing feedback about performance, clearly identifying the tasks of the job, and providing support.

Artificial intelligence (AI) is reshaping medical and health professions education; yet, adoption in anatomy remains uneven and often ad hoc. Anatomy's spatial and visualization demands make it a compelling domain for AI, but discipline-specific opportunities and risks are not well characterized in the United Arab Emirates. This study examines United Arab Emirates anatomy educators' AI use, attitudes, perceived barriers and enablers, and strategic perspectives on AI integration using a design informed by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A cross-sectional survey of anatomy faculty at United Arab Emirates medical and health sciences colleges used 5-point Likert items to assess educational technology proficiency, AI use patterns, AI attitudes, perceived barriers and facilitators, and professional development needs. Quantitative data were summarized descriptively and explored with nonparametric tests. Open-ended strengths, weaknesses, opportunities, and threats questions were analyzed using reflexive thematic analysis, organized within the strengths, weaknesses, opportunities, and threats framework, and interpreted through UTAUT2 constructs. Quantitative and qualitative strands were integrated at interpretation through triangulation. In total, 30 anatomy faculty participated. Self-rated educational technology proficiency was high (mean 3.73 out of 5, SD 1.01), and overall attitudes toward AI in anatomy education were positive (mean 4.23, SD 0.73), with strong interest in AI-focused professional development (mean 4.50, SD 0.73). Most respondents reported using generative AI tools, predominantly ChatGPT, for content creation, quiz and examination item generation, summarization of complex material, and, to a lesser extent, visualization and workflow streamlining. Capacity-related barriers predominated: time and workload pressures (mean 3.27, SD 1.17) and training gaps (mean 3.13, SD 1.22) were rated as moderate obstacles, whereas budget or resource limitations (mean 2.63, SD 1.19) and academic integrity concerns (mean 2.80, SD 1.10) were minor obstacles. Student interest (mean 4.23, SD 0.86) and institutional encouragement (mean 4.00, SD 1.14) emerged as strong facilitators, with no statistically detectable differences by academic rank, age, or years of experience in this small, underpowered sample. Qualitatively, themes highlighted strong institutional support and digital readiness as strengths; training needs, workload, and policy gaps as weaknesses; visualization, personalization, and efficiency as opportunities; and overreliance, ethical risks, and erosion of hands-on anatomy pedagogy as threats. UTAUT2 interpretation indicated high performance expectancy and social influence (student and institutional support) but reduced effort expectancy and facilitating conditions due to time, training, and governance constraints, collectively tempering behavioral intention. In this exploratory sample, United Arab Emirates anatomy educators were broadly receptive to generative AI and already experimenting and valuing the benefits for 3D visualization, adaptive practice, and feedback. However, workload, limited training, and unclear governance (disclosure, assessment integrity, and cadaveric or patient images) constrain uptake, underscoring the need for protected time, workflow-aligned training, and discipline-specific policies to enable sustainable, ethical integration.

Although various surveys on job satisfaction have been performed in mental health care settings, no studies have investigated in-depth the level of satisfaction with the various aspects of work in Italian mental health services. In the present study, all clinical mental health staff working in a large psychiatric catchment area in Rome were invited to anonymously complete a previously validated questionnaire designed to measure job satisfaction among mental health professionals. Of the total 236 health professionals, 196 (83%) agreed to participate. Most participants were not completely satisfied with many aspects of their job, and many were not even moderately satisfied. The level of satisfaction increased with age, and it was significantly lower among hospital-ward staff compared to the staff of outpatient clinics or residential facilities, even after adjusting for age, gender, profession, work setting, and time in current job, using a multiple logistic regression model. Our findings suggest that interventions aimed at increasing job satisfaction among Italian mental health professionals might be warranted, particularly among hospital-ward staff.

Health care is undergoing a profound transformation through the integration of artificial intelligence (AI). However, the rapid integration and expansive growth of AI within health care systems present ethical and legal challenges that warrant careful consideration. In this viewpoint, the author argues that the health care domain, due to its complexity, requires specialized approaches to regulating AI. Precise regulation can provide clear guidelines for addressing these challenges, thereby ensuring ethical and legal AI implementations.

The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non-peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher's Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this challenge is exacerbated by the lack of consensus among experts from different disciplines on the definition of trust in AI within the health care sector. We aimed to provide the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains. Our findings can be used to address the problem of defining trust in AI in health care applications, fostering the discussion of concrete real-world health care scenarios in which humans interact with AI systems explicitly. We used a combination of framework analysis and a 3-step consensus process involving 18 international experts from the fields of computer science, medicine, philosophy of technology, ethics, and social sciences. Our process consisted of a synchronous phase during an expert workshop where we discussed the notion of trust in AI in health care applications, defined an initial framework of important elements of trust to guide our analysis, and agreed on 5 case studies. This was followed by a 2-step iterative, asynchronous process in which the authors further developed, discussed, and refined notions of trust with respect to these specific cases. Our consensus process identified key contextual factors of trust, namely, an AI system's environment, the actors involved, and framing factors, and analyzed causes and effects of trust in AI in health care. Our findings revealed that certain factors were applicable across all discussed cases yet also pointed to the need for a fine-grained, multidisciplinary analysis bridging human-centered and technology-centered approaches. While regulatory boundaries and technological design features are critical to successful AI implementation in health care, ultimately, communication and positive lived experiences with AI systems will be at the forefront of user trust. Our expert consensus allowed us to formulate concrete recommendations for future research on trust in AI in health care applications. This paper advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care. By synthesizing insights into commonalities and differences among specific case studies, this paper establishes a foundational basis for future debates and discussions on trusting AI in health care.

Purpose Understanding professionals’ early perceptions of artificial intelligence (AI) is particularly critical in high-stakes contexts, where decisions carry ethical and legal consequences. Such perceptions can help clarify acceptable-use boundaries, identify conditions for trustworthy AI integration, and inform human-centered system design. Most prior research examines attitudes after organizational implementation, leaving professionals’ perceptions in such settings underexplored. This study addresses this gap by exploring child welfare professionals’ perceptions of AI in a context where individual-level use exists without formal institutional systems. Materials and Methods Semi-structured interviews were conducted with 23 child welfare professionals in Turkey. Data were analyzed using reflexive thematic analysis in MAXQDA, within an abductive analytic approach informed by the Unified Theory of Acceptance and Use of Technology. Results Participants viewed AI as useful for administrative and problem-solving tasks but expressed concerns about cognitive dependence and emotional unease. They drew firm boundaries around AI’s role in decision-making, emphasizing that ethical reasoning and context-sensitive judgment must remain human-led. Participants also identified organizational implications of AI adoption, including the need for institutional infrastructure and professional training. Discussion These findings suggest that AI acceptance in high-stakes human services is shaped not only by perceived usefulness but also by ethical concerns, professional values, and the perceived necessity of preserving human judgment. This underscores the importance of context-sensitive approaches to AI integration in child welfare. Conclusion Responsible AI integration in child welfare requires sustained collaboration among practitioners, organizations, and policymakers. The study offers practical implications for frontline practitioners, organizational administrators, and national policy development.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.

BackgroundThe COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles.ObjectiveThis study aims to determine the impact of AI on the management and practices of Qatar’s Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians’ job roles, including associated risks and ethical ramifications from their perspective.MethodsWe conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses.ResultsOf the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians.ConclusionsThis study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians’ roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process.

ObjectiveThis study aimed to develop the Therapist Self‐Efficacy Scale (T‐SES), and test its validity in a sample of Italian mental health therapists, to assess their professional self‐efficacy concerning their practice of eTherapy in a synchronous video‐based setting.MethodsA sample of 322 Italian mental health professionals (37.6% psychologists, 62.4% psychotherapists; Mage = 38.48, SD = 8.509) completed an online survey.ResultsThe T‐SES showed a clear, one‐factor structure with good psychometric properties. Significant associations were found with insight orientation, general self‐efficacy, self‐esteem, and personality traits of openness, conscientiousness, and agreeableness. The results showed no differences between psychologists and psychotherapists, or differences based on years of experience.ConclusionThe T‐SES is an agile and versatile self‐report measure for mental health professionals to assess their self‐efficacy concerning their therapeutic activity, which can provide information for tailoring training for eTherapy.

Purpose This study examines how secondary school administrators can lead ethical artificial intelligence (AI) integration within environments demanding technological innovation and educational value preservation. Design/methodology/approach The study conducted a scoping review of literature (2018–2025) to analyze administrative functions across four established leadership dimensions: instructional, managerial, strategic, and relational. Sources were obtained from academic databases and grey literature, with 21 sources selected based on relevance to secondary education and administrative practice. Analysis is grounded in foundational leadership scholarship while examining contemporary AI integration challenges. Findings The analysis reveals a misalignment between AI's most frequent use (relational leadership functions) and where it may be most appropriately suited (managerial and strategic functions). AI integration creates distinct opportunities and risks across each leadership dimension, with equity concerns emerging consistently. Communication represents the primary AI use, despite being the most fundamentally human aspect of educational leadership. Cognitive offloading risks emerge when administrators delegate critical thinking tasks to AI systems, potentially attenuating leadership capabilities essential for educational effectiveness. Research limitations/implications This study relies on secondary data collection and English-language sources, creating Western-centric bias and limiting generalizability beyond North American contexts. The corpus of 21 sources reflects the nascent research state in this emerging field. The rapid evolution of AI capabilities means current findings may prove transitional as technology advances. Future empirical research should examine long-term cognitive effects of AI reliance on administrators, stakeholder trust implications when AI-mediated communications are detected, differential equity impacts across diverse school communities, cross-cultural implementation patterns, and effectiveness of hybrid governance approaches for AI integration in educational leadership. Practical implications Findings support implementing hybrid governance models that combine regulatory oversight with participatory decision-making between administrators and stakeholders. Professional development programs must balance AI literacy training with preserving human capabilities essential for authentic educational leadership. Administrator preparation programs require redesign to address cognitive offloading risks while maintaining relationship-building and cultural competence development. Educational leaders should prioritize AI applications in managerial and strategic functions while preserving human judgment in relational leadership contexts. Policy frameworks must address equity concerns and provide guidance for schools serving vulnerable populations who currently receive less AI implementation support. Social implications AI implementation without critical examination risks amplifying existing educational inequities, particularly affecting Indigenous, newcomer, and racialized communities. Democratic participation in AI boundary-setting becomes essential for maintaining institutional trust and stakeholder engagement. The misalignment between AI deployment and appropriate applications threatens the relational foundations of effective educational leadership. Originality/value The study provides the first systematic examination of AI integration across established educational leadership dimensions in secondary school contexts, addressing a critical research gap given that nearly 60% of K-12 principals use AI tools while fewer than 10% of schools have established AI policies.

BackgroundPatient-centered care remains a foundational principle of modern health care. The digital transformation of health systems has accelerated the adoption of artificial intelligence (AI) across diagnostic, predictive, and communicative functions, with implications for efficiency and clinical workflows. At the same time, AI integration raises concerns regarding transparency, equity, accountability, and trust, positioning it as a potential driver of both rehumanizing and dehumanizing dynamics in health care practice.ObjectiveThis study examines how the adoption of AI in health care may influence patient-centered care, exploring its potential to promote rehumanization or contribute to dehumanization. The objective is to identify the factors that shape these outcomes at the macrolevel (policy and infrastructure), mesolevel (institutional practices), and microlevel (individual behaviors and interactions).MethodsThis study adopts an exploratory qualitative design informed by grounded theory principles, drawing on 20 semistructured interviews with health care leaders, clinicians, researchers, legal experts, and industry consultants who have substantial professional experience across European health care systems, with some participants also contributing experience from the US health care context. To enhance analytical rigor and transparency, the study applied the Gioia methodology, enabling inductive coding from first-order concepts to second-order themes and aggregate dimensions. This multistakeholder approach facilitated a nuanced examination of how AI integration is perceived and experienced across macro-, meso-, and microlevels of health care.ResultsThe analysis identified key system-level factors shaping rehumanizing or dehumanizing outcomes of AI integration. At the macrolevel, 8 factors—including regulatory frameworks, policy priorities, and infrastructure—were identified as influencing whether efficiency pressures outweigh patient-centered values. At the mesolevel, 5 factors related to institutional strategies, workflows, and leadership shape how AI tools are embedded into care delivery. At the microlevel, 7 factors related to individual behaviors, trust, and doctor-patient interaction dynamics influence whether AI supports empathy and engagement or diminishes them. Rehumanizing potentials include reduced administrative burden, improved care pathways, clearer health communication, and enhanced decision-making, while risks include shorter consultations, reduced empathy, overreliance on automation, and erosion of professional identity. Without deliberate alignment with patient-centered principles, efficiency gains risk undermining the human dimensions of care.ConclusionsThis study represents one of the first empirical examinations of how AI shapes health care practices through rehumanizing and dehumanizing dynamics. The findings demonstrate that outcomes depend not only on technical capabilities but also on regulatory frameworks, institutional strategies, and cultural adaptation. By systematically mapping influencing factors across macro-, meso-, and microlevels, the research provides actionable insights for decision-makers to ensure that efficiency gains remain aligned with patient-centered principles. Realizing AI’s promise requires coordinated action to preserve empathy, trust, and interpersonal connection, ensuring that innovation strengthens rather than weakens the human dimensions of care.