Antonia Pirrera

Adolescents increasingly engage with digital technologies, yet understanding patterns of smartphone use and fostering reflective awareness remain challenging. Traditional assessments in clinical or school settings may limit participation and self-reflection. This study evaluated the feasibility and impact of a Computer-Assisted Web Interviewing (CAWI) approach to monitor smartphone use, provide immediate individualized feedback, and support peer-mediated dissemination in a public science engagement context. Across three editions of the European Researchers’ Night in Rome (2023–2025), 807 adolescents aged 10–19 completed the SAS-SV questionnaire via on-site tablets or personal devices using QR codes. Smartphone use was categorized into Low Involvement, At-Risk, or Problematic. Participants were encouraged to share the survey link with peers, enabling snowball-mediated recruitment. Participant acceptance was assessed through the Net Promoter Score (NPS). Snowball participation accounted for the majority of responses, highlighting the effectiveness of peer-mediated diffusion. SAS-SV categorization indicated 46% Low Involvement, 39% At-Risk, and 15% Problematic use, with minimal gender differences. NPS values ranged from +69 to +79, with snowball participants reporting slightly higher satisfaction than on-site attendees. These results underscore high engagement, perceived value, and the role of peer networks in promoting reflective digital behavior. Integrating CAWI assessment, immediate feedback, and peer-mediated diffusion created a socially situated environment supporting self-reflection and voluntary dissemination. Peer networks extended both the temporal and social reach of the initiative beyond the public event, demonstrating a scalable and non-stigmatizing model. CAWI-based monitoring combined with feedback and peer-driven diffusion is feasible and effective for adolescent digital wellbeing interventions. This approach fosters reflective digital citizenship, supports self-awareness, and leverages social networks to enhance the reach and impact of youth-centered health promotion initiatives.

Background: Assistive technologies (ATs) for individuals with tetraplegia have evolved from mechanical aids to complex neurotechnological, digital, and psychosocial systems. However, the evidence base remains fragmented, with heterogeneous methodologies and limited integration across domains. This review synthesizes recent review-level evidence to clarify current trends, gaps, and directions in ATs for tetraplegia. Methods: A narrative review of reviews was conducted following the ANDJ checklist. PubMed and Scopus were searched for systematic, scoping, and narrative reviews addressing assistive technologies relevant to tetraplegia. After screening, de-duplication, and quality appraisal, 20 reviews were included and synthesized narratively. Results: The included reviews clustered into four main domains: neural and regenerative interfaces, motor and biomechanical assistive systems, digital and adaptive rehabilitation ecosystems, and psychosocial and integrative frameworks. Across domains, evidence highlights a shift toward personalized, adaptive, and interoperable systems, supported by neurotechnologies, robotics, mobile health, and virtual reality. Common limitations include heterogeneous outcome measures, scarcity of longitudinal evidence, limited system interoperability, and persistent inequities in access and adoption. Emerging applications of artificial intelligence support adaptive control, monitoring, and personalization, though robust clinical validation remains limited. Conclusions: This synthesis provides a structured overview of review-level evidence on assistive technologies for tetraplegia. The findings underscore the need for coordinated, multidisciplinary approaches and more rigorous, longitudinal evaluation to support the development of inclusive, human-centered, and interoperable assistive ecosystems.

Population-level evidence on how different phases of assistive technology service delivery contribute to user satisfaction with assistive products remains limited, despite its importance for strengthening provision systems. This study investigates how different aspects of assistive technology service delivery influence user satisfaction with assistive products and their perceived usefulness. Drawing on data from 992 Italian respondents to the WHO rapid Assistive Technology Assessment (rATA) survey, hierarchical regression and mediation analyses were conducted to examine the relative contribution of pre- and post-delivery services (i.e., assessment and training, and maintenance and follow-up) to overall product satisfaction. Results showed that satisfaction with pre-delivery services (β = 0.571, p < 0.001) was the strongest predictor of product satisfaction, followed by post-delivery services (β = 0.280, p < 0.001). Together, both service dimensions explained nearly 60% of the variance in product satisfaction (R² = 0.595). Mediation analysis further revealed that satisfaction with pre-delivery services partially mediated the relationship between product satisfaction and perceived usefulness (β = 0.147, p < 0.001), accounting for 29% of the total effect. These findings suggest a complementary pattern, in which pre-delivery services may provide a foundation for positive user experiences, while post-delivery services contribute meaningfully to sustaining satisfaction and perceived usefulness. The results provide population-level insights that may support national reflections on how to strengthen assistive technology service delivery, while recognizing that both early- and later-stage service components play important and interdependent roles.

Background/Objectives: Pain, anxiety, and distress are common yet frequently insufficiently managed issues for patients undergoing radiology and radiotherapy procedures. Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), are emerging as innovative non-pharmacological approaches to alleviate such burdens through engaging interventions. This review, combining scoping and narrative methodologies, seeks to examine the current application, efficacy, and integration of these technologies to enhance patient care and wellbeing within diagnostic and oncological environments. Methods: Employing a mixed scoping and narrative review approach, this study conducted a systematic search of PubMed, EMBASE, Scopus, and Web of Science databases (no date restrictions—search included studies up to May 2025) to identify relevant studies utilizing VR, AR, MR, or XR for mitigating pain, anxiety, or distress in patients undergoing radiology or radiotherapy. Two independent reviewers selected eligible papers, with data extracted systematically. The narrative analysis supplemented the scoping review by providing contextual insights into clinical relevance and technological challenges. Results: The screening process identified 76 articles, of which 27 were assessed for eligibility and 14 met the inclusion criteria. Most studies focused on oncology and primarily employed VR as the immersive technology. VR has shown promising effects in reducing anxiety and pain—particularly during radiotherapy sessions and invasive procedures—and in supporting patient education through engaging, immersive experiences, making it a valuable approach meriting further investigation. Patient acceptance was notably high, especially among those with elevated distress levels. However, findings in radiology were less consistent, likely due to shorter procedure durations limiting the effectiveness of VR. The variability in outcomes highlights the importance of tailoring immersive interventions to specific procedures and patient needs. The narrative component identified key barriers, such as regulatory hurdles, standardization issues, and implementation challenges, that need addressing for broader clinical adoption. Conclusions: Immersive digital therapeutics are evolving from preliminary research tools toward more structured incorporation into clinical practice. Their future success relies on harmonizing technological advancements with patient-focused design and robust clinical evidence. Achieving this will require collaborative efforts among researchers, industry stakeholders, and healthcare providers. The integration of scoping and narrative review methods in this study offers a comprehensive perspective on the current landscape and informs strategic directions for advancing immersive technologies in radiology and radiotherapy.

Background: The integration of artificial intelligence (AI) into cervical cancer diagnostics has shown promising advancements in recent years. AI technologies, particularly in the analysis of cytological images, offer potential improvements in diagnostic accuracy and screening efficiency. However, challenges regarding model generalizability, explainability, and operational integration into clinical workflows persist, impeding widespread adoption. Aim: This narrative review aims to critically evaluate the current state of AI in cervical cancer diagnostic cytology, identifying trends, key developments, and areas requiring further research. It also explores the potential for AI to improve diagnostic processes, alongside examining international guidelines and consensus on its adoption. Methods: A narrative review was conducted through a comprehensive search of PubMed and Scopus databases. Thirty studies published between 2020 and 2025 were selected based on their relevance. Results: The literature review reveals a growing interest in the application of AI for cervical cancer diagnostics, particularly in the automated interpretation. However, large-scale clinical adoption remains limited. Most studies are experimental or application-based in controlled settings. Consensus efforts and specific recommendations for this domain are still limited and not specific. Key barriers include limited model generalizability, lack of explainability, challenges in integration into clinical workflows, and regulatory and infrastructural constraints. Conclusions: A sustainable and meaningful integration of AI in cervical cancer diagnostics requires a unified framework that addresses both technical challenges and operational needs, supported by context-specific strategies and broader consensus-building efforts.

As the interest in social and assistive support robots (SASRs) grows, a review of 17 systematic reviews was conducted to assess their use in healthcare, emotional well-being, and therapy for diverse populations, including older adults, children, and individuals with autism and dementia. SASRs have demonstrated potential in alleviating depression, loneliness, anxiety, and stress, while also improving sleep and cognitive function. Despite these promising outcomes, challenges remain in identifying the most effective interventions, refining robot designs, and evaluating long-term impacts. User acceptance hinges on trustworthiness and empathy-driven design. Compared to earlier review studies, recent research emphasizes the ongoing significance of emotional engagement, the refinement of robot functionalities, and the need to address ethical issues such as privacy and autonomy through robust cybersecurity and data privacy measures. The field is gradually shifting towards a user-centered design approach, focusing on robots as tools to augment, rather than replace, human care. While SASRs offer substantial benefits for emotional well-being and therapeutic support, further research is crucial to enhance their effectiveness and address concerns about replacing human care. Algorethics (AI ethics), interdisciplinary collaboration, and standardization and training emerge as key priorities to ensure the responsible and sustainable deployment of SASRs in healthcare settings, reinforcing the importance of rigorous methodologies and ethical safeguards.

The integration of artificial intelligence (AI) into assistive technologies is an emerging field with transformative potential, aimed at enhancing autonomy and quality of life for individuals with disabilities and aging populations. This overview of reviews, utilizing a standardized checklist and quality control procedures, examines recent advancements and future implications in this domain. The search for articles for the review was finalized by 15 December 2024. Nineteen review studies were selected through a systematic process identifying prevailing themes, opportunities, challenges, and recommendations regarding the integration of AI in assistive technologies. First, AI is increasingly central to improving mobility, healthcare diagnostics, and cognitive support, enabling personalized and adaptive solutions for users. The integration of AI into traditional assistive technologies, such as smart wheelchairs and exoskeletons, enhances their performance, creating more intuitive and responsive devices. Additionally, AI is improving the inclusion of children with autism spectrum disorders, promoting social interaction and cognitive development through innovative devices. The review also identifies significant opportunities and challenges. AI-powered assistive technologies offer enormous potential to increase independence, reduce reliance on external support, and improve communication for individuals with cognitive disorders. However, challenges such as personalization, digital literacy among the elderly, and privacy concerns in healthcare contexts need to be addressed. Notably, AI itself is expanding the concept of assistive technology, shifting from traditional tools to intelligent systems capable of learning and adapting to individual needs. This evolution represents a fundamental change in assistive technology, emphasizing dynamic, adaptive systems over static solutions. Finally, the study emphasizes the growing economic investment in this sector, forecasting significant market growth, with AI-driven assistive devices poised to transform the landscape. Despite challenges such as high development costs and regulatory hurdles, opportunities for innovation and affordability remain. This review underscores the importance of addressing challenges related to standardization, accessibility, and ethical considerations to ensure the successful integration of AI into assistive technologies, fostering greater inclusivity and improved quality of life for users globally.

The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early developments and potential future impact of these technologies in radiology. The current literature, comprising 15 systematic reviews, highlights potentialities, opportunities, areas needing improvements, and recommendations. This umbrella review offers a comprehensive overview of the current landscape of natural language processing (NLP) and natural language models (NLMs), including chatbots, in healthcare. These technologies show potential for improving clinical decision-making, patient engagement, and communication across various medical fields. However, significant challenges remain, particularly the lack of standardized protocols, which raises concerns about the reliability and consistency of these tools in different clinical contexts. Without uniform guidelines, variability in outcomes may hinder the broader adoption of NLP/NLM technologies by healthcare providers. Moreover, the limited research on how these technologies intersect with medical devices (MDs) is a notable gap in the literature. Future research must address these challenges to fully realize the potential of NLP/NLM applications in healthcare. Key future research directions include the development of standardized protocols to ensure the consistent and safe deployment of NLP/NLM tools, particularly in high-stake areas like radiology. Investigating the integration of these technologies with MD workflows will be crucial to enhance clinical decision-making and patient care. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, must also be explored to ensure responsible use in clinical settings. Longitudinal studies are needed to evaluate the long-term impact of these technologies on patient outcomes, while interdisciplinary collaboration between healthcare professionals, data scientists, and ethicists is essential for driving innovation in an ethically sound manner. Addressing these areas will advance the application of NLP/NLM technologies and improve patient care in this emerging field.

The rapid advancement of artificial intelligence (AI) technology has catalyzed unprecedented innovation in the healthcare industry, transforming medical practices and patient care. However, this progress brings significant ethical challenges, highlighting the need for a comprehensive exploration of algorethics—the intersection of algorithm design and ethical considerations. This study aimed to conduct a narrative review of reviews in the field of algorethics with specific key questions. The review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar. The review revealed a growing emphasis on integrating fairness, transparency, and accountability into AI systems, alongside significant progress in ethical AI development. The importance of collaboration between different domains of scientific production, such as social sciences and standardization (like the IEEE), and the development of guidelines is significantly emphasized, with demonstrated direct impact in the health domain. However, gaps persist, particularly in the lack of standardized evaluation methods and the challenges posed by complex sectors like healthcare. The findings underscore the need and importance for robust data governance to prevent biases and highlight the importance of cross-disciplinary collaboration in creating comprehensive ethical frameworks for AI. The field of algorethics has important applications in the health domain, and there is a significant increase in attention, with a focus on addressing issues and seeking both practical and theoretical solutions. Future research should prioritize establishing standardized evaluation practices for AI, fostering interdisciplinary collaboration, developing sector-specific ethical guidelines, exploring AI’s long-term societal impacts, and enhancing ethical training for developers. Continued attention to emerging ethical standards is also crucial for aligning AI technologies with evolving ethical principles.

This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR’s integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR’s transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.