ABSTRACT In this paper, we demonstrate how a new form of ethics work emerges in the area where social work and artificial intelligence (AI) technologies converge. The paper reports on an organisational ethnography of a Scandinavian NGO, specifically comprising the efforts of social workers and data engineers to establish a fair AI Counselling Assistant (AICA) for supporting volunteer staff in their online communications with children seeking help and support. The purpose of the AICA is to retrieve relevant information and advice for the volunteer social workers’ conversations with children written in real time. Drawing on science and technology studies, we analyse ethics work related to the AICA as a more-than-human endeavour. We highlight four ethical dimensions related to (1) distance, (2) agency, (3) time and (4) errors, which characterise what we term socio-technical work which continuously questions and addresses the ethicality of the AICA. We conclude that ethics work in the area where social work and AI converge requires social workers to possess a technological awareness that enables them to engage with both data ethics and the situated ethics of social work.

Current status and determinants of artificial intelligence-related anxiety among healthcare professionals: A cross-sectional survey study.

Artificial intelligence (AI) increasingly supports medical diagnosis, interventions, and clinical decision-making. In various domains of human communication, AI systems based on large language models offer effective tools for conducting interviews, supportive dialogue, and engaging in problem-focused discussions with healthy individuals and clinical patients. In contemporary clinical settings, interactions between psychiatrists and patients are increasingly complemented by AI; however, the position of this new agent within the therapeutic triad largely depends on patients' attitudes towards AI technologies. The study aimed at two primary objectives: (1) to examine how clinically relevant adaptive and maladaptive personality traits are associated with positive and negative attitudes toward AI, and (2) to evaluate these attitudes among patients diagnosed with anxiety, depression, bipolar disorder, and schizophrenia. This multicenter study used the General Attitudes Toward Artificial Intelligence Scale to examine how social adaptation and maladaptation are associated with a broad set of standardized measures, including the DSM-5 Personality Inventory, Schizotypal Traits Questionnaire, Anxiety Sensitivity Index, and Self-Concept Clarity Scale, in psychiatric patients and healthy participants. Patients with schizophrenia and bipolar disorder showed a more positive attitude toward AI use than those with depression and anxiety disorders. A negative attitude towards AI was associated with affective, cognitive, and behavioral vulnerability. This included elevated levels of negative affectivity, detachment, disinhibition, psychoticism, and anxiousness, as well as higher scores on schizotypal traits and reduced self-coherence. Patients' symptomatology and diagnostic profiles significantly shape their attitudes toward artificial intelligence, influencing their acceptance or rejection of AI-assisted interventions.

Evolution of artificial intelligence at Hospital Italiano de Buenos Aires: A retrospective review of experience and lessons learned.

Artificial intelligence (A.I.) technologies are increasingly deployed across clinical care, yet reimbursement remains a major barrier to sustainable adoption. The Centers for Medicare & Medicaid Services (CMS) currently reimburses A.I.-enabled technologies through a fragmented set of procedural codes, add-on payments, and legacy payment models, none of which were designed to support the complexity or workflow integration of clinical A.I. This article examines how existing CMS reimbursement pathways for A.I. function in practice, identifies structural misalignments that limit adoption, and highlights the risks of continuing to rely on these approaches. We then propose policy-level solutions to modernize A.I. reimbursement, including clearer coverage pathways, value-aligned payment models, and mechanisms to promote equitable adoption across diverse healthcare settings. Aligning reimbursement with clinical value is essential to ensure that A.I. improves care delivery and can be sustainably integrated into routine clinical practice.

Against the dual background of digital transformation and the development of New Medicine, traditional paper-based experiment reports increasingly show inherent limitations in efficiency, interactivity, archiving, and resource sharing. This study designed and developed an online electronic experiment report submission system tailored for medical universities, and further explored the application of artificial intelligence (AI) technology in automated grading and plagiarism detection. The system was built on the ThinkPHP 5.0 framework and MySQL database, with three role-based portals for administrators, teachers, and students. To meet the specific requirements of medical experiment teaching, core functions including copy-paste restriction, modular grading, a reusable comment library, one-click score aggregation, and batch PDF export were implemented. Furthermore, this study proposed a multi-algorithm similarity-based plagiarism detection scheme and an AI auto-grading concept based on a dual-layer evaluation model, providing a feasible path for the intelligent upgrade of experiment teaching. The application of this system is expected to effectively alleviate problems such as heavy grading workload, delayed feedback, and physical archiving difficulties, thereby improving the management efficiency and teaching quality of medical experiment education.

Traditional computer vision-based quality perception lacks depth information, limiting its application to reliable food quality management in the post-harvest supply chain. Three-dimensional (3D) reconstruction technology captures detailed surface geometry and internal structural information for reliable non-destructive quality inspection. Combined with emerging artificial intelligence (AI) technologies, 3D data-driven adaptive management brings potential for next-generation post-harvest quality management. This review analyzed 90 major related studies during 2015–2025, covering high-throughput 3D in-line inspection, high-resolution tomography, portable 3D sensing, and AI-driven 3D reconstruction technologies. Post-harvest supply chain application scenarios are mainly distributed in post-harvest processing (33 studies), manufacturing (29 studies), distribution (15 studies), and consumption (13 studies). Among them, fruit and vegetable products are the most intensively researched, highlighting the suitability and potential benefits of 3D reconstruction for these types of products. Besides, multiple 3D reconstruction technologies have been validated for postharvest evaluation, with X-ray CT dominating postharvest processing, manufacturing, and distribution, and portable RGB imaging devices dominating application in consumption. Besides, relevant 3D reconstruction analysis is evolving from geometry-driven to AI-enhanced analysis and management, highlighting the growing role of 3D reconstruction technology in intelligent, traceable, and sustainable post-harvest supply chain quality management. In the future, by integrating digital twins, IoT, and blockchain technologies, it is expected to build a transparent, tamper-proof quality traceability and control system across the global food supply chain. • 3D reconstruction significantly improves non-destructive food quality inspection. • AI integration expands 3D reconstruction applications in food supply chains. • Applications span post-harvest, processing, logistics, and consumer evaluation. • Enables automation in grading, manufacturing, cold chain, and dietary assessment. • Future trends include sensor fusion, XR, and digital twin for food quality control.

Artificial intelligence-driven protease cleavage site prediction: Advances and challenges.

Utopian or dystopian? A mixed-methods study of nursing and midwifery students' perceptions of artificial intelligence and robot-assisted person-centred care in education.

With the digital cultures that youth are exposed to and participating in come potential risks and protective factors for their mental health. However, despite clear need there is a lack of guidance to help mental health professionals evaluate the role of social media, artificial intelligence, and other technologies in young people's mental health. To co-design with young people the Digital Culture Interview, an interview tool to support the clinical assessment of digital cultural factors in mental health care. We recruited a diverse group of 12 participants aged 16-35 years (mean age 22 years) from outpatient mental health clinics in Montreal, Canada. Using the nominal group technique, they identified topics they found most relevant for exploring in a clinical assessment the experiences and practices involving digital technologies. Based on the topics that received the most votes from participants, we co-developed a list of interview questions and written guidance for their administration. Participants identified and ranked 48 themes. Drawing from these, 14 questions were developed for inclusion in the Digital Culture Interview, covering four topics: identity and worldview, negative experiences online, coping, and understanding of mental health. Participants emphasised that exploring digital culture in mental health care requires patients' trust and a baseline of knowledge. If done sensitively, this may enhance the patient-clinician alliance and improve mutual understanding. The Digital Culture Interview has the potential to enhance rapport and reveal risk and protective factors that are salient to and actionable in mental health care.

Multiple pathways to generative AI entrepreneurial behavior: A mixed-methods study of Chinese undergraduates

Dear Editor, We read with great interest the article by Haritha et al.,[1] titled “Platelet rich plasma resultant cutaneous nodules on scalp”. In this case, the patient was diagnosed with platelet rich plasma (PRP) induced benign non-Langerhans cell histiocytosis (LCH) of the skin, a condition described for the first time in the literature. To date, no prior reports have documented the onset of non-LCH following any medical procedure. Instead, this presentation could be interpreted as a granulomatous tissue response triggered by trauma, specifically, the injection of PRP into the scalp. Trauma has been identified in the literature as a known trigger for histiocytic infiltration.[2,3] Although PRP lacks foreign bodies, it contains various growth factors including vascular endothelial growth factor, which acts as an activating and chemotactic factor for monocytes and may contribute to the development of cutaneous granulomas.[4] Furthermore, the condition was classified as consistent with generalized eruptive histiocytoma (GEH), a subtype of non-LCH. GEH typically manifests as multiple, asymptomatic, firm, erythematous-to-brownish papules that appear in crops and are symmetrically distributed, without a tendency to cluster at a specific site.[5] There are no previous reports in the literature that have mentioned the onset of GEH post-procedure. Histopathologically, GEH is characterized by a monomorphic histiocytic infiltrate in the upper to mid-dermis, lacking foamy and giant cells. However, in this case, histopathological findings revealed the presence of Langhans giant cells, which contradicts the diagnosis of GEH based on both clinical and histopathological features. There are many granulomatous conditions such as foreign body reactions, infectious and non-infectious granulomas, that show positivity for CD68, but are negative for CD-1a (Langerin). Hence, it will be inappropriate to label these conditions as cases of non-Langerhan cell histiocytosis.[2] Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. Use of artificial intelligence (AI) The preparation of this manuscript was carried out entirely by the authors without the use of artificial intelligence technologies.

Artificial intelligence technology application and corporate green productivity: A study on financial transmission mechanisms

ObjectiveThis review systematizes knowledge about the use of artificial intelligence (AI) in neurobiological research of mental disorders and assesses its potential in identifying their causes.MethodsA qualitative synthesis of scientific literature from the Scopus and Web of Science databases for 2020-2024 was conducted. A total of 50 sources were identified, including papers describing the use of AI in the analysis of neuroimaging, biomarkers, cognitive impairment, and genetics data. A thematic encoding was used to analyze methods, accuracy, and limitations.ResultsMachine learning algorithms have accelerated the processing of large amounts of data, including magnetic resonance imaging, electroencephalogram, and genomic profiles, which has revealed new biomarkers and neural patterns associated with depression and schizophrenia. However, AI technologies face several limitations: low specificity, high computational complexity, and problems with reproducibility of results.ConclusionsThe integration of AI with neuroscience has significantly advanced the understanding of the etiology of mental disorders, revealing the complex relationships between genetic, neural, and behavioral factors. The practical significance of the research lies in the potential of AI to create personalized approaches to the diagnosis and treatment of mental disorders. This can improve the quality of life of patients and reduce the burden on healthcare systems.

Withholding and withdrawing life-sustaining therapy (LST) is common in European ICUs but significant variations exist. Behaviour artificial intelligence technology (BAIT) may help standardize the ethical dilemma to continue or withdraw LST for patients already admitted to the ICU. Several sessions with intensivists of an academic medical centre and a large urban teaching hospital were held to determine the criteria influencing the process. A discrete choice experiment was conducted during which 25 hypothetical cases were presented to the participants. For each case the participants had to decide whether they would continue, continue with a time limited trial of one week, or withdraw LST. The results of the experiment were used to develop a multinomial logistic regression model that was incorporated in a web-based decision-support system. Thirty-six participants (intensivists and fellows in intensive care medicine) completed the experiment. The estimated model consisted of twelve covariates and showed good model fit (McFadden's ρ2 0.25). The most important covariates were age, patient values, expected cardiovascular and pulmonary impairment after ICU discharge and frailty at admission. The BAIT system lets intensivists view expected decisions based on documented criteria and uses color-coding to show the magnitude of the effect and its direction (i.e. to continue or withdraw LST). We developed a BAIT system that may support clinicians facing the dilemma of continuing or withdrawing LST by elucidating the key criteria involved in assessing medical futility.

Healthcare systems worldwide face unprecedented challenges, including escalating costs, workforce shortages, and access disparities, which threaten their sustainability. The WHO projects an 18 million healthcare worker deficit by 2030, while financial and geographical barriers prevent millions from receiving necessary care. The integration of Artificial Intelligence (AI) into healthcare delivery systems presents opportunities to transform medical service provision, accessibility, and experiences, potentially democratizing healthcare access. This perspective analysis employs a theoretical framework combining Levesque etal.'s patient-centered healthcare access model with AI democratization frameworks. The analysis synthesizes current evidence on AI healthcare applications and proposes an implementation framework encompassing four dimensions: accessibility, affordability, usability, and ethical regulation. The framework addresses stakeholder roles and governance mechanisms aligned with international standards including the EU's AI Act and WHO's AI ethics guidance. Evidence demonstrates significant democratization potential through the implementation of AI. AI-powered platforms eliminate geographical barriers, reduce diagnostic timeframes, optimize resources, and enhance preventive care. Implementation challenges include algorithmic bias, data privacy concerns, digital divide risks, and regulatory fragmentation. AI integration holds transformative potential for democratizing healthcare across demographic and socioeconomic boundaries. Successful implementation requires structured, ethically grounded approaches that prioritize accessibility, affordability, usability, and regulation while maintaining a human-centered care approach. The framework offers actionable guidance for healthcare professionals and policymakers on deploying AI technologies to reduce disparities. Continuous research, interdisciplinary collaboration, and robust governance are crucial to ensuring that AI advances healthcare equity while preserving patient autonomy and clinical judgment. Patient and Public Involvement and Engagement was not appropriate for this theoretical framework and perspective analysis, as it represents a conceptual synthesis of existing literature and policy frameworks rather than primary research involving human participants. This manuscript establishes a theoretical foundation and an implementation framework for AI-driven healthcare democratization, grounded in published evidence and established models of healthcare access. The work focuses on guiding healthcare policymakers and planning professionals rather than collecting new data from patients or the public. However, the framework explicitly emphasizes the critical importance of patient advocacy organizations and community representation in AI development processes, recognizing that meaningful patient involvement will be essential during the actual implementation phases of AI healthcare technologies described in this theoretical foundation.

As artificial intelligence (AI) technologies become increasingly integrated into healthcare systems, understanding their impact on healthcare professionals is essential. This study provides a comprehensive examination of dietitians' perspectives, experiences, and future projections regarding AI in Türkiye. The research elucidates how artificial intelligence may affect professional identity, patient-dietitian interactions, and ethical boundaries, drawing on direct insights from practitioners. This qualitative research was conducted with 16 dietitians working in different institutions (hospitals, universities, and counselling centres) in Türkiye, selected through purposive sampling. Data were collected via a semi-structured interview (face-to-face/online) and analysed using inductive thematic analysis. The analysis indicates that dietitians' perspectives on artificial intelligence are influenced by both perceived opportunities and risks. Participants described artificial intelligence as a valuable assistant that enhances information retrieval and efficiency yet expressed concerns regarding misinformation and the erosion of professional boundaries. A prevailing view among participants is that artificial intelligence can perform technical calculations but cannot replicate essential human attributes in dietetics, such as empathy, emotional connection, and personalized care. Furthermore, participants highlighted that information pollution generated by artificial intelligence may undermine client trust, with data privacy identified as the most significant ethical concern. Dietitians maintain a cautious yet optimistic perspective regarding artificial intelligence technologies. Participants generally do not view technology as a competitor that will replace dietitians, but rather as a complementary tool that supports guidance and coaching beyond information provision. To maximize the benefits of artificial intelligence and mitigate associated risks, revising educational curricula to include digital literacy and promptly establishing professional ethical standards are essential.

With the rapid development of Internet of Things and artificial intelligence technologies, flexible wearable sensors have shown great potential in human-machine interaction and health monitoring fields. However, traditional hydrogel sensors face challenges such as water loss, freezing, and the use of toxic initiators during the preparation process, which lead to biological safety issues. To address these challenges, this paper proposes a green, initiator-free polymerization strategy based on the deep eutectic solvent system composed of choline chloride (ChCl) and D-sorbitol. By utilizing the property that the nitrogen-containing quaternary ammonium group in the ChCl molecule can generate free radicals upon ultraviolet irradiation, this study achieves the rapid polymerization of acrylamide in an initiator-free manner. The prepared eutectogel exhibits high transparency (≈96%), skin-fitting elastic modulus, good antifreezing performance, suitable breathability, and broad-spectrum adhesion. The flexible strain sensor constructed based on the eutectogel has high sensitivity, wide detection range, and good fatigue resistance. Combined with machine learning algorithms, this sensor system achieves a high accuracy (98.5%) of recognizing Curwen gestures. This research provides an innovative approach for developing safe, reliable, and environmentally adaptable intelligent wearable devices and has broad application prospects in intelligent music education and modern human-machine interaction.

Artificial Intelligence technologies like ChatGPT have potential benefits and challenges in educational settings, particularly in enhancing dental postgraduate education in India. At the same time, it presents potential implications for increased dependency and academic detachment in the population in question. This study aims to explore the use, effectiveness and concerns related to ChatGPT among dental postgraduate students. This cross-sectional study employed a convergent mixed-methodology design, starting with a comprehensive literature review followed by questionnaire development reviewed by an expert panel. The questionnaire was pilot-tested and subjected to an expert review. Subsequent data collection through web-based surveys and focus group discussions assessed ChatGPT's utilisation and its impact on learning and ethical concerns. A total of 202 dental postgraduate students participated in the quantitative aspect and three focus group discussions were conducted (26 students) with varied utilisation of ChatGPT, with significant use for academic writing and research. 95.02% use the free version of ChatGPT and only 21.39% received training. Satisfaction with its accuracy shows 46.53% neutral, 22.28% satisfied and 10.89% very satisfied. Six themes were identified after FGDs. Cluster analysis identified four distinct groups, showing variations in ethical concerns and AI tool confidence based on training. High reliance on ChatGPT was noted even among those unfamiliar with its advanced features. ChatGPT offers substantial benefits for academic efficiency in dental education but requires careful integration to address ethical concerns and prevent over-reliance. Future educational strategies should focus on developing tailored AI tools and comprehensive training programs to maximise benefits while safeguarding academic integrity.

The rapid integration of artificial intelligence (AI) technologies in healthcare, ranging from diagnostic tools to clinical decision support systems, is transforming medical practice and education. However, without deliberate integration of ethics, there is a risk that medical education will reproduce a technosolutionist orientation by privileging efficiency and data-driven outputs over patient autonomy, justice and professional integrity. While AI-related courses are increasingly being introduced into medical curricula, ethical considerations often remain peripheral, with most frameworks emphasising technical skills over moral reasoning. As future clinicians will face complex ethical challenges related to autonomy, safety, bias, transparency and accountability in AI-integrated clinical settings, there is an urgent need to evaluate how ethics is incorporated into AI education. With AI curricula still in their formative stages, this moment presents a critical opportunity to proactively design ethical components, rather than introducing them after harms have emerged. This scoping review aims to systematically map the ethical-technical balance in AI-related medical education curricula, identifying current practices, gaps and opportunities for curriculum development. This scoping review will follow the Joanna Briggs Institute methodology and be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. The review will address how ethical considerations are integrated into AI-related curricula in medical education and examine the balance between ethical and technical content. A comprehensive search strategy will be employed across multiple databases, including MEDLINE, Web of Science, Google Scholar, EBSCO, the Virtual Health Library, the Bioethics Literature Database and PhilPapers, as well as grey literature sources such as institutional reports, curricula and policy documents. Publications from January 2020 to December 2025 will be included. Data will be charted and analysed using descriptive qualitative content analysis, followed by a theory-informed interpretive analysis drawing on the hidden curriculum theory of medical education. This review does not require ethics approval, as it involves analysis of publicly available data. Findings will be disseminated through a peer-reviewed publication and presented at relevant conferences and workshops focused on medical education or bioethics.