Future of Robotics and Integration of Artificial Intelligence: Toward Computer-Assisted Surgery and the Real Democratization of Surgical Care.

The rapid integration of artificial intelligence, machine learning, and data science into daily life raises ethical concerns and stimulates discussions among stakeholders responsible for their development. Given the media's role in shaping imaginaries of emerging technologies and their acceptance, this paper systematically analyzes media discourse on ethics in artificial intelligence, machine learning, and data science from 2015 to 2020. Our results show that media coverage of advanced algorithmic technologies mainly focused on the industry sector, frequently addressing short-term challenges such as algorithmic bias, social justice, data privacy, and socioeconomic effects. Its portrayal in media often maintained a balanced perspective between positive and negative outcomes, paired with realistic and grounded future scenarios. This study offers a holistic and integrated analysis of how the media frames the ethics of artificial intelligence, data science, and machine learning, highlighting previously overlooked dimensions such as accountability strategies and the relationship between areas of application and their consequences.

Chimeric antigen receptor (CAR)-T cell immunotherapy shows significant success in hematologic malignancies. However, it faces critical challenges in solid tumours, such as suppressive tumour microenvironment (TME) and antigenic heterogeneity, highlighting the urgent need for effective and safe CAR products. The integration of artificial intelligence (AI) into CAR-T cell immunotherapy offers exceptional opportunities to improve its therapeutic efficacy. More specifically, this paper highlights the transformative role of AI in addressing key challenges that impede the success of CAR-T cell therapy in solid tumours, including assisting in CAR design and manufacturing process, identifying novel CAR-targeted genes, and detecting cell heterogeneity in solid tumours. We remain optimistic about AI-driven strategies for enhancing CAR T-cell persistence, trafficking, and visualisation in the TME. In addition, we highlight the current challenges and prospects for advancing AI-driven CAR-T cell therapies.

To review and summarize the current literature on recent advances in corneal topography and anterior segment tomography, highlighting emerging technological advances and the integration of artificial intelligence, and their clinical applications in screening, planning, and optimizing outcomes of modern refractive surgery. A comprehensive review of the recent literature was conducted using the PubMed, Scopus, and Web of Science databases to identify peer-reviewed articles published within the last 18 months that focus on advancements in corneal imaging technologies, including Placido-based topography, Scheimpflug imaging, and swept-source anterior segment optical coherence tomography (SS-ASOCT). Comparative studies, diagnostic accuracy reports, and new indices and software for ectasia risk assessment were analyzed. Recent innovations in corneal topography and tomography have greatly improved the ability to detect subclinical keratoconus, evaluate corneal biomechanics, and develop personalized surgical plans. The integration of 3D corneal mapping, epithelial thickness profiling, and artificial intelligence-based analysis has increased diagnostic accuracy and safety in refractive procedures. These developments enable earlier detection of at-risk corneas and more accurate prediction of postoperative stability. Advances in topography and tomography continue to improve the safety and predictability of refractive surgery. The use of multimodal imaging and AI-based diagnostics is transforming preoperative assessment and postoperative care, leading to more personalized and reliable visual outcomes.

Post-stroke dysphagia (PSD) affects approximately 42% of acute stroke patients, increasing hospitalization costs and length of stay. Early identification improves outcomes, yet many patients-especially in low-resource settings-lack access to gold-standard evaluations. This scoping review explores the integration of artificial intelligence (AI) and data science-defined as the interdisciplinary use of computational methods, statistical modeling, and machine learning to extract clinically meaningful patterns from biomedical data-in PSD screening and assessment. We synthesize evidence from bedside screening instruments, acoustic voice analyses, and emerging AI-driven models for dysphagia and aspiration risk stratification, critically appraising limitations related to small datasets, overfitting risk, and the need for external validation. Traditional tools like the water swallow test show high sensitivity but varying specificity; recent studies support augmenting these with voice-based biomarkers such as post-swallow wet voice, jitter, and shimmer. While wet voice as a standalone marker has limited sensitivity (8-29%), its high specificity (75-94%) within multimodal approaches justifies continued investigation. AI models trained on acoustic parameters have demonstrated strong performance in detecting penetration-aspiration events, while mobile and voice-based platforms may expand diagnostic reach, pending further validation. We also review optimal screening timing, emphasizing assessment within 24 h of stroke onset with repeated evaluations for high-risk patients. Future directions advocate multimodal, patient-centered approaches combining wearable biosensors, cloud-based analytics, and culturally adapted algorithms, while addressing implementation challenges including infrastructure requirements, digital literacy, workflow integration, and ethical considerations. The convergence of clinical expertise and computational technologies presents a promising path to equitable, scalable, and precise dysphagia care.

The rapid integration of generative artificial intelligence (GenAI) into undergraduate nursing education has prompted significant debate regarding its impact on the development of critical reasoning, inquiry skills, and clinical judgement. While some scholars argue that reliance on GenAI may undermine independent thinking, contextual decision‑making, and autonomous judgement, emerging perspectives suggest that GenAI has the potential to enhance rather than erode these foundational competencies. This commentary examines the evolving role of GenAI in nursing education and argues that its thoughtful integration can strengthen students' preparedness for increasingly complex, technology‑rich clinical environments. Clinical judgement is central to safe nursing practice and is shaped by the nurse's interpretation of patient needs, contextual factors, and professional reasoning. While GenAI can synthesize large amounts of information efficiently, it does not replace human judgement; instead, it provides data that students must interpret within ethical, relational, and contextual dimensions of care. Integrating GenAI into educational contexts allows students to engage with realistic, data‑driven scenarios that mirror contemporary practice environments, supporting deeper analytical thinking and the ability to critique algorithmic outputs and biases. At the same time, the use of GenAI raises epistemological tensions between nursing's humanistic ways of knowing and AI's computational logic. These tensions underscore concerns that tacit knowledge, ethical reasoning, and patient‑centered judgement may be marginalized if GenAI tools are used uncritically. Addressing this challenge requires adapting nursing theory and curriculum to incorporate digital epistemologies while maintaining the profession's ethical and relational foundations. This commentary concludes that rather than discouraging GenAI use, nursing education must embrace it deliberately and ethically. Through intentional curriculum design, faculty development, and emphasis on AI literacy, educators can ensure that nursing students emerge as competent, reflective practitioners capable of navigating GenAI‑enabled healthcare environments with confidence and integrity.

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.

Integration of artificial intelligence in lignocellulosic biomass valorization in biorefineries: Enabling energy efficiency through analysis of feedstocks and conversion pathways

Transport and distribution patterns of floating marine litter: Numerical modeling and AI-empowered solutions.

The role of artificial intelligence in virtual emergency care: a systematic review.

The rapid integration of artificial intelligence (AI) into research presents emerging ethical and governance challenges for institutions overseeing human research. While existing frameworks provide general protections, they offer limited guidance for addressing AI-specific risks related to informed consent, transparency, data privacy, and fairness. This commentary synthesizes key ethical concerns associated with AI-enabled research and examines some implications for institutional policy development and research ethics review. Rather than proposing a comprehensive evaluative framework, this invited commentary examines governance and procedural gaps that complicate implementation of existing AI oversight resources and emphasizes institution-level policy approaches to support consistent, responsible research ethics review. An illustrative example is provided to demonstrate how the absence of clear policy guidance can create ambiguity in research ethics review and researcher practice. The ethical considerations discussed apply broadly to research ethics review systems internationally, including Institutional Review Boards (IRBs), Research Ethics Committees (RECs), and Ethics Review Committees (ERCs).

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.

The ethical integration of generative artificial intelligence in Doctor of Nursing Practice project planning: A formative approach.

Quantitative scrutiny of biomass-derived battery electrodes: A strategic analysis.

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.

Cardiovascular imaging is integral to modern clinical trials of new pharmaceuticals or devices, enabling refined eligibility, mechanistic insight, and sensitive assessment of treatment response and safety. Potential heterogeneity in data acquisition, analysis, and reporting may affect reproducibility and interpretability across multicentre settings. Rigorous standardization and fit-for-purpose validation of imaging endpoints can improve statistical efficiency, reduce trial duration and cost, and strengthen generated evidence. This Scientific Statement outlines a reference framework for the implementation of cardiovascular imaging in clinical trials. We provide considerations on use of imaging parameters as eligibility criteria in clinical trials, for efficacy signals evaluation, and for assessment of safety. We define principles for clinical, analytical, and operational validation of imaging endpoints, and discuss concepts of minimal clinically important change and minimal detectable change. Additionally, we discuss feasibility considerations for use of cardiovascular imaging endpoints in multicentre clinical trials where differences in equipment and local experience may exist. We delineate standards for harmonization, centralized analysis, and quality control in clinical trials, as well as challenges and opportunities of the integration of artificial intelligence within core-lab workflows. Lastly, we identify gaps in knowledge, challenge of preclinical-clinical translatability and highlight training needs and innovation priorities.

Diagnostic Uncertainty & Improving Diagnostic Certainty in Radiology.

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.

Health Research Ethics Committees (HRECs) play a pivotal role in safeguarding research participants and ensuring ethical conduct. The rapid integration of artificial intelligence (AI) into health research introduces novel ethical and operational complexities, including algorithmic opacity, bias, data governance challenges, and difficulties in post-approval monitoring. However, empirical evidence on how these AI-specific complexities affect HREC operations in Tanzania remains limited. This study explored operational challenges associated with ethical oversight of AI-related health research among HRECs in Tanzania. An exploratory qualitative study design was employed, involving 25 participants (15 HREC members and 10 secretariat staff) purposively selected from 10 HRECs across five zones of mainland Tanzania. In-depth interviews were conducted using a semi-structured guide. Data were transcribed, translated, and analyzed using inductive content analysis with thematic interpretation, supported by NVivo software. An audit trail, reflexive journaling, and data source triangulation were used to enhance credibility and trustworthiness. A total of 28 codes were generated and organized into 10 subthemes and four overarching themes. While general challenges such as limited funding, high workload, and staffing constraints persisted, AI-related protocols introduced additional operational burdens, including difficulties in assessing algorithmic validity, increased reliance on external technical experts, and challenges in reviewing large-scale datasets. Although 50% of secretariat staff had more than five years of experience, participants emphasized that the key limitation was not general experience but insufficient AI-specific technical expertise. Weak post-approval monitoring systems were particularly inadequate for tracking AI-driven studies. Tanzanian HRECs demonstrate foundational governance capacity but face AI-specific operational and technical challenges that constrain effective oversight. Strengthening AI-focused training, technical advisory mechanisms, digital review systems, and sustainable financing is essential to support the ethical governance of emerging technologies.