At the beginning of the XXI century, as never before, the topic of artificial intelligence has been in the focus of scientific interest. This is due to the galloping pace of information technology and digitalization that the judiciary and law must inevitably follow. Artificial intelligence is a new challenge for court proceedings. This is because using artificial intelligence tools in court proceedings is expected to improve access to justice, increase the speed of court proceedings, and reduce the costs of conducting them. The subject of this paper’s analysis is the various applications of artificial intelligence through which the court procedure can potentially be automated. These are the following applications: an application for the search for the best evidence, an application for the summary analysis of previously made court decisions, an application for the electronic management of court proceedings, a lawyer - robot application, a judge - robot application, an application for the simultaneous resolution of several repetitive actions (lawsuits), an application for simulating court decisions with algorithms, an application for tracking a person while serving a sentence (compass system), an application for examining cases of domestic violence, sexual abuse, and others. By analyzing the applications of artificial intelligence in court proceedings, the authors of this paper want to point out the advantages and weaknesses in the use of artificial intelligence in them. Undeniably, the benefits of artificial intelligence can be used to streamline and reduce the costs of court proceedings without violating any basic human rights or information security breach.

As artificial intelligence tools become increasingly integrated into emergency department workflows, healthcare providers face a growing risk of legal liability stemming from improper use, particularly with respect to data privacy and Health Insurance Portability and Accountability Act (HIPAA) compliance. This article explores a realistic clinical scenario in which an emergency physician inadvertently violates HIPAA using a publicly available AI tool, such as ChatGPT, Gemini, Llama, and Grok, without a valid Business Associate Agreement in place. We review the legal framework of the HIPAA Privacy, Security, and Breach Notification Rules and delineate the respective liabilities of healthcare institutions and individual clinicians. Key distinctions are made between incidental, accidental, and unauthorized disclosures of protected health information, and we provide clear guidance on post-breach mitigation steps. The article also discusses the statistical likelihood of protected health information reidentification or reproduction by AI models and outlines risks associated with state-level data protection laws. Ultimately, we offer practical recommendations for physicians seeking to leverage AI responsibly in clinical care, including verifying institutional Business Associate Agreements, understanding platform-specific privacy policies, and consulting with privacy officers before entering any patient data. As AI rapidly evolves, clinicians must remain vigilant in safeguarding patient information to avoid legal exposure and uphold ethical standards of care.

Water knowledge, understanding the current and future availability and needs of water, has been critical in negotiating international water disputes. Drawing from expert interviews, this article examines how Artificial Intelligence (AI) tools influence knowledge production and exchange in water diplomacy. The findings suggest that technical strides from AI technologies can enhance data and information objectivity and social learning, potentially benefiting water negotiations and consensus building. However, without addressing political and human challenges, AI tools canexacerbate the risk of eroding trust and spreading dis- and mis-information about politically sensitive water issues. The malicious use of AI poses a serious risk, as negotiators may face increased pressure from public opinion, potentially undermining cooperative progress and escalating tensions over water.

To quantify external apical root resorption (EARR) on the incisors following non-extraction treatment of Class I malocclusion patients with moderate crowding, comparing clear aligners and fixed appliances using a novel 3D analysis of Cone-Beam Computed Tomography (CBCT) derived surface models. In this randomized clinical trial, 32 adult patients, mean age 22.3 years, mean treatment duration 24.2 months, with Class I malocclusion and moderate crowding (mean Little's Index 4.76 mm) were allocated to either clear aligner (n = 15) or fixed appliance (n = 17) treatment. CBCT scans were obtained before and after treatment. EARR was measured using surface-based analysis of 3D models, and associations with patient and treatment-related factors were tested. The overall median EARR was -0.72 mm, with no significant difference between clear aligners (-0.71 mm) and fixed appliances (-0.72 mm). Upper lateral incisors exhibited significantly greater EARR than lower incisors (p = 0.002) and upper central incisors (p < 0.001). No significant predictor for EARR was found considering age, sex, crowding severity or treatment duration. EARR occurred following non-extraction treatment of Class I malocclusion with both clear aligners and fixed appliances, with no significant difference between appliance types. Upper lateral incisors were most susceptible to EARR. The novel 3D analysis enabled comprehensive quantification of total EARR, setting a new methodological standard. Monitoring root health during treatment is important, particularly for upper lateral incisors.

Digital companionship: The interplay of conversational AI, loneliness, social anxiety, and quality of life among young adults.

Isocitrate dehydrogenase (IDH) enzymes have recently emerged as a highly promising target for therapeutic intervention in cancer treatment. Mutations in IDH genes result in the production of the oncometabolite, D-2-hydroxyglutarate (D-2HG), which contributes to tumorigenesis through epigenetic dysregulation, genomic methylation patterns and altered cellular metabolism. The functions of IDH1 and 2 under normal and cancer conditions are distinct from those of IDH3, although IDH1 and 2 are known to play a crucial role in cancer. IDH mutations are highly prevalent in various cancers such as gliomas, acute myeloid leukemia (AML) and chondrosarcoma. Thus, IDH inhibitors stand as a promising class of drugs in cancer treatments, by reducing tumor size and enhancing improvements in overall survival. In contrast, targeting specific IDH mutant with IDH inhibitors is associated with challenging and heterogenous outcome, as it causes resistance mechanisms such as isoform switching (From IDH 1-2 and vice versa), secondary mutations (D279N, S280F) and metabolic bypass, although these inhibitors are often well-tolerated with manageable side effects. On the other hand, wild type IDH itself acts as an oncogene when overexpressed, via, enhancing HIF1α signalling driven through Warburg effect, increasing tumor cell proliferation through prevention of oxidative stress response and inhibiting ferroptosis pathway, as reported in various cancers, including lung and breast cancer. Hence, this review emphasizes the biological functions of IDH enzymes, the impact of overexpressed wild type IDH levels and IDH mutations on cancer development and the recent therapeutic strategies, particularly targeting IDH for gliomas, AML and chondrosarcoma treatment. The potential of IDH inhibitors in personalized medicine approaches and their implications for improving patient outcomes, along with the computer-based emerging technologies such as Computer aided drug design, Machine learning and Artificial Intelligence tools for development of novel lead IDH inhibitors are also discussed.

Rapid advances in artificial intelligence (AI) that assist clinical workflows and processes demand systematic educational strategies to cultivate AI competencies within dental curricula. This perspective calls for prioritising educational initiatives to create an AI-proficient dental workforce. AI education should be integrated vertically throughout preclinical to clinical years while ensuring horizontal coherence with existing competencies. Early-stage students should focus on beginner-level competencies, acquiring foundational AI knowledge and ethical considerations. Senior learners should demonstrate the ability to implement AI tools for clinical tasks and to critically interpret AI-generated outputs. Advanced students should be equipped with skills to innovate AI-driven studies and novel applications for oral healthcare. Assessments should also be well designed to capture and evaluate the expected AI competencies. As curricula include massive amounts of both technical and nontechnical content, integrating AI teaching and learning must be carefully balanced with the core competencies required of dental professionals.

This study examines the impact of artificial intelligence (AI) on the sustainability of small to medium-sized tourism enterprises (SMTEs) through an extensive bibliometric analysis of research conducted between 2014 and 2025. The goal is to identify prevailing trends, challenges, and opportunities related to AI integration in the tourism sector, especially concerning SMTEs. Given the rapid advancements in AI-driven technologies, the study also highlights the increasing role of generative AI models in content creation to enhance customer interactions and emphasizes the need for designing and evaluating AI tools tailored to address the constraints faced by SMTEs. A bibliometric study was conducted using the Scopus database, narrowing an initial set of 643 records down to 373 relevant articles. VOS Viewer software (v1.6.20) was used to map publication trends, country contributions, journal outlets, author collaborations, and thematic clusters. To provide a comprehensive overview of the field, the study combined network analysis and thematic analysis. The results indicate a steady increase in publications over the study period, with a notable acceleration after around 2019. International collaboration is also growing, reflecting increased global interest in AI and sustainable tourism. However, despite this growth, only a small portion of studies explicitly focus on SMTEs; most research either addresses larger tourism enterprises or applies generalized models that do not account for SMTE-specific constraints and contexts. The study emphasizes the need for ongoing research to ensure AI tools are used in ways that foster innovation and sustainability within specific types or niche SMTEs, while developing and evaluating AI tools that are affordable, scalable, and responsive to their constraints.

ABSTRACT The integration of Ghanaian languages, especially the Akan language, into generative artificial intelligence tools such as Khaya AI, ChatGPT and Claude AI is a significant step towards the revitalisation of marginalised languages and the projection of the relevance of minority languages in the digital age. However, the extent to which these technologies are used by students pursuing indigenous languages in higher education is unexplored. Hence, using the unified theory of acceptance and use of technology (UTAUT 2), the current study explored the drivers of higher education students’ behavioural intention and actual use of Gen-AI. Data were drawn from 157 higher education students in four tertiary academic institutions in Ghana. Results from a partial least squares structural equation modelling revealed that students’ behavioural intention was predicted by effort expectancy, habit, price value and hedonic motivation. In contrast, social influence, facilitating conditions, and performance expectancy, which are traditionally perceived as strong predictors of technology adoption, did not influence intention in this context. Behavioural intention was found to be a strong predictor of their actual use of Gen-AI in learning Akan. Based on these statistical insights, the study offers constructive suggestions that have implications for policy and practice.

Integrating artificial intelligence (AI) into maternal and neonatal health (MNH) offers significant opportunities for enhancing patient care through advanced predictive modeling, early disease diagnosis, and ongoing monitoring of conditions such as preeclampsia or gestational diabetes. However, significant challenges in economic valuation persist, including data scarcity, complexity, and the nascent stage of AI implementation in clinical practice. There has been no consolidated empirical proof directly justifying widescale AI application in MNH so far, despite its potentially significant economic benefits and direct cost savings. This review demonstrates that AI systems can mitigate adverse drug reactions (ADRs) and enhance the operational efficiency of organizations. As the full economic potential has yet to be understood and quantified, this review examines several existing economic evaluation frameworks: Cost-Effectiveness Analysis (CEA), Cost-Utility Analysis (CUA), Cost-Benefit Analysis (CBA), and Budget Impact Analysis (BIA). A crucial gap exists between rapid technological advancements and robust economic evaluations, further compounded by a lack of standardized reporting frameworks that hinder the synthesis of available evidence. In addition, the review addresses key challenges, including how they affect the healthcare workforce and the economic impact of systemic errors and security breaches, and then discusses the clinical and liability risks posed by "black box" models. Furthermore, the frequent updates essential for the clinical efficacy and safety of AI tools in MNH are often tied to subscription-based models, creating significant financial strain, particularly in low and middle-income-countries (LMICs). To bridge this crucial research gap and the absence of uniform reporting, this paper proposes the AI-MNH economic evaluation lifecycle and a tailored CHEERS checklist. This multi-phase framework is designed to guide comprehensive, long-term economic evaluations and the adoption of a consolidated, standardized approach to support evidence-based policymaking and sustainable resource allocation.

This study analyzed the use of artificial intelligence in the processes of guidance and psycho-pedagogical support at a public educational institution in Ecuador. The research was conducted with a quantitative approach, a non-experimental design, and a descriptive scope, considering a sample of 50 students from middle school, upper elementary school, and the unified general baccalaureate program. Pedagogical observation techniques, a structured survey, and an academic monitoring form supported by an artificial intelligence tool were employed. The results showed improvements in academic monitoring, personalized feedback, and the early identification of learning difficulties. Most students perceived the support as adequate, with notable progress in school organization, participation, and confidence in learning. Furthermore, it was observed that artificial intelligence optimizes the counselor's intervention by providing relevant information for pedagogical decision-making. The study concludes that artificial intelligence constitutes a complementary resource that strengthens attention to diversity and promotes inclusive education without replacing the professional role of the psycho-pedagogue within the school context.

Nurses confront artificial intelligence-generated health information at the bedside: A case study.

Binding affinity is a critical parameter that can influence the state of the drug in vivo and help to define the formulation strategy. The current study implements a multimodal approach to analyse the binding affinity between human serum albumin (HSA) and pioglitazone. Ultraviolet (UV) absorbance and fluorescence spectrometry analyses were performed on different combinations of HSA and pioglitazone complexes, and the absorbance and fluorescence intensities were mapped to calculate the binding constant. DynamicBind, a distinct deep-learning artificial intelligence tool, was implemented to perform in silico docking studies using a non-conventional approach. Furthermore, molecular dynamics simulation was also performed to generate root mean square deviation, radius of gyration, and root mean square fluctuation values, followed by principal component analysis, probability distribution function, and free energy landscape analysis. The simulation output was analysed to interpret the binding affinity and associated conformation of the protein-active pharmaceutical ingredient (API) complex. The binding constant calculated through UV analysis was 1.1 × 104 M-1. Fluorescence spectroscopic analysis derived a value of 1.7 × 105 M-1. At the same time, DynamicBind predicted the cLDDT score for the top predicted model to be 0.634, and a binding affinity value of greater than 5, indicating a relatively moderate binding between pioglitazone and HSA. The results from molecular dynamics simulations further complemented our earlier observations, indicating non-covalent binding interactions and a stable protein-API complex, which is desirable for developing a formulation using HSA as a carrier polymer. This orthogonal approach also provided critical information on the fate of the API and possible considerations that needed to be made during the design of the formulation process, highlighting the need for similar approaches that could provide multifaceted advantages and help in optimising R&D costs and timelines.

ABSTRACT Purpose To determine the diagnostic accuracy and reliability of artificial intelligence (AI) in identifying diabetic retinopathy (DR) and macular oedema (ME) compared to ophthalmologists. Methods This prospective study included 294 patients (576 eyes). Fundus images obtained using a non-mydriatic Topcon NW400 fundus camera were analyzed by an AI tool (Google ARDA (Automated Retinal Disease Assessment). Clinical grading was performed by a retina specialist using the International Clinical DR Severity Scale and considered the reference standard. Sensitivity, specificity, predictive values, and inter-grader agreement (κ statistics) were calculated. Results The AI tool identified 69.8% of the eyes as DR, compared to 75.2% by the retina specialist, with an 83.3% accuracy rate, specificity of 90.9%, sensitivity of 97.1%, and Kappa = 0.77. For DME, AI classified 15.3% of eyes, compared to 5.9% by ophthalmologists, with an 89.9% diagnostic efficiency and Kappa = 0.48. Conclusion AI tools show high sensitivity and substantial agreement with ophthalmologists in diagnosing DR and DME, indicating their potential to enhance diagnostic accuracy and efficiency in retinal health screening.

This paper presents a review on the use of technology in promoting teacher professional development in relation to its utilisation, benefits, challenges, and best practices. Lack of systematic analysis on these aspects within the literature prompted the need for this review. A systematic review technique was applied, and 47 studies published between 2010 and 2025 from various countries were selected from different databases for detailed analysis. These studies focused on in-service school professional development and higher education teachers. The findings highlight that teachers use digital tools, platforms and materials, such as smartphones, computers, laptops, Artificial Intelligence tools (like ChatGPT), Learning Management Systems, webinars, social media communities, Open Educational Resources, and multimedia for professional development. They found several benefits to the use of technology for their professional development, such as its flexibility, eliminating geographical boundaries, easy resource availability, time saving, extending professional networks, and improving digital skills. However, lack of digital devices, limited digital skills, poor internet connections, insufficient interaction, inadequate institutional support, concerns about AI accuracy, and plagiarism were found to be challenges. Best practices include receiving governmental and institutional support, designing user-friendly platforms, encouraging effective collaboration, considering participants' psychological factors, and conducting follow-up studies to gauge its effectiveness. The results of this systematic review should assist stakeholders in addressing these limitations and using technology more effectively.

Artificial intelligence (AI) is increasingly shaping radiology, though its integration into paediatric radiology has progressed more slowly due to challenges specific to the paediatric population. This is especially true in the field of paediatric abdominal imaging. Key barriers include regulatory and ethical issues, the scarcity of large paediatric datasets necessary for algorithm training, reduced vendor interest linked to limited economic incentives, and the inherent differences in children throughout the developmental stages including organ size, signal/sonographic characteristics, and pathologies. Despite these obstacles, AI has the potential to enhance clinical care by augmenting radiologists' workflow across both interpretive and non-interpretive tasks. Currently, most published research focuses on AI's role in musculoskeletal imaging. Although AI is expanding its reach in other imaging domains, paediatric imaging lags behind, as does its potential in abdominal imaging. The use of AI in paediatric abdominal imaging has received limited attention in the existing literature. Emerging research applications cover multiple tasks: detection, classification, functional analysis, severity prediction, automated segmentation, image quality optimization, and acceleration of image acquisition. This review aims to provide practicing radiologists with a concise, simple, and clinically oriented overview of the potential applications and limitations of these new AI tools in paediatric abdominal imaging, categorized by organ. For the time being, most applications described in the literature remain confined to the research setting. To advance these approaches towards clinical utility, validation on larger and more heterogeneous datasets is required. Moving forward, it will be essential to integrate human expertise with AI systems to strengthen diagnostic capacity in paediatric abdominal radiology and to promote paediatric-specific regulatory standards, clear governance structures, and human-centred oversight.

The integration of artificial intelligence (AI) into education quickly transforms education and training, especially in the fields of science and mathematics. This study investigated the impact of AI-based educational tools based on conceptual understanding of mathematics among first-year teachers at private education facilities in Rwanda. Using a quasi-experimental mixed-methods design, 14 participants were intentionally assigned to the experimental group (n = 7). This used AI-based tools and traditionally directed control groups (n = 7). The purpose of this study is to compare conceptual understandings between groups. Results show that the experimental group showed significantly higher learning results (mean amplification = 33.5%) compared to the control group (mean amplification = 18.5%), indicating that 71.4% of AI users reached excellence (post-test &amp;gt; 80%) compared to 28.6% of traditional group. Statistical analysis confirmed a significant difference in the index after testing (t = 3.24, p = 0.007). Furthermore, a strong positive correlation was found between the frequency of AI usage and conceptual increase (r = 0.85, p = 0.017), indicating the importance of sustainable interactions. The results of the research based on the technology adoption model showed that participants had a positive attitude towards AI tools and identified improvements in their usefulness, ease of use, and interaction. A significant correlation between perceived ease of use and utility (r = 0.78, p = 0.023) highlighted the important factors affecting adoption. This study concludes that AI-based tools significantly improve conceptual understanding of mathematics that is significantly integrated into educational education. These results provide valuable information to teachers who use AI to support future teacher skills and seek to support training in training.

This study conducts a bibliometric and content analysis of ‘artificial intelligence-enabled auditing’ over three decades. The use of artificial intelligence (AI) tools in auditing has evolved and is now an imperative practice in the auditing space. Using bibliometric methods via Bibliometrix R-package (Biblioshiny) and VOSviewer, this research mainly examines the scholarly discussion on AI-enabled auditing, using the Scopus database. The main themes identified are: Theme 1: AI in auditing: readiness, representation, and implementation; Theme 2: data-driven audit ecosystems and digital technologies; and Theme 3: audit quality, professional skepticism, and ethical governance. On the descriptive end, publication trends, prominent authors, articles, and sources are identified. The findings highlight a significant increase in AI-enabled auditing studies since 2018, coinciding with growing global awareness on the importance of AI across all spheres of business. The outcome of this research contributes to a wide array of stakeholders, including businesses, audit firms, shareholders, and policymakers; it should give insights to business organizations on the capabilities of AI-assisted auditing, while policymakers should have access to verifiable, auditable and regulatory-compliant systems for the implementation of their regulations. Investors may further enhance their knowledge in terms of how AI-assisted auditing increases the quality of their investment decisions and, at the same time, the risks involved. Finally, auditing firms should further invest in improving the application of technology in the auditing environment and ensure quality, evidence-based audit outcomes, and reporting.

Human resource management is a strategic axis for organizations, especially in contexts where artificial intelligence (AI) tools, such as natural language processing (NLP), play a fundamental role. Recruiting external applicants from large CV repositories requires consistent screening. The proposed methodology involves leveraging an existing curriculum vitae (CV) repository, structuring and indexing the data within a vector-based knowledge base, and applying retrieval techniques to identify candidates that satisfy role-specific criteria. Using 5029 CVs as benchmarks, we evaluate 3 queries, 3 variables (Degree, Skills, Experience), and 7 scenarios. Sampling n = 76 CVs for Queries 1–2 and n = 350 CVs for Query 3. The proposed approach achieved consistently high specificity across scenarios and query profiles, while sensitivity showed the largest fluctuations, particularly under single-requirement configurations. Across all queries and scenarios, accuracy ranged 65.79–98.00%, specificity 86.67–100.00%, and sensitivity 0.00–94.92%, while error rates decreased from 34.21% to 2.00% as constraint strictness increased. Sensitivity fluctuated most under single-requirement settings, and Experience-only screening showed the weakest selection behavior. Moreover, the results indicate that the ability to confirm suitable candidates is sensitive to query formulation, since non-standard role naming, experience phrasing, and other lexical variations can reduce the system’s capacity to detect positive evidence. Overall, these findings indicate that a knowledge-base-centered design enables consistent and interpretable requirement-driven candidate screening and provides a quantitative baseline for future improvements in recruitment-oriented retrieval systems.

Abstract We examine whether information processing constraints limit managers’ ability to effectively integrate tax planning and core business strategies (i.e., effective tax planning). We propose that artificial intelligence (AI) tools, such as machine learning, can mitigate these constraints by providing enhanced predictive information for key business decisions (e.g., customer demand, supply chain), thereby reducing processing costs. Using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018, we find that AI investment is positively associated with tax effectiveness. This effect is concentrated among more complex firms and those where the tax function holds a higher status. Consistent with AI reducing information processing costs, we find that it improves tax effectiveness by enhancing internal information quality and internal capital management. We provide novel evidence that processing constraints hinder effective tax planning and show that AI can mitigate these constraints.