Ethical Research Articles

Fraud Factor Analysis Hexagon in Detecting Financial Report Fraud In Listed Companies in Indonesia: A Systematic Literature Approach

Financial reporting fraud is a significant challenge across industries, undermining stakeholder trust and the reliability of financial data. The Fraud Model Hexagon expands upon earlier models, like the Fraud Triangle and Fraud Diamond, by introducing six elements pressure, opportunity, rationalization, capability, ego, and collusion that offer a comprehensive view of the factors enabling fraud. This literature review systematically examines the model's effectiveness in detecting financial fraud within Indonesian corporations, especially those listed on the Indonesia Stock Exchange or connected to political and governmental entities. Ten studies from 2018 to 2024 were analyzed, focusing on each Fraud Hexagon element's role in fostering fraud. Findings highlight that pressure, opportunity, and collusion are primary drivers of fraud in politically connected sectors, with capability and rationalization further facilitating fraud, while ego emboldens managers to act without fear of repercussions. The Fraud Hexagon provides a nuanced framework for risk assessment, prompting recommendations for stronger internal controls and ethical standards. It also serves as a valuable diagnostic tool for auditors and regulators, particularly in politically sensitive sectors. This study contributes to understanding the social and psychological dimensions of financial fraud and offers a foundation for future cross-industry research.

An ESG-Compliant Framework for Fraud Detection in Online Payments Using Data Privacy and Machine Learning

Purpose: The purpose of this paper is to analyze the role of machine learning (ML) in fraudulent online payment transaction detection across industries, with a specific focus on integrating Environmental, Social, and Governance (ESG) principles to bring sustainability and ethics in fraud detection. Methodology: This study reviews the effectiveness of machine learning (ML) techniques in real-time fraud detection, presenting the efficiency of different ML techniques across industries and how those techniques can be modified to apply the principles of ESG. It checks on ethical practices concerning data, compliance with data privacy, and sustainable management, further investigating how effectively ML-driven frameworks can support fraud detection without compromising the standards of ESG. Findings: The study finds that integrating ESG principles within machine learning frameworks for fraud detection enhances both the effectiveness and ethical alignment of these systems. ML models not only support real-time fraud detection but also reinforce sustainable data management and governance practices, providing businesses with an advanced approach to mitigating cyber risks while upholding ESG commitments. Unique Contribution to Theory, Practice, and Policy: This paper contributes by advancing ESG-integrated frameworks for fraud detection, offering a sustainable and ethical model for using ML in financial systems. In practice, it provides actionable insights for industries seeking to align fraud prevention with ESG goals. The paper also suggests policy considerations, advocating for stronger ethical standards and data governance in ML applications for fraud detection.

Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review.

To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care. AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care. A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing. The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing. The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards. Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing. AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance. Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.

Empathetic Leaders: Cultivating Success Through Multifaceted Support and Continuous Growth

This study aims to explore the interconnected roles of mentoring, coaching, modeling, and supervision in supporting personal and professional development. A systematic literature review was conducted, sourcing 29 articles to clarify definitions, roles, and distinctions among these frameworks. Through mentoring, individuals gain structured guidance; coaching fosters self-discovery; modeling provides behavioral examples; and supervision ensures adherence to ethical standards. The results reveal unique and overlapping functions of these roles, especially emphasizing coaching supervision’s value in maintaining quality and ethical practices. This study’s novelty lies in its comprehensive integration of these concepts within a single framework, highlighting synergies that enhance developmental outcomes. Consequently, this work contributes to the field by offering a holistic approach for practitioners to optimize human potential across educational and professional contexts.

CLINICAL EFFECTIVENESS AND FEATURES OF THE POSTOPERATIVE PERIOD IN PATIENTS AFTER OSTEOSYNTHESIS OF THE MANDIBLE USING CELL TECHNOLOGIES

Due to a signifi cant increase in the frequency and complexity of maxillofacial injuries, one of the priorities in the development of modern surgical dentistry is the search for new eff ective methods of treating facial bone fractures. In recent decades, the attention of clinicians has been focused on biological treatment methods aimed at restoring bone defects using cell technologies and biological transplants.The aim of the study was to determine the eff ect of using osteoplastic materials based on multipotent adipose- derived mesenchymal stromal cells in dental patients in the surgical treatment of mandibular fractures.Materials and methods. The clinical material included 56 patients, 13 of whom underwent mandibular osteosynthesis with hydroxyapatite bone graft substitute (subgroup 1I), 25 of whom underwent surgery with a combination of multipotent adipose tissue mesenchymal stromal cells, hydroxyapatite bone graft substitute and platelet-rich plasma (subgroup 1B), and 18 of whom underwent spontaneous healing of the fracture line (subgroup 1C). Patients were clinically evaluated on a daily basis. Clinical parameters were assessed on days 1, 3, 5, 7, 10, 14 and 28 after surgery. The postoperative pain syndrome was assessed using the Numerical Rating Scale (NRS), considering the subjective signs of pain in the operated patients.The statistical processing of the study results was performed using the statistical analysis software for biomedical research ‘Microsoft Excel 21’ and ‘Statistica’ (licence number STA862D175437Q). In the statistical processing of the results, we used the variational analysis of series – calculation of the arithmetic mean and its standard error (Mim) – and assessed the signifi cance of diff erences in the results obtained in the comparison groups using the Student’s t-test method. p<0.05 was considered a signifi cant diff erence. [17]. The study was conducted in accordance with the basic provisions of the «Rules of Ethical Principles for Scientifi c Medical Research Involving Human Subjects» approved by the Declaration of Helsinki (1964-2013), ICH GCP (1996), EEC Directive 609 (24.11.1986), Orders of the Ministry of Health of Ukraine ¹ 690 of 23.09.2009, ¹ 944 of 14.12.2009, ¹ 616 of 03.08.2012. The Commission on Biomedical Ethics of the Bukovinian State Medical University found no violations of ethical and moral standards in the conduct of the research (ProtocolNo. 6, 16.03.2023). Patients signed informed consent to participate in this study and all measures were taken to ensure their anonymity.The results. The comparative analysis of the clinical eff ectiveness of the use of various osteoplastic materials in the performance of osteosynthesis surgery of the lower law confi rmed the advantages of the use of bone tissue substitute based on hydroxyapatite and its combination with multipotent adipose tissue mesenchymal stromal cells before spontaneous augmentation. It is confi rmed by the data of subjective and objective symptoms of patients during the postoperative period.Conclusions. Our data show that osteoplastic material based on multipotent mesenchymal stromal cells of adipose tissue, bone substitute based on hydroxyapatite and platelet-rich blood plasma is a promising biological material. Its application improves the regenerative and reparative properties of bone tissue, facilitates the postoperative period and shortens the duration of hospitalization, promotes the development of innovative ways of reconstructive osteogenesis in modern maxillofacial surgery.

Next generation tissue biobanking: quality assured, financed, and integrated?

Biobanks are essential for biomedical research, particularly in the era of personalized medicine. In Germany, 36biobanks have been established over the past decade that are connected under the German Biobank Alliance (GBA). These biobanks store high-quality biological samples along with clinical data to support research projects. Biobanks can be integrated, handling both tissue and liquid samples, or set up as separate entities depending on specific requirements.Tissue biobanking is especially complex due to the invasive nature of tissue collection and the non-replicability of the samples. Close collaboration between clinics, pathologists, IT specialists, and biobank managers is crucial to ensure the quality of samples and promote interdisciplinary research.The integration of pathology and biobanking is key, both organizationally and technically. Shared IT systems, standardized protocols, and collaborative governance structures are vital for efficient data management. Quality assurance, ethical guidelines, and data protection are critical to maintaining public trust and legal compliance.Long-term financial models are needed to ensure the sustainability of biobanks. The GBA supports emerging biobanks through its "Starterkit" initiative, offering guidance and best practices to help new biobanks develop.Tissue biobanks are indispensable for advancing the understanding of diseases and developing new therapies. However, they must adhere to strict ethical and legal standards to maximize their scientific and societal value.

Industry 5.0 for Society 5.0 a new Transformation

Industry 5.0 represents a transformative approach within Society 5.0, leveraging advanced technologies like AI, IoT, Robotics, and Blockchain to enhance productivity, automation, and decision-making across various sectors. This abstract examines how privacy, security, and ethical issues are addressed by modern technologies to create a societal structure that is morally and humanely sound. IoT improves smart city infrastructure and environmental monitoring, while AI enables tailored education and predictive healthcare. Blockchain guarantees safe and transparent data transfers, while robotics helps with accurate healthcare operations. Collectively, these technologies support Society 5.0's sustainability, inclusivity, and resilience. They also hold out hope for a time when technological breakthroughs will preserve strict ethical standards and cybersecurity safeguards while prioritizing the needs of people and society.

Panchakarma fitness form for clinical practice and research in Ayurveda

Background: The Panchakarma Fitness Form is an essential tool in Ayurveda, used to assess a patient’s suitability for specific Panchakarma treatments. By evaluating Dashavidha Pareeksha, Sama-Nirama Pareeksha, assessment of Agni, assessment of Koshta, examination of vital parameters, medical history, and other relevant factors, this form ensures that treatment decisions are informed, ethical, and patient-centered. Objectives: To develop and implement a Panchakarma Fitness Form for evaluating patient fitness for Panchakarma therapies and to assess its effectiveness in enhancing treatment safety, personalization, and compliance with clinical and ethical standards over a five-year period. Methods: A comprehensive Panchakarma Fitness Form was designed, incorporating sections on classical advocation on fitness for different Panchakarma therapies, medical history, clinical examination of patient, allergies, medication, and lifestyle factors. The form was used in both routine healthcare and clinical research settings, with a particular emphasis on its role in screening for study eligibility. The implementation of the Panchakarma Fitness Form enhanced patient safety by identifying contraindications and optimizing treatment decisions. This form contributed to improved treatment efficacy by tailoring therapies to individual health conditions and supporting personalized care. In clinical research settings, the form facilitated participant selection, improving study integrity and data reliability. Conclusions: The Panchakarma Fitness Form is a valuable tool that supports safe, effective, and personalized Ayurvedic treatment. By systematically assessing patient readiness, it reduces risks, enhances treatment outcomes, and aids in legal and ethical compliance in both clinical practice and research. This form sets a standard for quality and safety in Ayurveda, supporting patient-centered and informed healthcare.

Understanding BNS Sections 238 and 61(2): Evidence Tampering, Supplying False Information to Protect a Criminal, and Police Accountability

This paper provides a detailed analysis of Sections 238 and 61(2) of the Bharatiya Nyaya Sanhita, 2023 (BNS), focusing on the implications of tampering with evidence or providing false information to protect a criminal, especially concerning the responsibilities of police officers. Those who deliberately obliterate, remove, destroy or conceal evidence related to a crime or offer false information to shield an offender face penalty under Section 238 BNS, where the severity varies based on the nature of the offence, potentially resulting in a prison sentence of up to seven years and a fine. In addition, Section 61(2) BNS in this regard addresses issues related to criminal conspiracy between two or more persons in order to destroy or temper with evidence or furnish false information to shield a criminal from arrest or criminal liability and stipulates punishment commensurate with the seriousness of the offence. It also underscores the importance of investigating police officers' safety and the need to adhere to ethical and investigative standards. The study highlights the crucial role of honesty and transparency within law enforcement to promote accountability and uphold the principles of justice.

Semantic Segmentation Based on Geometric Calibration Using AI and AR in Health Care

In the medical field, medical imaging is essential for precise diagnosis, treatment planning, and condition monitoring. The goal of this work is to improve the field of healthcare imaging by investigating the complementary applications of augmented reality (AR) and artificial intelligence (AI) in semantic segmentation. More precise identification and delineation of anatomical structures and abnormalities in medical images is made possible by AI-driven semantic segmentation, opening the door to more precise diagnosis and treatment plans. Modern AI algorithms are employed in the suggested methodology to perform semantic segmentation in a variety of medical imaging modalities, including ultrasound, CT, and MRI scans. The detection of anomalies such as tumours, irregularities in organs, and neurological disorders is made easier by these algorithms. The foundation for integrating augmented reality technologies into the healthcare ecosystem is provided by the segmented medical images. AR enhances the interaction and visualization of segmented medical data in the context of healthcare. Real-time augmented reality overlays help surgeons by improving surgical navigation and precision. Additionally, AR apps help with medical education by offering professionals and students alike an immersive learning environment. AR provides interactive visualizations to help patients understand their medical conditions when they are going for therapy and rehabilitation. The difficulties in integrating these technologies in the healthcare industry are discussed in this paper, with a focus on the significance of data privacy, regulatory compliance, and easy integration with current healthcare systems. The successful deployment of AI technologies necessitates interdisciplinary collaboration among AI developers, healthcare professionals, and AR specialists to ensure compliance with ethical, legal, and clinical standards mandated in the healthcare domain. The proposed Segmentation mask is redefined with geometric calibration and used with U-Net for image segmentation. The segmentation is experimented on Cityscapes and PASCAL VOC 2012 datasets. The experimental results show that proposed semantic segmentation based on geometric calibration yields more accuracy than its counterparts.

Big Data Analytics And Predictive Analysis In Enhancing Customer Relationship Management (CRM): A Systematic Review Of Techniques And Tools

This systematic review examines the transformative role of big data analytics and predictive techniques in Customer Relationship Management (CRM), focusing on how these advancements enhance customer engagement, satisfaction, and retention strategies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzed a final selection of 100 articles, exploring CRM applications such as predictive modeling, real-time data processing, data integration, and Natural Language Processing (NLP). Findings indicate that predictive analytics enables CRM systems to anticipate customer behaviors and personalize interactions, while real-time data processing supports immediate responses to customer needs, essential in high-demand sectors. Data integration helps overcome silos, creating a cohesive customer view that enhances personalized engagement across all touchpoints. NLP applications, including sentiment analysis and chatbots, further improve CRM by fostering empathy and responsiveness in customer interactions. Despite these advancements, challenges related to data privacy and regulatory compliance remain central, necessitating strict data protection and ethical handling practices. This review underscores that while CRM technology is evolving to meet modern demands, effective implementation depends on a balance between technical innovation and adherence to ethical standards in data management, ultimately fostering meaningful and sustainable customer relationships.

AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors

This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards.

Season-dependent characteristics of shock-related injuries in the Arctic zone of the Arkhangelsk region

Relevance. Studying the dynamics of injury rates, injury types and severity, as well as the treatment quality across various country regions, in particular the Arctic, is a prerequisite for managerial, healthcare, and strategic efficiency prior to and during hospital stay, taking into account the climate, geography, social and economic characteristics of every Russian Federation constituent.The objective is to analyse shock-related injuries in the Arctic zone of the Arkhangelsk region to understand season-related injury dynamics.Methods. Retrospective case series description parameters were used to study 140 patients with traumatic shock admitted to the Arkhangelsk Regional Clinical Hospital (ARCH) - a tier 1 trauma center. The analysed medical registration records included patient ambulance record and token (form 114/u), inpatient medical record (form 003/u), surgery logbook (form 008/u), patient radiographs, CT and nuclear MRI reports, as well as laboratory results. The study was conducted in accordance with ethical standards of the Declaration of Helsinki and European Community Directives (8/906 EC) and was approved by the local ethics committee of the Northern State Medical University of the Russian Ministry of Healthcare, Arkhangelsk (protocol no. 02/4-15 dated 04/08/2015). Statistical application package STATA ver.12 was used for statistical data processing.Results and discussion. The research demonstrated that the incidence of shock trauma is 4 times higher in men than in women. The vast majority of the injured are of working age. Seasonal distribution analysis per type of injuries and shock severity, including due to alcohol abuse, was carried out. The number of shock injuries is highest in winter, mainly due to road accidents which constitute 75.6 % of all injuries in winter. Throughout the year, over 50 % of shock injuries are combined injuries, that outperform isolated and multiple injuries in number. In particular, numerous combined injuries occur in winter with a responsible for almost 60 % among all other injuries. The majority the injured with shock-related trauma presented with stage 2 shock (49.3 %), whereas stage 1 and stage 3 shock was diagnosed in 27.9 % and in 21.4 % respectively. In winter, one in four patients with a shock-related injury presented with stage 3 shock. Alcohol-associated shock injuries are more numerous in summer (35.3 %), showing almost the same incidence as road traffic, domestic, and street injuries.Conclusion. The analysis of various types of injuries and season-related incidence of shock injuries in the Arctic territory of the Arkhangelsk region provides evidence to justify preventive measures to reduce the injury rates. Intensified efforts are required to minimise road traffic accidents in winter and combat alcohol abuse responsible for most injuries in summer. Since the vast majority of shock injuries occur in winter, in cold months ambulances must be equipped with local heating facilities, such as evacuation thermal bags with autonomous electric heating, thermal blankets, infusion warmers for blood transfusion and infusion sets.

A risk-based approach to mitigating the Gen(AI) challenge to assessment integrity

In the evolving landscape of higher education, emerging technologies such as Generative AI (GenAI) present both opportunities and challenges. The rapid increase in access to, and sophistication of, GenAI poses significant challenges for our institution and the wider sector (Bearman & Luckin 2020, Bobula 2024, Chan & Hu 2023). The most immediate of these challenges is the increased risk to the existing approaches used to assure that students achieve the stated outcomes of their learning experiences. Director of Higher Education Integrity Unit, TEQSA, Dr Helen Gniel argues ‘We are entering an age where institutions can no longer ensure the integrity of every single assessment, and it makes sense to think at that bigger level […] what are the mechanisms that you have in place to be confident that an individual has demonstrated the learning outcomes to receive that degree’(TEQSA information request: AI risk mitigation, webinar, 21 March 2024). While higher education institutions are exploring opportunities to realise the potential benefits of GenAI, our immediate focus is on mitigating these risks. This poster describes institutional Programmatic Assessment Security Project (PASP) at the University of Newcastle (UON), a pioneering four-stage initiative designed to address academic integrity risks associated with inappropriate uses of GenAI in summative assessment (Lodge 2024). Academic integrity risks in this context refers to the potential for students to engage in academic misconduct by using GenAI tools in ways that violate institutional policies or ethical standards for assessment. These risks pose a threat to the validity and reliability of summative assessments, potentially compromising the integrity of academic qualifications and the learning process itself. Led by the central teaching and learning support unit, in collaboration with the Artificial Intelligence Working Group (AIWG) a group of expert practitioners and academic stakeholders at UON, the PASP aims to secure assessments across a program of study, assuring student attainment of program learning outcomes. This innovative approach aligns with the sector’s focus on programmatic assessment and supports compliance with regulatory requirements, including TEQSA’s mid 2024 request for institutions to submit a detailed action plan to mitigate the risks that GenAI poses to the integrity of awards (TEQSA 2024). This poster showcases exemplary innovation in practice through the PASP, advancing research and practice in technology-enhanced learning (TEL). The PASP employs a risk-based approach, identifying seven risk factors for inappropriate student use of GenAI in assessment tasks. The first step in this project involved Course Coordinators completing an Assessment Reflection Survey, guiding them to reflect on individual assessment tasks in relation to these risk factors. The survey data were then used to compile an understanding of the varying levels of assessment risk across the institution’s programs. The PASP will deliver a defined series of secure assessments, delivered at key moments across a program of study to assure student attainment of program learning outcomes. This poster provides insight into UON’s contemporary and agile approach to TEL and assessment redesign, with potential for staff at other institutions to adopt similar responses.

Women in Higher Education Leadership

The integration of Artificial Intelligence (AI) into the higher education landscape has caused upset to many long-held practices in the sector as educators question the future. The male-dominated leadership landscape in AI reflects broader gender disparities prevalent in STEM fields. This position paper investigates the challenges women face in academic leadership within the context of AI and proposes strategies to address these disparities. By examining reflective narratives from three women leaders in higher education, we identify themes of visibility, recognition, and inclusive leadership. Our findings highlight the importance of mentoring programs and collaborative leadership models to support women in AI leadership roles. We argue that diverse leadership not only fosters innovative solutions and equitable access but is also crucial for ethical AI development. Promoting diverse voices in AI leadership can drive meaningful change, ensuring AI technologies benefit a broader demographic and align with ethical standards. Future research should focus on strategies to enhance visibility and support for women leaders in AI, addressing both gender and cultural dynamics.

Balancing Safety and Justice, the Ethics of Predictive Policing

Examining the ethical ramifications of predictive policing in South Africa, the research aims to pinpoint major obstacles and dangers while examining potential solutions to allay worries and guarantee responsible application. Key subjects include data privacy, algorithmic bias, over policing, community involvement, accountability and transparency, and transparency. reduction of bias, oversight procedures, Important Points, Significant ethical issues are brought up by predictive policing in South Africa, including invasions of privacy, algorithmic prejudice, and over policing. Given the historical and current backdrop of racial inequality, these worries are more pressing. In order to address these issues, a balanced strategy that puts individual rights and public safety first is needed. In order to guarantee that predictive policing is used responsibly in South Africa, transparency, accountability, community involvement, and ethical standards are crucial. It is essential to mitigate bias in algorithms and data in order to stop biased results. An over-reliance on predictive policing can be detrimental to community relations and conventional enforcement techniques. Methodological strategy: Designing qualitative research, review of the literature, semi-structured interviews with members of the public, law enforcement officers, and predictive policing specialists Case studies of South African predictive policing initiatives, Content study of South African media reports on predictive policing. Relevance. The work advances our knowledge of the moral conundrums raised by predictive policing in South Africa in a more comprehensive and nuanced way. The study can serve to raise public awareness and encourage participation in the discussion over predictive policing in South Africa. The findings can assist policymakers and law enforcement agencies in creating moral and practical strategies for implementing predictive policing. In conclusion, there are advantages and disadvantages to predictive policing in South Africa. Although technology presents the possibility of more effective and efficient law enforcement, it also brings up serious ethical issues. South Africa can minimize the dangers associated with predictive policing while maximizing its potential advantages by addressing these issues and putting the necessary procedures in place.

Investigating Remote Healthcare Accessibility with AI: Deep Learning and NLP-Based Knowledge Graph for Digitalized Diagnostics

Introduction: Recently, vast generational modern AI techniques have facilitated developments for accessing digital healthcare diagnosis with capabilities of detecting illnesses. Problem: There exists a lot of scepticism for e-health couple with high similarities on health symptoms which hinder text data analysis for remote diagnosis limiting remote services and affecting tech development. Objective: This research investigates and substantiates opportunities associated with computational leverage of text data analytics and cognitive extraction of knowledge insights to improve healthcare outcomes. Significance: The study presents an overview of public, an integrated deep learning (DL), and AI knowledge graph (KG) for healthcare accessibility of remote diagnostics with NLP assist. Method: This research applied both qualitative and quantitative analysis. Questionnaires were used to understand the computational analytics and cognitive extraction of AI knowledge graphs on healthcare data. Also, an AI model was built to detect, diagnose based on text data and streamline five (5) related disease symptoms for each given text input. Results: The result of the survey was tested with hypotheses of H1, H2, H3, H4, H5. Results show that deep learning models and knowledge graphs can effectively lead to a well-defined class of data classification. Our model also exhibits a tremendous level of acceptable prediction of health symptoms based on text data. The significant group was accepted as an identified health issue and the non-significant was identified as a non-health issue. Conclusion: The study concludes that a well-defined system based on a rigorous ethical healthcare standard can easily support determining a feasible remote diagnosis.

Rozwój technologii kwantowej: wyzwania i aspekty regulacyjne. Przegląd wybranych zagadnień prawnych

In the era of the Fourth Industrial Revolution, quantum technology is entering the scene with the promise of radically changing the technological paradigm. Quantum computing, simulation, quantum communications and the combination of this technology with artificial intelligence are opening up new horizons of innovation that could revolutionize various industries. Along with its enormous potential, this technology brings legal, ethical and social challenges that require detailed analysis and an interdisciplinary approach. The industrial deployment of quantum technology entails dilemmas regarding human rights, cyber security and national security, as well as the risk of exacerbating inequality, technological exclusion and algorithmic discrimination. With regard to artificial intelligence enhanced by quantum computing technology, we can speak of a completely new facet of the problem of the opacity of algorithms, which is determined not only by the cognitive limitations of the human mind, but stems from the indefinability of the world described by the laws of quantum mechanics. In light of the above, the thesis can be advanced that the classical approach to the principle of transparency and the postulates of creating algorithms that will clearly present the path to the final result, which are part of the broad current of creating ethical and explainable artificial intelligence, may prove difficult to realize in relation to Quantum AI. In the era of the Fourth Industrial Revolution, we are therefore faced with the challenge of implementing new instruments for testing, certifying and inspecting algorithms, such as tools for analyzing and visualizing the results of quantum algorithms, which will be suited to the specifics of Quantum AI and ensure the ethical correctness of the systems. This article will identify selected areas of application of quantum technology, and thus the potential benefits and risks of quantum technology, and also analyze the need to develop ethical and legal standards that allow for the sustainable and socially responsible development of these disruptive technologies.

Harnessing Artificial Intelligence to Transform Secondary Education in India: Innovations, Challenges, and Future Directions

India's National Education Policy (NEP) 2020 envisions a comprehensive education system that is inclusive, fair, and geared towards lifelong learning. Artificial Intelligence (AI) has a pivotal role in achieving these goals by enhancing personalized learning experiences, streamlining assessments, and promoting inclusivity within secondary education. This paper explores the integration of AI in Indian secondary education, analyzing its alignment with NEP 2020 objectives. It highlights AI innovations, such as adaptive learning platforms and automated grading systems, while also addressing challenges like data privacy, infrastructure limitations, and the need for teacher training. Through a review of recent literature and case studies, this paper offers recommendations for the responsible adoption of AI in India, underscoring the importance of policy support, ethical standards, and regional infrastructure development.

Spatial Analyses of Crisis Pregnancy Centers and Abortion Facilities in the United States, 2021 (Pre-Dobbs): Cross-Sectional Study

Background Crisis pregnancy centers (CPCs) are religious nonprofit organizations with a primary mission of diverting people from having abortions. One CPC tactic has been to locate near abortion facilities. Despite medical groups’ warnings that CPCs do not adhere to medical and ethical standards and pose risks, government support for CPCs has significantly increased. Objective This study aims to map CPCs, abortion facilities, and geographical areas in the United States into 4 zones based on their proximity to CPCs and abortion facilities. We sought to describe the number and percentage of reproductive-aged women living in each zone and the proximity of CPCs to abortion facilities. Methods Using 2021 data from CPC Map and the Advancing New Standards in Reproductive Health Abortion Facility Database, we determined the ratio of CPCs to abortion facilities. Along with census data, we categorized and mapped US block groups into 4 distinct zones based on locations of block group centroids within 15-mile (1 mile is approximately 1.609 km) radii of CPCs and abortion facilities, namely “no presence,” “CPC only,” “abortion facility only,” and “dual presence.” We calculated the number and percentage of block groups and reproductive-aged (15-49 years) women living in each zone. We calculated driving distances and drive times from abortion facilities to the nearest CPC and mapped abortion facilities with CPCs in close proximity. All analyses were conducted nationally and by region, division, and state. Results Nationally, the ratio of CPCs to abortion facilities was 3.4, and 54.9% (131,410/239,462) of block groups were categorized in the “dual presence” zone, 26.6% (63,679/239,462) as “CPC only,” and 0.8% (63,679/239,462) as “abortion facility only.” Most reproductive-aged women (45,150,110/75,582,028, 59.7%) lived in a “dual presence” zone, 26.1% (19,696,572/75,582,028) in a “CPC only” zone, and 0.8% (625,403/75,582,028) in an “abortion facility only” zone. The number of block groups and women classified as living in each zone varied by region, division, and state. Nationally, the median distance from abortion facilities to the nearest CPC was 2 miles, and the median drive time was 5.5 minutes. Minimum drive times were <1 minute in all but 11 states. The percentages of abortion facilities with a CPC within 0.25, 0.5, 1, and 3 miles were 14.1% (107/757), 22.6% (171/757), 36.1% (273/757), and 66.3% (502/757), respectively. Conclusions The findings suggest that CPCs’ tactic of locating near abortion facilities was largely realized before the 2022 US Supreme Court decision that overturned the federal right to abortion. Research on CPCs’ locations and tactics should continue given the dynamic abortion policy landscape and risks posed by CPCs. Tailored programming to raise awareness about CPCs and help people identify and access safe sources of health care may mitigate harm. Increased regulation of CPCs and government divestment may also mitigate CPC harms.