The integration and implications of artificial intelligence in forensic science.

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Background. In the context of the continuous shifts in the modern labour market and the escalating competitiveness among students of maritime specialties, there is an increasing demand for the training of highly qualified specialists in the maritime industry, who are able to interact effectively with international partners at a professional level. In this respect, the use of generative artificial intelligence (AI) to improve foreign language teaching methods for students of maritime subjects is especially relevant. This is fully justified under the conditions of globalisation and the digital transformation of the educational sphere. Purpose. The purpose is to determine the effectiveness of generative AI application in the process of building foreign language professional communicative competence of maritime students by modelling realistic scenarios of job interviews in shipping companies. Materials and methods. The present study adopted a comprehensive approach, integrating theoretical and empirical methods of analysis. The theoretical basis was established through a comprehensive review of normative and educational documents, with the aim of identifying the fundamental requirements for the educational process. This was complemented by a thorough examination of scientific and educational literature, as well as methodological literature, on the application of AI in education and linguodidactics, thus enabling the identification of current trends and the potential of technologies for teaching maritime students. The empirical element of the study involved modelling, describing and analysing professional interview scenarios using generative AI models, the observation of students' learning activities, and the conduction of interviews to obtain feedback on the perception and effectiveness of the new techniques. Results. The research yielded an innovative methodology for training students of maritime specialities at Sevastopol State University for professional interviews in international shipping companies. The developed three-stage training model, combining the creation of a database of questions, simulation of interviews using generative (AI) and practising skills through business games, demonstrated high efficiency in the formation of both linguistic and professional competences of students, in addition to soft skills deemed integral for a successful career in the maritime industry. The integration of generative AI within the training framework of maritime students has been found to expand the scope of opportunities for effective preparation for professional interviewing. The implementation of this methodology has been identified as a significant contributor to the modernisation of the educational process, rendering it more personalised, effective and aligned with the precise requirements of the contemporary labour market.

cancer remains a leading cause of morbidity and mortality worldwide, with nearly 20 million new cases and 9.7 million deaths reported in 2022. The increasing burden of cancer, driven by population growth and aging, necessitates innovative solutions to improve diagnosis, prognosis, and treatment outcomes. Artificial Intelligence has emerged as a transformative tool in oncology, offering significant potential in cancer detection, diagnosis, and personalized treatment strategies. This review explores the real-world applications of Artificial Intelligence in oncology, focusing on lung cancer and breast cancer, two of the most prevalent and deadly cancers globally. Artificial Intelligence-driven technologies, particularly in imaging, pathology, and genomics, have demonstrated remarkable success in enhancing early detection, diagnostic accuracy, and treatment planning. In lung cancer, Artificial Intelligence-powered imaging tools, such as deep learning models, have shown high sensitivity and specificity in detecting small pulmonary nodules, often missed by traditional methods. Similarly, in breast cancer, Artificial Intelligence has proven effective in mammography interpretation, reducing false positives and false negatives, and alleviating the workload of radiologists. Despite its promising potential, the integration of Artificial Intelligence into clinical practice faces several challenges, including issues related to data quality, algorithmic biases, and ethical considerations. The "black box" nature of many Artificial Intelligence systems poses a significant barrier to clinical acceptance, highlighting the need for explainable Artificial Intelligence to provide transparent and interpretable decision-making processes. Furthermore, the successful implementation of Artificial Intelligence in oncology requires robust regulatory frameworks and standardized protocols to ensure patient safety and data security. This review underscores the transformative potential of Artificial Intelligence in revolutionizing cancer care, emphasizing the importance of addressing key challenges to harness its full potential. By enhancing early detection, reducing diagnostic errors, and enabling personalized treatment strategies, Artificial Intelligence has the potential to significantly improve patient outcomes and reduce the global burden of cancer. However, its successful integration into clinical practice will depend on interdisciplinary collaboration, ethical considerations, and a commitment to responsible implementation.

Disclosure: H.M. Heshmati: None. N. Zameni: None.Background: Robotic surgery is quickly replacing traditional surgery performed by humans. It has significantly influenced the field of endocrine surgery. This review presents an update on the use of robotic surgery and artificial intelligence for adrenal tumors. Methods: A systematic search of literature was conducted using the search terms robotic surgery, artificial intelligence, adrenal tumors, safety, outcome, and cost. Results: Adrenal tumors are found in 10% of people who have imaging of the adrenal gland. They are typically unilateral and benign in the majority of cases. Robotic systems such as da Vinci Surgical System provide surgeons with enhanced dexterity and 3D visualization. Autonomous surgical robots can be categorized into various levels of autonomy including supervised autonomy (e.g., performance of specific tasks with human oversight), collaborative autonomy (e.g., robots working alongside human surgeons, sharing tasks and responsibilities), and fully autonomous systems (e.g., robots performing entire surgical procedures independently with human intervention when necessary). Robotic surgery allows access through tight spaces to remove adrenal tumors with small incision and minimal impact on surrounding tissues. The review of 1,162 patients who underwent adrenalectomy (robotic surgery in 747 patients, laparoscopic adrenalectomy in 415 patients) showed comparable blood loss and complications with the two techniques but patients with robotic surgery had significantly shorter hospital stay. The future of robotic surgery can be revolutionized by the integration of autonomous robots and artificial intelligence (AI), a field rapidly advancing. These innovations promise to further enhance the precision, safety, and effectiveness of surgery involving the adrenal glands. AI-powered systems provide real-time guidance and feedback to surgeons, enhancing decision-making and surgical precision. Autonomous systems can ensure consistent execution of surgical tasks, reducing variability and enhancing patient safety. AI algorithms can analyze extensive surgical data to refine techniques and predict potential complications, improving surgical outcomes. AI can customize surgical plans based on individual patient anatomy and pathology, potentially leading to better outcomes. The current high cost of these new procedures is a potential limitation for their use. Conclusion: Robotic surgery represents significant progress in minimally invasive techniques for the removal of adrenal tumors. It has improved dexterity and precision, and is associated with shorter recovery time compared to laparoscopic adrenalectomy. Collaboration between human surgeons and robotic systems can improve patient care and surgical outcomes. The precision, safety, and effectiveness of the adrenal surgery can be further enhanced by the integration of AI and robotics.Presentation: Saturday, July 12, 2025

The article comprehensively examines the profound impact of digital transformation on the processes of strategic decision-making within contemporary organizations, with a specific emphasis on the integration of artificial intelligence (AI) solutions and advanced big data analytics into managerial frameworks. The analysis delineates the principal directions of transformation in strategic management paradigms, substantiating the pivotal role played by intelligent digital instruments in strengthening organizational competitiveness, adaptability, and innovation capacity. A structured classification of essential data analytics tools employed in strategic decision support is presented, accompanied by meticulously selected practical case studies that demonstrate the successful application and tangible benefits of AI technologies across diverse sectors, including manufacturing, services, and public administration. Furthermore, the research critically assesses key obstacles to digital adaptation, such as data integrity and quality challenges, inadequate digital literacy among personnel, resistance to organizational change, and ethical dilemmas associated with algorithmic and automated decision-making processes. The proposed conceptual framework, termed the Digital Analytical Strategic Management Architecture (DASMA), encompasses multiple layers of analytics: descriptive, diagnostic, predictive, prescriptive, and cognitive. This model advocates decisionmaking logics that prioritize human-centricity, adaptability, and organizational transparency, particularly through the integration of explainable artificial intelligence and proactive strategic foresight. Notably, the DASMA model supports the iterative evolution of managerial approaches, enabling organizations to responsively navigate volatility and uncertainty in their operational environments.The results of this study make a significant contribution to the methodological foundation for the design and implementation of data-driven management strategies. The findings are intended to enhance executive-level decision-making, facilitate the operationalization of digital transformation frameworks, and foster sustainable organizational resilience and competitiveness in the face of ongoing digital disruption and complex, unpredictable market dynamics.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

This commentary examines the use of artificial intelligence (AI) in forensic science, highlighting its benefits inenhancing accuracy and efficiency across a number of forensic disciplines, including medicine, anthropology,forensics, and taphonomy. However its use, also raises concerns about privacy, data protection, bias,fairness, and the reliability of AI systems. The commentary emphasises the importance of scrutiny, standardizedprocedures, and ongoing dialogue to ensure AI is responsibly advanced in forensic science.

The effective integration of blockchain technology and artificial intelligence (AI) has the potential to change healthcare management. Two cutting-edge developments in the healthcare industry are blockchain and artificial intelligence. Blockchain, an open network for information sharing and permission, is being used in e-Health to apply artificial intelligence models. Healthcare workers will be able to view patient medical records on the blockchain. Artificial intelligence (AI) makes use of a wide range of suggested algorithms, decision-making power, and vast amounts of data. Health care services can be made more decentralized, transparent, safe, and impenetrable with the use of blockchain technology. AI needs cryptographic records to be stored, and blockchain makes this possible. Applications of artificial intelligence in healthcare management include chatbots for diagnosis and treatment, predictive analytics, personalized medicine, and more. Blockchain technology has applications in supply chain management, clinical trial management, interoperability, and data security and integrity in healthcare management. A more effective, safe, and patient-centered healthcare ecosystem can be created by integrating blockchain technology and artificial intelligence (AI) into healthcare management. It can encourage the development of a wide range of applications in radiology, cancer treatment, cardiology, dermatology, and fundoscopy that may save patients' lives.

Introduction: Maternal and child health provide critical indicators of the health status of a country. Despite gains made over the decades, challenges continue to plague health care systems, especially in some developing countries. The integration of Artificial Intelligence (AI) into midwifery practice can potentially offer innovative opportunities which can revolutionize the way care is delivered. This review aims to identify evidence of the use of AI to enhance midwifery practice thereby reducing maternal morbidity and mortality. Objective: To determine the applicability of AI in midwifery practice to reduce maternal morbidity and mortality. Methods: Ninety-one articles were obtained using key words artificial intelligence, artificial intelligence in midwifery practice, maternal morbidity and mortality, midwifery practice. Sources of data include Google Scholar and EBSCO host. A total of eight (23) articles met inclusion criteria using key terms. Results: After inclusion/exclusion criteria was applied, 23 articles were selected. Seven (30. 4% addressed AI in midwifery, 7 (30.4 %) health care, 4 (17.3 %) use of AI in medicine, 2 (9%) AI, 1 (4.3) deep learning, I (4.3%) International Federation of Midwives and 1 (4.3%) World Health Organization Fact Sheet. Conclusion: The literature is sparse with evidence of the application of AI in midwifery practice to aid in the reduction of maternal morbidity and mortality as well as its application in low-resourced countries of the Caribbean region. Based on the use of artificial intelligence by other professions such as medicine to identify and prevent maternal morbidity, its use in by midwives is likely to enhance midwifery practice.

Digital revolution and the resultant emergence of Industry 4.0 has driven the incorporation of Artificial intelligence (AI) in Industrial education to enhance skills development in the industry. However, there is a lack of adequate empirical evidence on the integration of Artificial intelligence in industrial education. To fill this gap, this study reviewed previous studies on the adoption of AI in Industrial education and examined the frequency of occurrence of variables obtained in the studies reviewed. Relevant literature was screened and reviewed to find empirical evidence to support findings. A systematic review of 14 studies provided insights into the current applications, benefit and challenges of AI integration in industrial education. The study found that the most cited applications of AI is Adaptive and personalised learning systems, which customise workers/learners’ information based on their interaction with learning content. Other applications are augmented simulators for real-time feedback, virtual mentors, and intelligent tutoring systems which replicate real-life interaction with professionals among others. Majority of the studies found increased engagement and improved learning outcomes and skills development as benefit of AI integration in Industrial education. Other benefits are promotion of early identification of learning challenges and timely intervention and feedback, improvement in administrative efficiency and support, personalisation of learning. Notable challenges were skills and capacity gaps, lack of infrastructure and AI resources, curriculum issues and difficulty in integrating AI into current curriculum, ethical and privacy concerns among others. Based on the findings of the study, it was recommended that the skill gap should be filled with training in AI applications and use, investment in AI infrastructural development should be explored, industry collaboration and partnership in the area of needs should be considered, AI marketing and literacy should be adopted in industries, all AI intervention should be a continuing and lifelong process to ensure sustainability.

Revolutionizing clinical laboratories: The impact of artificial intelligence in diagnostics and patient care.

A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff.

Abstract: The drug discovery landscape and neurodegenerative disease diagnostics are expe-riencing a paradigm shift with the integration of artificial intelligence (AI) and machine learn-ing (ML) technologies. This review provides an elaborate discussion of AI-based approaches across all stages of computer-aided drug design (CADD), including virtual screening, peptide synthesis, pharmacophore modelling, quantitative structure–activity relationships (QSAR), and drug repurposing. Particular emphasis is placed on Alzheimer's disease (AD), a multifac-eted neurodegenerative disorder, with AI enabling early diagnosis, patient stratification, and subtype classification to support precision medicine. The review highlights recent developments in supervised and unsupervised learning, omics integration, and the increasing importance of explainable AI (XAI) in addressing the “black box” limitations of traditional AI models. We categorize and assess major AI platforms and tools by their scope, stage of implementation, and form of explainability, offering a practical framework for their application in pharmaceutical and clinical research. The combined use of deep learning, quantum simulation, and virtual reality is also discussed in the context of de novo drug design, compound screening, and toxicity prediction. With con-tinued advances in AI, interdisciplinary collaboration and ethical oversight are essential to translate these digital innovations into effective, safe, and personalized therapies. This review serves as an all-encompassing guide for researchers, clinicians, and developers working at the interface of AI and drug development, particularly those addressing the unmet challenges in Alzheimer's disease. As outlined in the abstract and reflected in the article title, this review bridges AI-driven drug discovery and Alzheimer’s diagnosis, with specific empha-sis on explainable models for next-generation therapeutics. The subsequent sections expand on these themes, offering a structured synthesis of tools, applications, and methodological frameworks.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.