AI in Education: A Luxury or a Necessity for Developing Nations?

The article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.

The objective of this investigation is to determine the opportunities and challenges that secondary schools in Babylon, Iraq, encounter when utilizing artificial intelligence (AI) technologies. The study adopted a qualitative and quantitative method and also explored the infrastructure and existing technological tools in schools using documents from education management. Thematic analysis is used to find, assess, and report recurring patterns and key themes in interview transcripts to gain a comprehensive understanding of participants’ perspectives. Stakeholders are very excited about using AI in their schools and are in favor of the idea of switching to AI, even though there are some challenges that need to be processed, such as poor infrastructure, communication issues, and a lack of training programs on how to use AI technologies and applications. This paper establishes that the potential for the fundamental transformation of conventional teaching and learning methods exists through the integration of AI and mobile technologies into education. AI can provide customized educational experiences, optimize administrative functions, improve feedback systems, and facilitate comprehensive data analysis. Nevertheless, it is imperative to confront obstacles such as those related to guaranteeing equity, tackling ethical and safety issues, and fostering students’ self-learning capabilities. This study suggests that educators need to attain a balance between technological progress and new political, moral, and other issues, as well as more support for the education sector to improve electronic infrastructure. Additionally, this study contributes to the emerging literature on the implementation of AI in Iraq’s public education through a set of recommendations to offer valuable information for educational institutions, policymakers in strategies, researchers, and AI developers.

Artificial intelligence (AI) technologies have spread throughout the world and changed the way that many social functions are conducted, including health care. Future large-scale combat missions will likely require health care professionals to utilize AI tools among other tools in providing care for the Warfighter. Despite the need for an AI-capable health care force, medical education lacks an integration of medical AI knowledge. The purpose of this manuscript was to review ways that military health care education can be improved with an understanding of and using AI technologies. This article is a review of the literature regarding the integration of AI technologies in medicine and medical education. We do provide examples of quotes and images from a larger USU study on a Faculty Development program centered on learning about AI technologies in health care education. The study is not complete and is not the focus of this article, but was approved by the USU IRB. Effective integration of AI technologies in military health care education requires military health care educators that are willing to learn how to safely, effectively, and ethically use AI technologies in their own administrative, educational, research, and clinical roles. Together with health care trainees, these faculties can help to build and co-create AI-integrated curricula that will accelerate and enhance the military health care curriculum of tomorrow. Trainees can begin to use generative AI tools, like large language models, to begin to develop their skills and practice the art of generating high-quality AI tools that will improve their studies and prepare them to improve military health care. Integration of AI technologies in the military health care environment requires close military-industry collaborations with AI and security experts to ensure personal and health care information security. Through secure cloud computing, blockchain technologies, and Application Programming Interfaces, among other technologies, military health care facilities and systems can safely integrate AI technologies to enhance patient care, clinical research, and health care education. AI technologies are not a dream of the future, they are here, and they are being integrated and implemented in military health care systems. To best prepare the military health care professionals of the future for the reality of medical AI, we must reform military health care education through a combined effort of faculty, students, and industry partners.

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.

This study examines AI's role in enhancing guest satisfaction and efficiency in the hotel industry. Employing a mixedmethods approach, it analyzes guest feedback and interviews staff at AI-integrated hotels. The findings aim to identify key AI applications that boost satisfaction and efficiency, and outline challenges and best practices for AI implementation. This research offers a holistic view of AI's influence on hospitality, enriching understanding and guiding industry practices. As the hotel industry continues to evolve, the integration of artificial intelligence (AI) technologies has become increasingly prevalent, aiming to enhance guest experience. This research investigates the impact of AI integration on guest experience enhancement within the hotel industry. The purpose of this study is to comprehensively explore how AI technologies influence various aspects of guest satisfaction in hotels. A mixed-methods approach is employed, combining quantitative analysis of guest feedback data with qualitative methods by interviewing the guests staying in the hotel. Data is collected from a diverse range of hotels that have implemented AI technologies, allowing for a nuanced understanding of the impacts across their establishments. This research is expected to provide valuable insights into the multifaceted effects of AI integration in the hotel industry. Specifically, it aims to identify the specific AI applications that most significantly contribute to guest satisfaction levels. Additionally, the study seeks to uncover potential challenges and limitations associated with AI implementation, as well as best practices for successful integration. This topic lies in its comprehensive examination of AI's impact on both guest experience within the hotel industry. While previous research has explored AI's role in hospitality, few studies have undertaken such a holistic analysis, considering its implications for guests. By addressing this gap, this research contributes to a deeper understanding of the transformative effects of AI in the hotel sector, providing practical insights for industry practitioners and stakeholders.

Relevance. The development of artificial intelligence (AI) as a societal foundation has become crucial for leading economies, who view it as a key driver of national competitiveness and security. In an era defined by rapid technological advancement and industrial transformation, nations strive to lead in the international science and technology arena, taking advantage of AI to address challenges across sectors such as agriculture, astronomy, and cybersecurity. AI’s role in enhancing productivity, sustainability, and security highlights its strategic importance, underscoring the urgency for countries to actively pursue AI development to secure a competitive edge in a globalised world. Methodology. The study employs a multi-faceted methodological approach. First, a comprehensive literature review and analysis of AI applications in various sectors, including agriculture, astronomy, and cybersecurity, is conducted to provide context on current advancements and trends. Secondly, a comparative analysis examines the strategic AI policies of leading nations to assess how different countries are positioning AI within their national agendas. Third, case studies of AI implementation in specific sectors, such as precision agriculture and cybersecurity, illustrate the practical impacts and potential benefits of a society-oriented approach to AI. The aim of this study is to analyse the strategic value of fostering an AI-driven society as a means of enhancing national competitiveness and securing leadership in international technological innovation. It aims to explore how AI can be harnessed to support sustainable development, improve sectoral efficiency, and protect against security threats, thus contributing to the overall socio-economic resilience and global standing of a nation. Results. The study reveals that the integration of AI across diverse sectors has led to significant efficiency gains, particularly in resource management, sustainability, and security. AI-driven advancements in agriculture, such as precision farming, contribute to higher productivity and environmental sustainability, while applications in astronomy support large-scale data processing for deep space exploration. In cybersecurity, AI has proven instrumental in identifying and countering cyber threats in real time. These findings confirm that an AI-centric societal model can enhance national resilience, drive economic stability, and bolster a country’s competitive position on the global stage. Conclusion. The emergence and development of the “artificial intelligence society” in the context of the technological transformation of the world is a process in which societies adapt to the profound changes caused by the introduction and development of artificial intelligence (AI) technologies. This process includes several key aspects: economic change, based on the automation of production processes, leading to increased efficiency and productivity; the creation of new markets and business models based on AI capabilities, including the redistribution of jobs and changes in employee skill requirements; social change, which is based on changing the way people interact with each other and with technology; transition to smart cities and communities, where AI helps to manage resources and ensure the comfort of life; impact on education, health and other areas of life through the introduction of personalised AI-based solutions; cultural changes aimed at transforming the values and worldview associated with AI technologies; emergence of new cultural practices and media formats based on AI; development of digital culture and its impact on traditional cultural forms; political and ethical challenges, including defining new regulatory and legal frameworks for AI regulation; ensuring ethical use of AI, avoiding discrimination and ensuring fairness; managing risks related to data security and privacy; technological development, based on the continuous improvement of AI algorithms and models; integration of AI into various sectors of the economy and everyday life’; development of infrastructure to support the large-scale implementation of AI (e.g. 5G networks, data centres); This process reflects the overall technological transformation of the world, where AI is becoming an integral part of economic, social, cultural, and political life. Key words: artificial intelligence, philosophy of society, national competitiveness, strategy.

The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers

This paper presents a comprehensive review of the impact of Artificial Intelligence (AI) on recruitment and selection processes within the oil and gas industry. The primary objective is to understand how AI technologies are transforming traditional recruitment methodologies and the implications of these changes for both employers and candidates. The methodology involves a systematic analysis of existing literature, case studies, and industry reports to identify key trends, opportunities, and challenges associated with the integration of AI in recruitment processes. The findings reveal that AI significantly enhances the efficiency and effectiveness of recruitment in the oil and gas sector by automating routine tasks, improving candidate targeting, and facilitating data-driven decision-making. AI-driven tools such as resume screening algorithms, predictive analytics, and virtual assistants are increasingly being adopted to streamline the recruitment process, reduce biases, and improve the quality of hires. However, the study also identifies potential challenges, including ethical concerns, the need for transparency in AI algorithms, and the risk of over-reliance on technology. The paper concludes that while AI presents substantial benefits in optimizing recruitment and selection processes, it is crucial for companies in the oil and gas industry to approach its implementation thoughtfully. This involves balancing technological advancements with human judgment, ensuring ethical use of AI, and continuously updating AI systems to adapt to the dynamic nature of the job market. The paper suggests that the future of recruitment in this industry will likely be a hybrid model that leverages the strengths of both AI and human expertise. Keywords: Artificial Intelligence, Recruitment, Oil and Gas Industry, Talent Acquisition, Machine Learning, Natural Language Processing, Predictive Analytics.

Purpose This study examines how secondary school administrators can lead ethical artificial intelligence (AI) integration within environments demanding technological innovation and educational value preservation. Design/methodology/approach The study conducted a scoping review of literature (2018–2025) to analyze administrative functions across four established leadership dimensions: instructional, managerial, strategic, and relational. Sources were obtained from academic databases and grey literature, with 21 sources selected based on relevance to secondary education and administrative practice. Analysis is grounded in foundational leadership scholarship while examining contemporary AI integration challenges. Findings The analysis reveals a misalignment between AI's most frequent use (relational leadership functions) and where it may be most appropriately suited (managerial and strategic functions). AI integration creates distinct opportunities and risks across each leadership dimension, with equity concerns emerging consistently. Communication represents the primary AI use, despite being the most fundamentally human aspect of educational leadership. Cognitive offloading risks emerge when administrators delegate critical thinking tasks to AI systems, potentially attenuating leadership capabilities essential for educational effectiveness. Research limitations/implications This study relies on secondary data collection and English-language sources, creating Western-centric bias and limiting generalizability beyond North American contexts. The corpus of 21 sources reflects the nascent research state in this emerging field. The rapid evolution of AI capabilities means current findings may prove transitional as technology advances. Future empirical research should examine long-term cognitive effects of AI reliance on administrators, stakeholder trust implications when AI-mediated communications are detected, differential equity impacts across diverse school communities, cross-cultural implementation patterns, and effectiveness of hybrid governance approaches for AI integration in educational leadership. Practical implications Findings support implementing hybrid governance models that combine regulatory oversight with participatory decision-making between administrators and stakeholders. Professional development programs must balance AI literacy training with preserving human capabilities essential for authentic educational leadership. Administrator preparation programs require redesign to address cognitive offloading risks while maintaining relationship-building and cultural competence development. Educational leaders should prioritize AI applications in managerial and strategic functions while preserving human judgment in relational leadership contexts. Policy frameworks must address equity concerns and provide guidance for schools serving vulnerable populations who currently receive less AI implementation support. Social implications AI implementation without critical examination risks amplifying existing educational inequities, particularly affecting Indigenous, newcomer, and racialized communities. Democratic participation in AI boundary-setting becomes essential for maintaining institutional trust and stakeholder engagement. The misalignment between AI deployment and appropriate applications threatens the relational foundations of effective educational leadership. Originality/value The study provides the first systematic examination of AI integration across established educational leadership dimensions in secondary school contexts, addressing a critical research gap given that nearly 60% of K-12 principals use AI tools while fewer than 10% of schools have established AI policies.

n the context of total digitalization of education, problems connected with the implementation of artificial intelligence (AI) technologies, as well as issues related to the role of a person as a carrier of human intelligence (HI) in the educational process, become especially topical for universities. The increasing penetration of AI into many areas of higher education requires a balanced approach, considering all the pros and cons, assessing the risks and consequences for humans as subjects of education. The goal of the article is to compare AI and HI capabilities in the educational process of a university. The authors set and solved the following tasks: 1) to study the theoretical material and practical experience of Russian universities in the aspect of applying AI and HI in higher education; 2) to study the concepts of “artificial intelligence” and “human intelligence”; 3) to identify the advantages and disadvantages of AI and HI for the educational process at a university; 4) to present the comparative description of AI and HI in the educational process of a university; 5) to define the perspectives for the development of AI and HI in higher education. To solve the tasks the methods of analysis of scientific literature, analysis of regulatory documents, study and generalization of advanced pedagogical experience, and comparative analysis are used. The results indicate that the issues of introducing AI technologies into the educational process of universities are topical for the state and society. In theory, there is no consensus on the definition of the AI phenomenon, while HI is interpreted as the sum of a person’s cognitive abilities. The review of practice shows a wide range of applicability of AI technologies in universities: from automating routine tasks to creating an immersive educational environment. The comparative analysis of the capabilities of AI and HI has been carried out according to the following parameters: processing and analysis of big data, adaptive learning, interactivity, feedback, creative thinking and critical thinking, complexity of tasks, ethics and morality, emotional communication, objectivity of assessment. The analysis shows that AI and HI in relation to higher education have advantages and disadvantages. A promising scenario for the future development of the system of higher education under the influence of AI technologies will be the integration of AI and HI, which opens up new opportunities for humans in Education 4.0.

Background Artificial Intelligence (AI) is emerging as a pivotal technology with vast promises for healthcare. However, integrating AI into clinical and public health settings must be cautiously approached to ensure that it does not inadvertently exacerbate existing health system problems. The healthcare workers, already under severe stress, could view AI as a threat to job security rather than as a support mechanism. Moreover, past experiences with digital transformations, such as Electronic Health Records, have shown that technological integration can sometimes increase rather than decrease the burden on healthcare workers, leading to burnout and dissatisfaction, and thus worsening the healthcare workforce crisis. Furthermore, equity and ethical considerations are paramount in the deployment of AI in healthcare. Data privacy, patient consent, and algorithmic bias must be addressed to ensure that AI applications are designed to support and enhance human decision-making that is sensitive to the social determinants of health and accountable to equity and social inclusion, to the needs and rights of the healthcare workforce, and the dignity of the patients and populations. While AI presents significant opportunities for health systems and healthcare workers, there is a lack of knowledge, evidence-based policies, and ethical frameworks that support equitable and human-centred approaches to AI implementation. Objectives This round table workshop aligns three major public health challenges: the integration of AI in health systems, the global healthcare workforce crisis, and the improvement of equity and equality. It critically explores capacity building for AI, equity in AI implementation, and regulatory measures for ethical and responsible AI deployment, addressing the following major questions: What are the critical impacts of AI on the healthcare workforce? How can the healthcare workforce be effectively upskilled and supported to adapt to the changes brought by AI technologies? What regulatory frameworks and governance models are necessary to ensure AI’s safe and ethical implementation in healthcare, that is also sensitive to equity, gender equality and the needs of minority groups? Finally, what actionable steps and leadership can public health take to implement AI technologies while addressing the healthcare workforce needs, equity issues, and ethical guidelines? The panellists will illuminate these questions from different disciplinary approaches, helping us to disentangle complexity and to build capacity for evidence-based and socially inclusive AI policies. The workshop contributes to better understand the risks and benefits of AI. It seeks to advance knowledge exchange of good practice experiences and effective implementation. Key messages • The effects of AI on the healthcare workforce must be monitored and strategies adapted to mitigate the healthcare workforce crisis and to upskill and empower healthcare workers. • There is a need for human-centred and ethically responsive AI implementation and governance measures that support equity, gender equality, and diversity in healthcare settings. Speakers/Panelists Abi Sriharan Schulich School of Business York University, York, Canada Kasia Czabanowska Maastricht University, Maastricht, Netherlands Bernadette Kumar Migration Health Unit, Norwegian Institute of Public Health, Oslo, Norway Marius-Ionuț Ungureanu Babeș-Bolyai University, Cluj-Napoca, Romania Farhang Tahzib Faculty of Public Health, Haywards heath, UK

PurposeIn the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.Design/methodology/approachTo explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.FindingsThe investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.Originality/valueThis study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Introduction Health systems face escalating demands from growing population needs amidst workforce shortages of 1.2 million doctors, nurses and midwives. Artificial intelligence (AI) has emerged as a pivotal tool in transforming primary care. By aiding in clinical decision-making, automating routine tasks, and predicting patient needs, AI can bolster the efforts of health professionals and enhance patient outcomes. This workshop seeks to examine AI applications within primary care and explore their implications for healthcare providers, focusing on their integration strategies and addressing their impact on the skills of health professionals. Particular emphasis will be placed on AI applications managing non-communicable diseases such as cancer, cardiovascular diseases, chronic respiratory diseases and diabetes, which represent significant health burdens. Methods The workshop will feature concise pitch presentations by leading AI experts and healthcare practitioners from across Europe, highlighting AI technologies and their potential applications in primary care, with a focus on managing non-communicable diseases. These presentations and the following engaging panel discussion will showcase successful AI implementations, their practical applications in primary care settings and expected benefits for patient outcomes. The discussion will also consider the skills that healthcare professionals will need as AI becomes more integrated into their work, discussing strategies for continuous education and training to adapt to these technological advancements. In an interactive format, attendees will propose questions and identify valuable AI integration areas, with audience input guiding the discussion and encouraging a critical examination of AI's role in primary care. This will be done through live questions, voting on dilemmas, submitting real-life cases or proposing “what if” scenarios. Discussion and Conclusions This workshop will provide participants with insights into AI's transformative potential in primary care, particularly for managing non-communicable diseases. Attendees will gain insights from practitioners who have successfully integrated AI solutions, understanding the benefits and practical strategies for AI implementation, focusing on a human-centered approach and responsible innovation. With continuous education and training emphasised, participants will be equipped with knowledge to leverage AI effectively, enhancing workforce efficiency. By integrating diverse European experiences, the workshop will enrich the discussion, providing a comprehensive understanding and actionable strategies for AI integration in primary care systems. By facilitating a broader adoption of AI technologies, healthcare professionals can help transform care delivery, ensuring that primary care systems are resilient, efficient, and equipped to meet the needs of future patient populations such as the high burden of non-communicable diseases. Key messages • AI empowers primary care through task automation, risk prediction and decision support in prevention, diagnosis and treatment, crucial for managing growing patient needs and workforce challenges. • Emerging AI applications can provide key insights on the required skills development needs and integration strategies of AI in primary care to address non-communicable diseases. Speakers/Panellists Tino Marti Department of Health. Government of Catalonia, Barcelon, Spain Artin Entezarjou Tandem Health, Stockholm, Sweden Gregor Stiglic University of Maribor, Maribor, Slovenia Eric Sutherland OECD, Paris, France Federicaa Margheri European Health Management Association, Brussels, Belgium

Purpose This paper aims to explore the paradigm shift in leadership and strategic management driven by the integration of responsible artificial intelligence (AI) in healthcare. It explores the evolving role of leadership in adapting to AI technologies while ensuring ethical governance, transparency and accountability in healthcare decision-making. Design/methodology/approach This study conducts a comprehensive review of current literature, case studies and industry reports to evaluate the implications of responsible AI adoption in healthcare leadership. It focuses on key areas such as AI-driven decision-making, resource optimisation, crisis management and patient care, while also addressing challenges in integrating AI technologies effectively. Findings The integration of AI in healthcare is transforming leadership from traditional, experience-based decision-making to data-driven, AI-enhanced strategies. Responsible leadership emphasises addressing ethical concerns such as bias, transparency and accountability. AI technologies improve resource allocation, crisis management and patient care, but challenges such as workforce resistance and the need for upskilling healthcare professionals remain. Practical implications Healthcare leaders must adopt a responsible leadership framework that balances AI’s potential with ethical and human-centred care principles. Recommendations include developing AI literacy programmes for healthcare professionals, ensuring inclusivity in AI algorithms and establishing governance policies that promote transparency and accountability in AI applications. Originality/value This paper provides a critical, forward-looking perspective on how responsible AI can drive a paradigm shift in healthcare leadership. It offers novel insights into the integration of AI within healthcare organisations, emphasising the need for leadership that prioritises ethical AI usage and promotes patient well-being in a rapidly evolving digital landscape.

The dawn of artificial intelligence (AI) signifies a pivotal shift in medical and dental education. Integrating AI into the curriculum modernizes learning and equips future healthcare professionals with crucial tools for the 21st century. The COVID-19 pandemic revealed the limitations of conventional educational models, necessitating rapid adaptation to remote and online learning environments. This disruption expedited the transition to digital platforms, laying the foundation for further integration of technology, including AI, into medical education. What began as an emergency response has now become a permanent feature of the educational landscape, evolving from static textbooks to dynamic digital platforms that offer greater accessibility, inclusivity, and personalization of learning experiences.1 In the AI era, it is insufficient to merely digitize the curriculum; a comprehensive transformation is essential. The digital curriculum opens new avenues for interactive learning environments, simulation-based practices, and adaptive learning algorithms that respond to the individual needs of students. AI-driven tools such as virtual patient simulations, diagnostic decision-making platforms, and predictive analytics have the potential to revolutionize how medical students learn, practice, and apply their knowledge in clinical settings.2 These innovations allow for an enhanced learning experience where students can interact with realistic patient cases and make informed decisions, fostering a deeper understanding of clinical practice. One of the most promising applications of AI in medical education is its role as an educational partner. AI-powered platforms can function as personalized tutors, providing real-time feedback, adjusting learning modules based on student performance, and even predicting areas where additional support may be required.3 Adaptive learning systems can analyze the learner’s pace and comprehension, offering tailored resources to bridge knowledge gaps. This personalized approach to education ensures that no student is left behind, addressing one of the longstanding challenges of traditional, one-size-fits-all curricula. Additionally, AI can enhance clinical reasoning through simulation and data-driven case scenarios. By analyzing patterns in patient data, AI algorithms can help medical students gain deeper insights into complex clinical decision-making processes. This data-driven approach can significantly improve learners’ ability to diagnose and plan treatments, thereby improving clinical outcomes. While AI and digital tools offer substantial benefits, the role of educators remains essential in this new educational paradigm. Rather than replacing teachers, AI will augment their roles, allowing them to focus on mentorship, critical thinking, and the ethical dimensions of healthcare.4 Educators will need to reimagine their roles, becoming facilitators of learning who guide students in interpreting and applying AI-generated data in clinical settings. As AI takes on administrative tasks such as grading, educators can dedicate more time to meaningful interactions with students.5 However, this shift toward AI-driven curricula also requires significant investment in faculty development. Educators must be trained in the use of AI tools and possess a thorough understanding of their applications to ensure that AI is used responsibly and effectively in shaping future healthcare professionals. As AI becomes more integrated into medical education, addressing the ethical challenges associated with this technology becomes crucial. While AI-driven tools hold great promise, they must be designed and deployed with an acute awareness of biases, data privacy concerns, and the risk of over-reliance on algorithms in clinical decision-making.6 The digital curriculum must provide students with technical skills and a strong ethical foundation for AI use in healthcare. Students must be trained to critically evaluate AI outputs, understand their limitations, and ensure that human judgment remains central to patient care. Transforming medical curricula in the AI era is not without challenges. Digital divides, access to technology, and the initial cost of AI-driven platforms may pose barriers to widespread adoption. Institutions must ensure equitable access to resources for all students, regardless of their geographic or socioeconomic backgrounds. Moreover, regulatory bodies such as the Higher Education Commission (HEC) and the Pakistan Medical and Dental Council (PMDC) must revise standards to accommodate these technological advancements. In conclusion, the transformation of medical and dental curricula into a digital, AI-enhanced model represents not only a modernization of education but also a fundamental shift in preparing future healthcare professionals. By embracing AI as an educational partner, medical institutions can create personalized, data-driven learning environments that equip students with the skills and knowledge needed to thrive in an increasingly complex healthcare landscape. The integration of AI into the curriculum offers an opportunity to empower the next generation of doctors, enabling them to navigate future challenges with confidence and competence. Now is the time for this transformation, and it is a journey that we must embark on collectively to ensure the future of education, healthcare, and patient care.