Multimodal and cognitive approaches to academic discourse in AI-supported learning

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.

Artificial intelligence to complement rather than replace radiologists in breast screening

Early detection of clusters of health conditions is essential to proactive clinical and public health interventions. Effective intervention strategies require real-time insights into the health needs of the communities. Artificial Intelligence (AI) systems have emerged as a promising avenue to detect patterns in health indicators at an individual and population level. The purpose of this paper is to describe the novel expanded application of AI to detect clusters in health conditions and community health needs to facilitate real-time intervention and prevention strategies. Case-use examples demonstrate the capabilities of AI to harness a variety of data to improve health outcomes in conditions ranging from infectious diseases, non-communicable diseases, and mental health disorders. AI systems have been utilized in syndromic surveillance to detect cases of infectious diseases prior to laboratory-confirmed diagnosis. These AI systems can analyze data from healthcare facilities, laboratories, and online self-reported symptoms to detect potential outbreaks and facilitate timely vaccination, resource allocation and public health messaging to mitigate the spread of disease. Similarly, the spread of vector-borne diseases can be anticipated through the analysis of historical data, weather reports and incidence of disease to identify areas to deploy vector control measures. In the area of mental health, AI algorithms can analyze diverse data sources such as social media posts, emergency hotline calls, emergency department visits, and hospital admissions to identify clusters related to mental health issues including overdoses, suicides, and burnout. The timely detection of such clusters enables prompt intervention, facilitating deployment of targeted mental health support services and community outreach programs to address these issues in a targeted and proactive manner. Identifying trends and characteristics in chronic disease data can guide screening and intervention strategies in real time. Similarly, AI can enhance pharmacovigilance by identifying previously unknown patterns in adverse drug reactions to inform regulatory bodies, healthcare providers and researchers in efforts to provide data-driven, real-time patient safeguards. By harnessing data from air-quality monitors, health records, and meteorology reports, AI systems identify correlations between environmental factors and health issues to empower efforts to address specific environmental health risks. These case-use examples illustrate the potential for AI to serve as a valuable tool to facilitate real-time, data-driven insights to inform proactive clinical and public health intervention strategies. Ongoing challenges in harnessing AI technology for public health surveillance include data privacy, accessing quality data from diverse data sets, and establishing effective communication channels between AI systems and public health authorities. The use of anonymized data to detect clusters and identify the health needs of health regions is a potential strategy to mitigate these challenges. Available resources are limited and must be deployed in a targeted, informed, and timely manner to be most effective. The integration of AI into an expanded all-risks approach to syndromic surveillance represents the next step in identifying and responding to clusters of health-related events in a proactive manner that aligns with community needs while upholding ethical standards and privacy considerations.

Chapter 10 - Human-machine interaction: AI-assisted medicine, instead of AI-driven medicine

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

The integration of Artificial Intelligence (AI) systems into Earth Observation (EO) research and innovation has catalyzed significant advancements in environmental monitoring, humanitarian response, and urban planning. However, these developments also raise novel regulatory and ethical challenges, particularly in light of the European Union’s Artificial Intelligence Act (EU AI Act), which introduces a tiered risk-based framework for the governance of AI systems. This paper provides the first comprehensive examination of how the EU AI Act, and its provisions concerning high-risk AI systems as delineated in Annex III, apply to EO-based applications. Through a structured analysis of EO use cases across key domains, such as access to public and private services, law enforcement, critical infrastructure, migration, and biometric surveillance, we illustrate how the same EO AI system may be variably classified depending on its intended purpose, autonomy level, and deployment context. We demonstrate that while many current EO AI systems are not yet autonomous enough to trigger high-risk classification, the rapid technological trajectory suggests an increasing prevalence of high-risk EO applications in the near future. Furthermore, we argue that EO researchers and developers must proactively engage with the regulatory demands of the EU AI Act, not merely to ensure compliance, but to contribute to the development of methodological tools, such as explainability, risk assessment, and auditability, that are essential for ensuring responsible AI innovation. By linking legal interpretation with technical and ethical considerations, this paper contributes to an emerging interdisciplinary framework for governing AI in the EO domain under conditions of legal uncertainty and accelerating innovation.

As Artificial Intelligence (AI) systems become more widespread, there is a growing need for transparency to ensure human understanding and oversight. This is where Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations is still an open research problem. Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. Essential techniques include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods while Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. To ensure that explanations are tailored for diverse users, contexts, and AI applications, HCI principles and participatory design approaches can be utilized. Therefore, this article concludes with recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI. As Artificial Intelligence (AI) systems become more common in our daily lives, the need for transparency in these systems is becoming increasingly important. Ensuring that humans clearly understand how AI systems work and can oversee their functioning is crucial. This is where the concept of Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations for AI systems is still an open research problem. In this context, Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. By integrating HCI principles, we can create systems humans understand and operate more efficiently. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. The essential methods identified include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods. Each of these techniques has unique advantages and can be used to provide explanations for different types of AI systems. While Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. There is a risk of oversimplification, leading to misunderstanding or mistrust of the AI system. It is essential to employ HCI principles and participatory design approaches to ensure that explanations are tailored for diverse users, contexts, and AI applications. By developing human-centred XAI systems, we can ensure that AI systems are transparent, interpretable, and trustworthy. This can be achieved through interdisciplinary collaboration between HCI and AI. The recommendations in this article provide a starting point for designing such systems. In essence, XAI presents a significant opportunity to improve the transparency of AI systems, but it requires careful design and implementation to be effective.

Is the future of medical diagnosis in computer algorithms?

The integration of artificial intelligence (AI) and learning management systems (LMS) is revolutionising higher education, offering unprecedented opportunities for personalised learning, adaptive assessments, and data-driven decision-making. This review investigates the impact of AI–LMS integration on educational quality, student success, and institutional performance in higher education. In addition, this review not only examines the technological integration but also evaluates how AI–LMS systems contribute to sustainable development in higher education through reduced resource consumption, improved accessibility, and enhanced educational equity. Following the PRISMA 2020 guidelines, a comprehensive search of the Scopus database yielded 60 relevant studies published between 2014 and 2023. The review reveals significant benefits of AI–LMS integration, including enhanced student engagement, personalised learning paths, and improved learning outcomes. Key applications include AI-powered conversational agents, adaptive assessments, and learning analytics. However, challenges such as data privacy concerns, algorithmic bias, and the need for faculty training were also identified. The findings highlight strategies for effective AI–LMS implementation, emphasising the importance of ethical considerations and addressing the digital divide. Results demonstrate that AI–LMS integration can significantly enhance educational quality and student performance when implemented thoughtfully. The review also uncovers areas requiring further research, including long-term impacts on learning outcomes, scalability of AI–LMS solutions, and strategies for ensuring equitable access. Future studies should focus on longitudinal assessments of AI–LMS effectiveness, the development of ethical frameworks for AI in education, and the exploration of AI–LMS applications in diverse educational contexts. This review provides valuable insights for higher education institutions seeking to leverage AI–LMS integration to transform teaching and learning practices.

<div class="section abstract"><div class="htmlview paragraph">The integration of ergonomics and artificial intelligence (AI) in the automotive industry has the potential to revolutionize the way how vehicles are designed, manufactured and used. The aim of this article is to review the recent literature on the subject and discuss the opportunities and challenges presented by the integration of these two fields. The paper begins defining the ergonomics and the AI and providing an overview of their respective roles in the automotive industry. It then examines the benefits of the integration of ergonomics and AI in the automotive industry, including the optimization of vehicle design and manufacturing process. The enhancement of the driver experience, and improvement of safety accessibility, and customization, however, the integration of ergonomics and AI in the automotive industry also presents challenges, including ethical and legal considerations, data privacy, liability, and the impact on the employment in the automotive industry. The paper reviews research on these challenges and suggests that the development of international standards for the integration of AI in the vehicles may be necessary to ensure that AI systems in vehicle are secure, highlighting the need for future research to explore the integration of ergonomic and AI in the automotive industry. Future research should focus and addressing the ethical, legal, and societal implications of the AI in vehicles, as well as exploring new opportunities for the use of AI in design, manufacturing, and use of vehicles in overall, the integration of ergonomics and AI in the automotive industry has the potential to significantly improve the design and manufacturing of vehicles, as well as enhance the driving experience for users. However, the integration of these two fields also poses challenges that must be addressed, including ethical concerns, legal considerations, and the employment in the automotive industry. By working to overcome these challenges, we ensure that benefits of ergonomics and AI in the automotive industry are fully realized while minimizing their potential negative impacts.</div></div>

e13650 Background: To evaluate and compare the diagnostic performance of a deep learning-based artificial intelligence (AI) system versus three radiologists in the detection of breast cancer using digital mammography, specifically within the context of Uzbekistan, and to determine if AI can serve as a reliable tool in this setting. Methods: This retrospective study utilized a dataset of mammograms, sourced from Uzbekistan, which were independently assessed by three radiologists and an AI system. The AI model, based on deep neural networks, was designed for automated breast cancer detection. The radiologists’ interpretations and the AI predictions were compared against a reference standard of biopsy results. The primary outcome measures included the area under the receiver operating characteristic curve (AUC), accuracy, and specificity for both the AI system and radiologists. The data underwent rigorous statistical analysis to establish the significance of the observed differences. The model was trained using data from multiple institutions in multiple countries. Results: The AI system demonstrated a significantly higher area under the curve (AUC of 0.89) compared to the average of three radiologists (AUC of 0.82). The AI also showed higher specificity (e.g., 93.0% versus 77.6%), and the recall rate for AI was three times lower than that of radiologists. The AI was more sensitive in detecting cancers with mass, distortion, or asymmetry and better at detecting T1 or node-negative cancers. This result underscores AI's potential to reduce false positives, but also demonstrates that it can detect cancers missed by radiologists. The AI system's performance aligns with other studies showing AI sensitivity to be non-inferior to, or surpassing, radiologists. AI systems can detect more cancers with mass or distortion than radiologists. The statistical analysis showed that the AI system achieved robust accuracy and demonstrated potential as a reliable tool to enhance breast cancer screening outcomes. A study also showed that AI can reduce the number of reads in a screening program by 41.4%. Conclusions: In this study the AI system outperformed the group of radiologists in terms of AUC, specificity, recall rates, and positive predictive value. These findings suggest that deep learning-based AI can significantly improve the detection of breast cancer in mammography and may serve as a valuable tool in the Uzbekistan healthcare setting. Additional studies that include larger, more heterogenous datasets are warranted and it is important to continue researching AI integration, including risk management and real-world follow up of performance. Future studies should examine the impact of AI on screening performance when used by radiologists and assess the value of different models for various conditions.

The article is devoted to the study of the phenomenon of rapid transformation of the world, which is artificial intelligence (AI). It is noted that the aforementioned is due to the ability of AI to accelerate innovation, change economic processes, influence the ways in which people interact with technology, and open up new prospects for the development of humanity. It is noted that in recent years, unprecedented rates of AI implementation have been observed in key areas of life, which allows for significant changes in the labor market, scientific research, social communications, global security and the economy. Attention is focused on the successful implementation of AI in the military sphere, which in the current conditions of an undeclared war against the Russian Federation is extremely important, since this helps to properly ensure national security. Examples of the use of AI in various spheres of life in Ukraine are given, including public management, administration, and control, which helps to perform complex tasks and make decisions based on data analysis, and such use of AI will become increasingly widespread and significant. The integration of AI into public administration, in particular, will help to analyze regulatory and legal acts and translate European legislation. It is very important that digital expertise of regulatory and legal acts will be carried out thanks to AI tools. And in the future, of course, all these solutions can be scaled for the work of the government as a whole. Digital technologies, especially AI, are the future of digital governments. Ukraine has already established itself as a powerful player in the field of technology, and is now taking other important steps in the development of artificial intelligence. At the same time, attention is drawn to the fact that, along with the above-mentioned positive aspects of AI, concerns have been expressed in the world about the existence of a danger of the introduction of AI, the emergence of a potentially catastrophic risk for humanity. This is confirmed by the signing of the Bletchley declaration (November 1–2, 2023, Great Britain) on the first day of the Artificial Intelligence Security Summit organized by the British government, according to which countries agreed to work together on AI security research. It was emphasized that the summit participants focused on the need for appropriate international efforts to study and address the potential impact of AI systems, as well as recognizing that the protection of human rights, transparency and clarity, fairness, accountability, regulation, safety, appropriate public oversight, ethics, bias mitigation, privacy and data protection require urgent attention. It is noted that the above requires appropriate knowledge and consideration of advanced European experience in this area and an appropriate vision of AI. For this purpose, we will analyze the content of the scientific article by Oriol Mir Puigpel “The Impact of the AI Law on Public Authorities and Administrative Procedures”. The said Law (Regulation) will have an impact on automated decision-making by public authorities throughout Europe. In addition, Member States may supplement its provisions with their national acts on administrative procedure, which will positively affect the creation of a regulatory framework and the use of artificial intelligence systems by European public authorities. It is noted that the Law aims to guarantee the free movement of artificial intelligence systems with equal application to entities of different forms of ownership. In this case, public authorities are usually considered “users”. At the same time, they will also be “suppliers” if they develop their own AI systems themselves or purchase an individual AI system. The Law prohibits certain AI systems and imposes numerous obligations on suppliers and, to a lesser extent, on users of high-risk systems, and defines four levels of risk for AI systems. The Law’s impact on EU and Member State public authorities when using or developing AI systems is disclosed, and their respective responsibilities are also defined.

Artificial intelligence systems are being used in various areas such as e-commerce, e-government and e-advertising, mainly because of their efficiency and ability to provide fast access to services. However, AI uses a massive amount of data, raising concerns about the privacy and control of this data held by large corporations or government entities. Although there are some risks associated with AI, such as discrimination in AI algorithms and over-reliance on technology, there are also many benefits to be gained from using these systems: automating administrative processes, providing assistance and support to citizens, data analysis and personalised decision-making etc. To maximise benefits and minimise risks, responsible control and management of AI data and systems is required. The integration of artificial intelligence in e-government and in the detection of breaches of the integrity of public functions may prove useful, but it must be accompanied by measures to ensure respect for human rights, data protection, discrimination and the avoidance of violations.

The integration of artificial intelligence (AI) systems into medical practice, specifically in cancer detection, presents unknown opportunities for better diagnoses and treatments for patients.However, with the integration of AI systems into a traditional relationship between healthcare professionals and patients, questions regarding accountability in this expanded relationship arise since traditional standards of medical law and medical ethics are addressed towards a healthcare professional.Against this backdrop, we investigate the necessary capacities to hold each involved party accountable (i.e., the healthcare professional, the patient, the developer, a regulatory oversight, and perhaps a clinical AI expert to support the healthcare professional).For this, we first explore the ethical and regulatory implications of employing AI systems in healthcare.We stress that the possibility of maintaining accountability is of central importance for the acceptability of the implementation of AI systems.As AI systems are often inscrutable and do not allow any party to explain and justify the behavior of the AI system, we examine whether and how explainable AI (XAI) methods can support each party with their accountability obligations.With our considerations, we propose a theoretical model for distributing accountability among each involved party and finally highlight the need for regulatory frameworks that can enable an ethically acceptable development and use of AI systems.