A Review on Artificial Intelligence in Pharmacy

A power plant converts energy from a non-electric form to an electric one. Depending on the energy conversion, power plants are classified as minerals, nuclear, solar, geothermal, hydroelectric, and so on. The main goal is to perform this transformation as best as possible. Criteria of safety, efficiency, reliability and affordability are taken into account as benchmarks. The station consists of several units that generate and work together to meet electricity needs. For a fossil fuel power plant, each unit consists of three main components: a boiler, a turbine and a generator. The complexity of the operation is due to the variability of the load and high efficiency required in a wide range of operations. The main difficulties for the management task then arise due to the strong link between process variables and process nonlinearity. The purpose of this article is to analyze and develop a proposal for the introduction of artificial intelligence in the power industry of Ukraine. To achieve this goal, we will offer the following tasks: - analyze existing systems for the use of artificial intelligence in industry; - develop proposals for the introduction of artificial intelligence in the electricity system of Ukraine. In this study, methods of statistical, factorial, historical, comparative, logical, economic-mathematical and systems analysis, the method of expert assessments were used, which allowed to formulate proposals for the introduction of artificial intelligence in the power industry. Artificial neural networks are the first step towards a fundamentally new system of information analysis. They are mathematical and computer models that simulate the work of biological neurons, ie a system of interacting processes, built on the principle of connecting nerve cells of the human brain. It should be noted that they differ from conventional machine algorithms in the ability to learn, memorize and reproduce images, determine patterns, memorize and analyze information and obtain results. Output signals that arrive at the next processor and continue to be converted. Thus, artificial neurons form networks and layers in which connections are created, restored, maintained and destroyed by special signals. With regard to artificial intelligence, there is no generally accepted definition of artificial intelligence, therefore, by artificial intelligence we mean a machine system capable of learning using objective knowledge and experience, to solve creative problems like the human brain and inventive tasks, not going through options, build strategies and apply abstract concepts. Digitalization and artificial intelligence are penetrating most sectors of the economy, including the electricity sector. The digitalization of energy requires the development and widespread use of end-to-end technologies, including industrial Internet, components of robotics, wireless communications, artificial intelligence and others. Thus, artificial intelligence technologies have prospects for development. Of course, significant targeted funding is needed to achieve significant results. The interaction of research institutes and universities with business is very important, where artificial intelligence technologies are also being developed. Attention of the authorities and society, their understanding of the importance of the tasks facing countries to achieve the goal of implementing artificial intelligence. With regard to electricity, the integration of artificial intelligence into the industry will help optimize and increase efficiency in all aspects of energy production, transmission and consumption. It should be noted that the development of electricity is a step towards the development of other industries. That is why the transition to the digital industry is impossible without the digitalization and intellectualization of the power industry

PurposeThe introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.Design/methodology/approachThe evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.FindingsThe results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.Originality/valueThe authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

Artificial Intelligence (AI), a fundamental aspect of the Fourth Industrial Revolution, has become a pivotal concern across various industries, including business management. It is increasingly applied in fields such as marketing, human resource management (HRM), and manufacturing. Within HRM, AI and machine learning technologies are utilized to enhance employee experience, provide personalized training, and analyze HR data for informed decision-making. This integration of technology into HRM, traditionally a people-focused domain, represents a significant shift. AI technology adoption in HRM has surged in prominent settings. For instance, IBM has leveraged AI solutions like Blue Matching, CogniPay, and Watson Career Coach to streamline HR operations. Blue Matching predicts mobile tasks based on employee profiles, while CogniPay aids managers in decision-making by evaluating performance. Watson Career Coach offers personalized career guidance. However, notable cases like Amazon’s AI Recruitment Program have highlighted concerns regarding biases. Artificial intelligence is reshaping various aspects of our lives, including recruitment practices. As more businesses harness AI for candidate selection and evaluation, there’s a growing interest among HR managers in adopting AI solutions. However, this technological advancement also brings challenges, such as the displacement of existing job roles and the need for additional professionals to manage the evolving landscape. Understanding the potential benefits and challenges of AI in recruitment is essential for informed decision-making in HRM.

Objective: The objective of this study is to examine the civil liability implications of errors associated with artificial intelligence (AI) technologies in healthcare, with the aim of proposing comprehensive legal frameworks to regulate AI’s integration into the sector. This research seeks to align AI advancements with international standards and sustainable development goals, particularly SDG 3 (Good Health and Well-Being) and SDG 16 (Peace, Justice, and Strong Institutions). Theoretical Framework: The research is grounded in international human rights conventions, national legislation on data protection, and frameworks such as the World Health Organization’s guidelines and the Council of Europe Framework Convention on Artificial Intelligence, Human Rights, Democracy, and the Rule of Law. These provide a robust foundation for exploring the legal and ethical dimensions of AI in healthcare. Method: A qualitative research approach was adopted, analysing legal documents, international standards, and case studies to assess the regulatory challenges of AI in healthcare. The study also reviews best practices from jurisdictions effectively addressing AI’s legal and ethical implications. Results and Discussion: The findings reveal significant gaps in existing legal frameworks governing AI in healthcare, particularly in areas of accountability and data protection. The discussion contextualizes these gaps within the theoretical framework, emphasizing the need for proactive legislative measures. Challenges such as algorithmic bias, transparency, and equitable access to AI technologies are critically examined. Research Implications: The study underscores the necessity for Omani legislators to adopt robust regulatory measures, ensuring accountability and alignment with international best practices. These measures can enhance healthcare quality and contribute to sustainable development. Originality/Value: This study contributes to the literature by addressing the underexplored legal and ethical challenges of AI in healthcare within the context of sustainable development. It offers actionable recommendations for policymakers, emphasizing the responsible and ethical use of AI to advance medical innovation and protect human rights.

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

The article discusses the essence and features of modern Internet marketing based on the use of artificial intelligence (AI) technologies. The article examines how AI can help online marketers collect and analyze consumer data, segment and personalize audiences, create and distribute effective content, optimize and automate advertising campaigns, and measure and improve marketing results. The article also analyzes the latest trends in the use of AI technologies by Ukrainian and foreign companies and identifies their advantages.The investigation identified effective AI tools in online marketing, such as chatbots, recommender systems, text generators, sentiment analysers, face and speech recognition systems, etc. The article presents a comparison of AI tools in online marketing of Ukrainian and foreign companies. Foreign companies use modern products that have no analogues in Ukraine. This is due to the popularity of the trend of using AI technologies in foreign companies. In Ukraine, this trend is developing and we can see it in the use of chatbots and CRM systems. They have been implemented by such companies as PrivatBank and Promodo. Despite this, the development of foreign companies is progressive. Previously, only large corporations could afford to use AI technologies, but now young startups are catching up with the trend. AI combined with human capabilities can actually turn business into a universal tool for profit. However, this path is not without obstacles, so the article highlights the challenges of AI that may affect the economic, legal, ethical, and social spheres of life. Scenarios for preventing possible problems allow for the effective use of AI's potential. The article suggests measures that should help society adapt to changes in the socio-economic environment associated with the displacement of humans by AI. It is important to understand the advantages and disadvantages of modern online marketing tools to ensure effective solutions to business problems.

Objective: This study examines the role of Artificial Intelligence (AI) as both an innovative tool and a challenge in citizenship education at Islamic Vocational School Kanigoro. It aims to provide insights into how AI impacts educational practices, student engagement, and learning outcomes.Methods: A qualitative approach was employed, utilizing observations and interviews to gather data from teachers and students. This method allowed for an in-depth exploration of the dynamics and implications of AI integration in the classroom.Results: The study found that AI technologies, such as Quizizz and Kahoot, were effectively used to automate administrative tasks and personalize learning experiences, significantly improving educational efficiency and student engagement. AI-driven platforms provided tailored instructional content and rapid assessments, enhancing teaching methodologies and learning outcomes. However, challenges such as over-reliance on AI, privacy concerns, and potential algorithmic bias were identified.Conclusion: This study highlights the transformative potential of AI in citizenship education while underscoring the importance of balancing technological innovation with human interaction. Effective integration of AI can enhance learning experiences and foster critical thinking and ethical reasoning among students. The findings advocate for continuous professional development for educators, ethical AI practices, and the preservation of human-centric educational values. Future research should explore diverse educational contexts to further understand the benefits and challenges of AI in education

Pharmaceutical companies rely on regulatory intelligence (RI) to analyze information from internal and external sources. To facilitate RI activities, companies are seeking ways to harness technology to optimize their capabilities. Specifically, there is a growing interest for artificial intelligence (AI) to enhance RI activities. However, exploration of the potential utility of AI and related technologies will be key to begin unlocking these tools for the regulatory community. To identify potential development paths for these technologies, we interviewed over 30 global regulatory leaders at 22 pharmaceutical companies and 3 leading companies in RI and AI technologies. Thirteen of the 22 pharmaceutical companies also provided responses to a subsequent informational survey. This study elucidated potential value proposition, barriers, and risks to integrating AI into the RI field. Twenty of the 22 participating companies consider that AI offers significant opportunity for RI activities of data processing (mining, searching, monitoring, alerting). Thirty-two percent of companies envisage use in data synthesis (combining different types of information across formats), 36% in data analysis (trends, patterns, predictive analytics), and 23% in decision making. Additionally, results of this research provided insights about the potential role of precompetitive consortia, which may enhance future actualization. Opportunity presents for AI to enhance quality, speed, and efficiency of RI activities. This assessment of the current technology landscape revealed a lack of fully developed AI tools; however, the RI community demand is beginning to be recognized. Therefore, now more than ever, the timing to advance AI within the RI field is right.

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

We propose the popular board game Blood Bowl as a new challenge for Artificial Intelligence (AI). Blood Bowl is a fully-observable, stochastic, turn-based, modern-style board game with a grid-based game board. At first sight, the game ought to be approachable by numerous game-playing algorithms. However, as all pieces on the board belonging to a player can be moved several times each turn, the turn-wise branching factor becomes overwhelming for traditional algorithms. Additionally, scoring points in the game is rare and difficult, which makes it hard to design heuristics for search algorithms or apply reinforcement learning. We present the Fantasy Football AI (FFAI) framework that implements the core rules of Blood Bowl and includes a forward model, several OpenAI Gym environments for reinforcement learning, competition functionalities, and a web application that allows for human play. We also present Bot Bowl I, the first AI competition that will use FFAI along with baseline agents and preliminary reinforcement learning results. Additionally, we present a wealth of opportunities for future AI competitions based on FFAI.

The integration of artificial intelligence (AI) in rural healthcare systems holds great promise for improving access to quality medical care and addressing healthcare disparities. However, numerous barriers must be overcome to successfully implement AI technologies in these settings. This paper explores the key challenges and potential solutions for implementing AI in rural healthcare systems, focusing on infrastructure, data privacy, internet connectivity, and workforce readiness. One of the primary barriers is the lack of infrastructure in rural areas. Many rural healthcare facilities are under-resourced, lacking the necessary hardware, software, and technical support to adopt AI solutions. Addressing this issue requires significant investment in healthcare infrastructure, including upgrading existing facilities and ensuring they are equipped with modern technology. Data privacy and security present another significant challenge. AI systems rely on vast amounts of patient data to function effectively, raising concerns about data confidentiality and protection. Implementing robust cybersecurity measures and developing clear data governance policies are essential to safeguard patient information and build trust in AI technologies. Internet connectivity is a crucial enabler for AI-driven healthcare, yet rural areas often suffer from unreliable or limited internet access. Solutions to this challenge include investing in broadband infrastructure, exploring alternative connectivity options such as satellite internet, and leveraging offline AI capabilities that can operate with minimal connectivity. Workforce readiness and acceptance of AI technology are also critical for successful implementation. Many rural healthcare providers may lack the training and expertise needed to utilize AI tools effectively. Comprehensive training programs, ongoing education, and support from technology providers can help bridge this knowledge gap and encourage the adoption of AI. In conclusion, while the integration of AI in rural healthcare systems faces significant barriers, targeted solutions can address these challenges. Investment in infrastructure, robust data privacy measures, improved internet connectivity, and comprehensive training programs are essential steps to enable the successful implementation of AI in rural healthcare. By overcoming these barriers, AI has the potential to transform rural healthcare delivery, improve patient outcomes, and reduce healthcare disparities. Collaborative efforts among government entities, healthcare providers, and technology companies will be crucial in realizing this potential and ensuring equitable access to advanced healthcare technologies in rural areas.

The rapid advancement of Artificial Intelligence (AI) has driven the expansion of telemedicine solutions worldwide, enabling remote diagnosis, patient monitoring, and treatment support. This study aimed to explore medical students' perceptions of AI in telemedicine, focusing on how these future physicians view AI's potential, benefits, and challenges. A cross-sectional survey was conducted among 161 Romanian medical students spanning Years 1 through 6. Participants completed a 15-item questionnaire covering demographic factors, prior exposure to AI, attitudes toward telemedicine, perceived benefits, and concerns related to ethical and data privacy issues. A questionnaire on digital health acceptance was conceived and integrated into the survey instrument. Out of 161 respondents, 70 (43.5%) reported prior telemedicine use, and 66 (41.0%) indicated high familiarity (Likert scores ≥ 4) with AI-based tools. Fifth- and sixth-year students showed significantly greater acceptance of AI-driven telemedicine compared to first- and second-year students (p = 0.014). A moderate positive correlation (r = 0.44, p < 0.001) emerged between AI familiarity and telemedicine confidence, while higher data privacy concerns negatively affected acceptance (β = -0.20, p = 0.038). Gender differences were noted but did not reach consistent statistical significance in multivariate models. Overall, Romanian medical students view AI-enhanced telemedicine favorably, particularly those in advanced academic years. Familiarity with AI technologies is a key driver of acceptance, though privacy and ethical considerations remain barriers. These findings underline the need for targeted curricular interventions to bolster AI literacy and address concerns regarding data security and clinical responsibility. By proactively integrating AI-related competencies, medical faculties can better prepare students for a healthcare landscape increasingly shaped by telemedicine.

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) has emerged as a transformative force in nursing education, offering benefits such as adaptive learning, simulations, and intelligent assessment. However, challenges related to ethics, privacy, and technical limitations may influence students’ ability to engage confidently with AI. Aim: To examine the mediating role of AI challenges in the relationship between nursing students’ attitudes toward AI and their self-efficacy. Methods: A cross-sectional study was conducted with a convenience sample of 342 undergraduate nursing students across all four academic levels at Sohag University. Data were collected using three validated instruments: the AI Challenges Assessment Questionnaire, the AI Attitude Scale, and the General Self-Efficacy Scale. Statistical analyses included descriptive statistics and path analysis to assess direct, indirect, and total effects. Results: Most participants were female (50.3%), from rural areas (63.5%), and had not received formal AI training (89.5%). However, 91.2% reported using AI applications, with ChatGPT being the most common (77%). High levels of AI challenges (80.4%), AI attitude (67.5%), and self-efficacy (76.6%) were observed. Path analysis revealed a significant direct effect of AI attitude on self-efficacy (β = 0.343, p < 0.001), an indirect effect through AI challenges (β = 0.266, p < 0.001), and a total effect of β = 0.609 (p < 0.001). Conclusion: AI challenges partially mediate the relationship between students’ attitudes toward AI and their self-efficacy. Positive attitudes enhance self-efficacy, but overcoming AI-related barriers is essential to maximize this impact. Recommendation: Provide supportive guidance from faculty members how model effective AI use, address student concerns, and foster a positive learning environment.

Introduction: This study aims to analyze the readiness of accounting students in adopting and developing AI. This research aims to obtain a comprehensive insight into the knowledge, perceptions, and readiness of accounting students in adopting and developing Artificial Intelligence (AI) in the context of accounting. The results of the study show that the readiness of accounting students to face AI opportunities and challenges is still quite low. The research shows that the higher students' knowledge of AI concepts and applications, and the better their perceptions of AI opportunities and challenges, the higher their readiness to face AI integration will be. The findings of this study are expected to complement the academic literature by providing new insights into the opportunities and challenges faced by accounting students in adopting and developing AI technologies in the accounting context. Methods: This research method uses a quantitative descriptive method with data collected through questionnaires distributed to respondents as an instrument used on the object of research, namely accounting students who are studying in Semarang City, Indonesia. The technique is used in collecting samples using the purposive sampling method. The analysis methods used include a Descriptive Test, Validity Test, Reliability Test, Determination Coefficient, Classical Assumption Test, and Multiple Linear Regression with F Test and T-test. Results: The results of the study show that the readiness of accounting students to face AI opportunities and challenges is still quite low. The research shows that the higher students' knowledge of AI concepts and applications, and the better their perceptions of AI opportunities and challenges, the higher their readiness to face AI integration will be. The findings of this study are expected to complement the academic literature by providing new insights into the opportunities and challenges faced by accounting students in adopting and developing AI technologies in the accounting context. Keywords: Artificial Intelligence, Accounting Students, Accounting Technology.