Artificial Intelligence Tools Research Articles

Exploring Pharmacists’ Perceptions of Text-Based Artificial Intelligence in Resident and Student Education

Introduction: As artificial intelligence (AI) becomes increasingly integrated into various professional fields, understanding its impact on pharmacy education is crucial. This study explores pharmacists’ perceptions of AI’s role in enhancing educational and professional practices, particularly focusing on the generation of educational content and analytical tasks. Objectives: The primary objective was to assess pharmacists’ concerns and perceived benefits regarding the use of AI in pharmacy education, examining variations across different age groups and years of practice. Methods: A cross-sectional survey was completed by 446 pharmacists who actively precept pharmacy residents and students. Respondents practiced across 35 states with over half (53.4%) being in Ohio. The survey included items on concerns about AI’s quality and accuracy, human interaction, plagiarism, and its potential benefits in data analysis and research literature summarization. Responses were analyzed to identify trends across demographic categories, including age and years in practice. Results: Of the respondents, 67.9% expressed concerns about the quality and accuracy of AI-generated content, while 50.9% were concerned about plagiarism. Younger pharmacists (73.8% of those aged 20-29) showed heightened concern about accuracy compared to older groups (56.8% of those aged 60+). In contrast, 57.8% of respondents recognized AI’s potential benefits for data analysis, with experienced pharmacists (>20 years in practice) being more likely to see these advantages (62.2%). Conclusion: The findings indicate a need for targeted educational strategies to address AI literacy and ethical use in pharmacy education. Integrating AI tools that support educational objectives while addressing these concerns could enhance the efficacy and acceptance of AI in pharmacy practice. Further research should explore the development of training programs that align with the evolving expectations and technological competencies of different pharmacist demographics.

Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design

Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.

Factors predicting treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs in psoriatic arthritis - a systematic review and meta-analysis.

The therapeutic response of patients with psoriatic arthritis (PsA) varies greatly and is often unsatisfactory. Accordingly, it is essential to individualise treatment selection to minimise long-term complications. This study aimed to identify factors that might predict treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs) in patients with PsA and to outline their potential application using artificial intelligence (AI). Five electronic databases were screened to identify relevant studies. A random-effects meta-analysis was performed for factors that were investigated in at least four studies. Finally, 37 studies with a total of 17,042 patients were included. The most frequently investigated predictors in these studies were sex, age, C-reactive protein (CRP), the Health Assessment Questionnaire (HAQ), BMI, and disease duration. The meta-analysis revealed that male sex (odds ratio (OR) = 2.188, 95% confidence interval (CI) = 1.912-2.503) and higher baseline CRP (1.537, 1.111-2.125) were associated with greater treatment response. Older age (0.982, 0.975-0.99), higher baseline HAQ score (0.483, 0.336-0.696), higher baseline DAPSA score (0.789, 0.663-0.938), and higher baseline tender joint count (TJC) (0.97, 0.945-0.996) were negatively correlated with the response to therapy. The other factors were not statistically significant but might be of clinical importance in the context of a complex AI test battery. Further studies are needed to validate these findings and identify novel factors that could guide personalised treatment decisions for PsA patients, in particular in developing AI applications. In accordance with the latest medical developments, decision-support tools based on supervised learning algorithms have been proposed as a clinical application of these predictors. Key messages • Given the often unsatisfactory and unpredictable therapeutic response in patients with Psoriatic Arthritis (PsA), treatment selection must be highly individualized. • A systematic literature review was conducted to identify the most reliable predictors of treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in PsA patients. • The potential integration of these predictors into AI tools for routine clinical practice is discussed.

Navigating the AI Wave: Overcoming Barriers and Unleashing the Potential of Artificial Intelligence in Transforming European Public Health

Abstract Artificial Intelligence (AI) is not just a future promise but an urgent necessity for modern public health. By providing invaluable insights into disease patterns, therapeutic interventions, and overall public health management, AI has the potential to revolutionise healthcare. To tackle essential public health functions effectively, harnessing AI must become a top priority. However, there is an urgent need for a cohesive strategy across Europe. Currently, varying readiness levels among European nations regarding AI adoption in health result in uneven progress across the continent. This disparity must be addressed to ensure all countries benefit equally from AI advancements. Recognising this, the World Health Organization European Regional Office launched a regional report on digital health in 2023. The report evaluated the integration of big data and advanced analytics, including AI, in health systems. Findings revealed that while 60% of Member States have a national data strategy, only 35% have a policy regulating big data and AI in health, and 38% lack both. This highlights a critical gap that must be filled urgently. Our upcoming session will address these challenges head-on. Organised to provide visionary insights, practical applications, and a landscape view of artificial intelligence in public health, the presentations will be followed by a roundtable discussion in which panellists will delve into practical challenges surrounding AI adoption. They will reflect on the profound impact AI could have on the future of European health systems and offer pragmatic and responsible steps forward, culminating in achievable recommendations for public health professionals. To enhance the session, we will utilise existing Generative AI tools to provide a real-time summary of the plenary and reinforce the call to action in alignment with panellists’ recommendations. This approach ensures that the session discusses the urgent need for AI in public health and actively demonstrates its practical applications. Moderators Natasha Azzopardi Muscat Director, Division of Country Health Policies and Systems, WHO Regional Office for Europe Dimitra Panteli Programme Manager/Senior Health Systems Analyst, European Observatory on Health Systems and Policies Facilitator Stefan Buttigieg Vice-President, EUPHA Digital health section Speakers/Panellists Martin McKee Professor of European Public Health, London School of Hygiene & Tropical Medicine, UK Katharina Ladewig Director, Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Germany Marco Marsella Director Digital, EU4Health and Health systems modernisation, Directorate-General for Health and Food Safety (DG SANTE), European Commission Keyrellous Adib Technical Officer Data Science and Digital Health, WHO Regional Office for Europe

Artificial Intelligence-Assisted Music Education: A Critical Synthesis of Challenges and Opportunities

Artificial intelligence (AI) is a hot topic that presents new challenges and opportunities for the improvement of educational processes. The disruptive and transformative force of this new technological development implies the adaptation of educational ecosystems for its use and integration as a didactic and pedagogical resource. From this perspective, a systematic literature review has been conducted to analyze the didactic potential of generative AI tools in the field of promoting artistic creativity in music education. The research results confirm that the incorporation of AI in music education is paving the way for a more personalized, interactive and efficient learning experience. In addition, the analysis suggests nine fundamental fields of IA implementation in music education: virtual and augmented reality (VR; VA); learning personalization, intelligent tutoring systems; composition assistants; improved historical and contextual learning; assessment systems; interactive ear training and music theory systems; tools for music collaboration and performance; and assistive technologies. Furthermore, the challenges presented by the intersection of AI and digital didactics in the field of music education are discussed.

Artificial intelligence‐based dynamic capabilities and circular supply chain: Analyzing the potential indirect effect of frugal innovation in retailing firms

AbstractAcademics and practitioners are increasingly interested in the role of the circular supply chains in maximizing resource use and improving sustainable performance. However, the shift from linear to circular supply chains is still at an early stage and still needs more research. The current study seeks to test the direct effects of artificial intelligence‐based dynamic capabilities on frugal innovation and circular supply chains in a group of retail firms operating in Jordan. The current paper also aims to test the potential indirect effect of frugal innovation. To achieve these objectives, a research framework was developed that explains hypothesized relationships between the previous constructs and was tested empirically through a sample of 212 respondents from the top administrative level in these firms. The casual and quantitative approach was employed to test the hypothetical relationships, and the partial least squares‐structural equation modelling (PLS‐SEM) approach was employed as a tool to analyze the data. The empirical results reported through data analysis showed acceptance of all hypothesized relationships that were hypothesized in this study, as there was a positive effect of both artificial intelligence‐based dynamic capabilities and frugal innovation on circular supply chains. In addition, the mediation was statistically significant, as frugal innovation mediated the relationship between artificial intelligence‐based dynamic capabilities and circular supply chains. This study had a set of interesting contributions at the theoretical and the managerial contributions, as this paper is the first work in the body of the literature that explores these innovative relationships. The current paper also contributed to developing recommendations that would improve sustainable performance in the retail sector by enhancing its dynamic capabilities based on artificial intelligence tools, which leads to strengthening circular supply chains.

Integrating artificial intelligence in forensic science

Thesis. The thesis of this article is to explore the integration of artificial intelligence (AI) methodology in forensic science. An assessment of the potential implications of AI for improving investigative processes and outcomes will also be addressed. Concept. The concept focuses on exploring the application of AI technologies in ar- eas of science such as analysing evidence recognising patterns and supporting decision- making systems. The article emphasises the practical use of AI algorithms in investiga- tions. on exploring how artificial intelligence technologies can be implemented in various aspects of forensic science. Results. An analysis of existing literature and case studies shows that integrating AI into forensic science can improve efficiency, accuracy, and objectivity in investigations. AI tools can automate tasks analyse datasets and identify patterns that might be challeng- 16 “ABoUT The INTeRNeT” - TheoRy ing for humans to detect. However utilising AI in forensic science poses challenges like algorithms, privacy issues with data handling, and the necessity for oversight. Further- more, when employing intelligence for inquiries it is essential to prioritise transparency, and accountability and uphold integrity in decision-making processes. Originality. This article adds to the discussion about integrating AI into science by of- fering a thorough examination of its potential advantages and obstacles faced along, with ethical concerns. By merging research findings and offering perspectives on emerging trends we can gain insights, into the impact of AI on advancing investigations in the years ahead. Additionally, the theoretical structure presented here establishes a foundation for research studies and practical implementations, within the field of forensic science. Keywords: Artificial intelligence, forensic science, AI applications in forensic science, machine learning for evidence analysis, crime scene reconstruction with AI

Conceptualizing the Implications of Artificial Intelligence (AI) Tools and Personalization Marketing on Consumer Purchase Intention: Insights from the Malaysian E-Commerce Market

Artificial intelligence (AI) has emerged as a powerful tool, enabling online retailers to offer highly personalized shopping experiences tailored to individual preferences and behaviors. As e-commerce continues to grow in Malaysia, understanding the influence of AI and personalization marketing on consumer purchase intentions has become increasingly important for businesses seeking to remain competitive. However, the rapid adoption of AI also raises concerns about data privacy, ethical AI usage, and compliance with emerging data protection regulations, such as Malaysia’s Personal Data Protection Act (PDPA). This study aims to explore the potential impact of artificial intelligence (AI) and personalization marketing on consumer purchase intention within the Malaysian e-commerce market. By reviewing previous literature and theoretical frameworks, the study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments. The study adopts a quantitative approach, in which quota sampling will be used for the participant selection. A self-administered questionnaire with a five-point Likert scale will be employed to gather data from e-commerce users in Malaysia. The findings from this study have important implications for both e-commerce businesses and policymakers in Malaysia. For businesses, understanding which aspects of AI and personalization most influence consumer purchase intentions can help them strategically implement these technologies to enhance customer engagement and drive sales. For policymakers, the study highlights the need to consider ethical and legal issues, such as data privacy and policy issues, in the growing use of AI in the e-commerce market.

The Impact of AI-Powered Personalized Learning on Student Performance in Udaipur Colleges

The integration of Artificial Intelligence (AI) into educational settings has garnered increasing attention for its potential to enhance personalized learning and improve student outcomes. This study investigates the impact of an AI-powered personalized learning tool implemented at colleges in Udaipur. Using a mixed-methods approach, we collected quantitative data through pre-test and post-test scores and qualitative feedback via surveys and interviews with students and teachers. The results revealed significant improvements in student performance and engagement, with the AI tool providing tailored learning experiences that addressed individual needs. Statistical analyses demonstrated a substantial positive effect on academic outcomes, while qualitative feedback highlighted the tool's usability and effectiveness in identifying students' weaknesses. The findings suggest that AI-powered personalized learning can be a valuable asset in education, offering recommendations for broader adoption and further research in this field.

Pedagogy and generative artificial intelligence: Applying the PICRAT model to Google NotebookLM

Healthcare educators (HPE) are challenged by rapid developments in Generative Artificial Intelligence (GenAI) tools. They need a standardized model to evaluate these new tools and to guide them in pedagogically-sound integration in the curriculum. PICRAT is an educational model designed specifically to help teachers meet this challenge. NotebookLM is a new multi-featured GenAI tool to help teachers and learners in education and research. Its newest feature allows automatic generation of an engaging podcast (called audio overview) from uploaded education or research content. Using the example of NotebookLM and, specifically, the auto-podcast feature, we illustrate how HPE can use the PICRAT model to evaluate GenAI tools for technology integration. We discuss how this model can be utilized as a standardized approach for evaluation and implementation of GenAI tools in health professions education.

The Role of Artificial Intelligence (AI) in Personalized Learning: A Case Study in K-12 Education

This study investigated the mediating role of artificial intelligence (AI) in intensifying personalized learning in K-12 education by employing qualitative case research. The study examines how an AI deployed within a K-12 school changes the engagement, academic performance, and role of teachers through its implementation. Results show that AI tools lead to large improvements in student achievement through personalized learning paths, increased engagement, and the immediacy of feedback. Nevertheless, challenges including data privacy ponderings and the digital gap are significant insights for educators to consider as well. The report suggests guidelines for policymakers and practitioners to ensure the productive application of AI in personalized learning, including equal access to technology and continuous teacher upskilling.

Use of Artificial Intelligence tools in supporting decision-making in hospital management

BackgroundThe use of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits of AI integration, and examines hospital managers’ perceptions of AI as a decision-support tool.MethodsA descriptive and exploratory study was conducted using a qualitative approach. Data were collected through semi-structured interviews with 15 hospital managers from various departments and institutions. The interviews were transcribed, anonymized, and analyzed using thematic coding to identify key themes and patterns in the responses.ResultsHospital managers highlighted the current inefficiencies in decision-making processes, often characterized by poor communication, isolated decision-making, and limited data access. The use of traditional tools like spreadsheet applications and business intelligence systems remains prevalent, but there is a clear need for more advanced, integrated solutions. Managers expressed both optimism and skepticism about AI, acknowledging its potential to improve efficiency and decision-making while raising concerns about data privacy, ethical issues, and the loss of human empathy. The study identified key challenges, including the variability in technical skills, data fragmentation, and resistance to change. Managers emphasized the importance of robust data infrastructure and adequate training to ensure successful AI integration.ConclusionsThe study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns. Effective integration of AI requires addressing technical, ethical, and cultural issues, with a focus on maintaining human elements in decision-making. AI is seen as a powerful tool to support, not replace, human judgment in hospital management, promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare institutions with the necessary infrastructure and providing specialized training for managers are crucial for maximizing the benefits of AI while mitigating associated risks.

Enhancing instructional design learning: a comparative study of scaffolding by a 5E instructional model-informed artificial intelligence chatbot and a human teacher

ABSTRACT To address the limitations of general-purpose artificial intelligence (AI) tools, we developed a task-oriented AI chatbot based on the 5E (i.e. “engage”, “explore”, “explain”, “elaborate” and “evaluate”) model to scaffold students’ instructional design process. We examined the impact of integrating the 5E instructional model-informed AI chatbot on students’ learning performance and perceptions. The results indicated that the AI chatbot, when combined with human teacher scaffolding, significantly improved the students’ instructional design performance relative to receiving human teacher scaffolding only. The chatbot provided valuable suggestions on instructional design frameworks, class activities and teaching topics during the “explore” phase. In the “evaluate” phase, the chatbot offered immediate feedback on the students’ design plans and proposed alternative instructional frameworks regarding areas for improvement. However, the students expressed concerns about the chatbot’s evaluation quality, noting that it needed to be better aligned with the course assessment rubric. We recommend using AI chatbots for instructional design conceptualisation, although we emphasise the critical role of human teachers in evaluating final design work and providing timely support.

Intelligent E-commerce under the Trend of Integration of Artificial Intelligence and E-commerce: A Case Study of Alibaba International Station

Abstract: China has fully entered the era of digital economy, and undoubtedly, the e-commerce industry needs to keep pace with the times and evolve into a new stage of development: integrating artificial intelligence with e-commerce. Nowadays, an abundance of new productivity tools have unveiled new opportunities for the e-commerce industry. This study focuses on Alibaba International, a premier global digital trading platform, to illustrate the applications of artificial intelligence tools in e-commerce, examine both the positive and negative impacts of these tools on e-commerce, and suggest strategies for enhancing e-commerce Operation Management. The aim is to offer enlightenments for small and medium-sized cross-border e-commerce businesses. Through this case analysis, it can be understood that intelligent e-commerce has provided many conveniences for the e-commerce industry, but also faces a series of challenges, such as AI generated false or infringing content, and increased operational management difficulties. In this situation, e-commerce companies should take strengthening the cultivation of intelligent talents as a guarantee, focusing on customer experience and valuing personalized product needs as advantages, and improving operational management efficiency through the reasonable use of favorable policies to bring profits to the enterprise.

Nature redux: interrogating biomorphism and soft robot aesthetics through generative AI

Artificial Intelligence (AI) has rapidly become a widespread design aid through the recent proliferation of generative AI tools. In this work we use generative AI to explore soft robotics designs, specifically Soft Biomorphism, an aesthetic design paradigm emphasizing the inherent biomorphic qualities of soft robots to leverage them as affordances for interactions with humans. The work comprises two experiments aimed at uncovering how generative AI can articulate and expand the design space of soft biomorphic robotics using text-to-image (TTI) and image-to-image (ITI) generation techniques. Through TTI generation, Experiment 1 uncovered alternative interpretations of soft biomorphism, emphasizing the novel incorporation of, e.g., fur, which adds a new dimension to the material aesthetics of soft robotics. In Experiment 2, TTI and ITI generation were combined and a category of hybrid techno-organic robot designs discovered, which combined rigid and pliable materials. The work thus demonstrates in practice the specific ways in which AI image generation can contribute towards expanding the design space of soft robotics.

Challenges of adhering to scientific research ethics in the artificial intelligence applications- a study on a sample of algerian researchers

This study aims to explore the impact of artificial intelligence (AI) on the accuracy and efficiency of scientific research, while focusing on the ethical challenges associated with it. It also seeks to assess the level of awareness of AI ethics among Algerian researchers and the adequacy of current policies in dealing with these challenges, such as issues of plagiarism, privacy violation, and biased results. This will provide a deeper understanding of the reality of scientific research in Algeria in light of rapid technological developments. An electronic questionnaire was used, targeting a group of Algerian researchers, reaching a sample size of 623 individuals. Through this questionnaire, we aimed to collect data that would enable us to provide recommendations to enhance the responsible and ethical use of AI tools while maintaining the integrity and reliability of scientific research. The study found that the respondents demonstrated varying levels of awareness of the ethical implications of using AI in their research, with a lack of adequate training and resources to help researchers deal with the ethical complexities of AI-based research. The study also confirms that current ethical policies and regulations in Algeria are insufficient to effectively address the challenges posed by AI. This calls for the establishment of a comprehensive legal framework in the near future and the updating of ethical guidelines to regulate the use of AI in academic research.

Education in the AI era: a long-term classroom technology based on intelligent robotics

Artificial Intelligence (AI) will have a major social impact in the coming years, affecting today’s professions and our daily routines. In the short-term, education is one of the most impacted areas. The autonomous decision making that can be achieved with tools based on AI implies that some of the traditional methodologies associated with the fundamentals of the learning process in students, must be reviewed. Consequently, the role of teachers in the classroom may change, as they will have to deal with such AI tools performing parts of their work, and with students making a common use of them. In this scope, the AI in Education (AIEd) community agrees on the key relevance of developing AI literacies to train teachers and students of all educational levels in the fundamentals of this new technological discipline, so they can understand how these tools based on AI work and pilot the adaptation in an informed way. This implies teaching students about the fundamentals of topics like perception, representation, reasoning, learning, and the impact of AI, with the aim of delivering a solid formation in this area. To support them, formal teaching and learning resources must be developed and tested with students, properly adapted to different educational levels. The main contribution of this proposal lies in the presentation of the Robobo Project, a technological tool based on intelligent robotics that supports such formal AI literacy training for a wide range of ages, from secondary school to higher education. The core part of this paper is focused on showing the possibilities the Robobo Project offers to teachers in a simple way, and how it can be adapted to different levels and skills, leading to a long-term educational proposal. Validation results that support the feasibility of this technology in the education about AI, obtained with students and teachers in different educational levels during a period of six years, are presented and discussed.

O trudach dialogu – sztuka komunikacji z AI w projektowaniu

The rapid spread of LLMs (large language models) and other technologies based on generated content and image has significantly impacted the perception of creative work, including the area of product design. Tools using generative AI enable the acceleration or facilitation of the ideation processes, producing many different solutions to a given problem. Beside facilitating creative processes due to automation of some tasks, especially those based on working with large databases, generative AI can assume a form of specific creativity stimulator. The key notion conditioning the effective use of generative AI tools is the ability of asking adequate questions. This requires increased engagement in works towards changes in education, currently directed to reproduction and memorisation. Contrary to popular opinion, popularisation of AI as element of design process could result in expanding the spectrum of designer’s possibilities. Methodical education in compliance with the basics of cause-and-effect thinking and teaching the analytical design approach guarantee providing the young generation with solid foundation of collaboration with generative AI models. Keywords: artificial intelligence (AI), design education, creativity

Transformation of Traditional Corporate Tax Planning into AI-Driven Corporate Tax Planning

Corporate tax planning has traditionally relied on manual processes and expert judgment to minimize tax liabilities. However, the rise of artificial intelligence (AI) presents new opportunities to transform these practices through automation, real-time analysis, and predictive capabilities. To conduct this research, a comprehensive review of academic literature, case studies, and existing AI tools in tax planning was carried out. The proposed AI-based tax planning model was developed based on these findings and validated through simulations and expert feedback. The study also examined the process of integrating AI systems with existing corporate tax structures to assess the feasibility and effectiveness of implementation. Results show that AI-driven corporate tax planning significantly enhances accuracy and efficiency compared to traditional methods. The AI model provides real-time analysis and predictive insights, enabling businesses to optimize tax strategies while ensuring compliance with evolving regulations. Tax authorities also benefit from AI implementation, gaining more transparent and equitable tax structures, along with stronger supervision capabilities. The proposed model can be developed into customized software tailored to individual companies, seamlessly integrating with existing corporate systems. These findings indicate that AI has the potential to revolutionize corporate tax planning by streamlining tax management and optimizing outcomes. For businesses, AI enables personalized tax strategies that adapt to specific data and regulations, improving both compliance and long-term tax optimization.

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. Therefore, multiple radiopharmaceuticals targeting different receptors on a cell is warranted. However, past implementations invivo have not met with convincing results because of the absence of optimization strategies. Here we present artificial intelligence (AI) tools housed in a new version of our software platform, MIRDcell V4, that optimize a cocktail of radiopharmaceuticals by minimizing the total disintegrations needed to achieve a given surviving fraction (SF) of tumor cells. Methods: AI tools are developed within MIRDcell V4 using an optimizer based on the sequential least-squares programming algorithm. The algorithm determines the molar activities for each drug in the cocktail that minimize the total disintegrations required to achieve a specified SF. Tools are provided for populations of cells that do not cross-irradiate (e.g., circulating or disseminated tumor cells) and for multicellular clusters (e.g., micrometastases). The tools were tested using model data, flow cytometry data for suspensions of single cells labeled with fluorochrome-labeled antibodies, and 3-dimensional spatiotemporal kinetics in spheroids for fluorochrome-loaded liposomes. Results: Experimental binding distributions of 4 211At-antibodies were considered for treating suspensions of MDA-MB-231 human breast cancer cells. A 2-drug combination reduced the number of 211At decays required by a factor of 1.6 relative to the best single antibody. In another study, 2 radiopharmaceuticals radiolabeled with 195mPt were each distributed lognormally in a hypothetical multicellular cluster. Here, the 2-drug combination required 1.7-fold fewer decays than did either drug alone. Finally, 2 225Ac-labeled drugs that provide different radial distributions within a spheroid require about one half of the disintegrations required by the best single agent. Conclusion: The MIRDcell AI tools determine optimized drug combinations and corresponding molar activities needed to achieve a given SF. This approach could be used to analyze a sample of cells obtained from cell culture, animal, or patient to predict the best combination of drugs for maximum therapeutic effect with the least total disintegrations.