Artificial Intelligence Research Articles

EXplainable artificial intelligence (XAI) in business management research: a success/failure system perspective

PurposeeXplainable artificial intelligence (XAI) is an evaluation framework that allows users to understand artificial intelligence (AI) processes and increases the reliability of AI-produced results. XAI assists managers in making better decisions by providing transparency and interpretability in AI systems. This study explores the development of XAI in business management research.Design/methodology/approachThis study collects and analyzes business management research related to XAI using common management keywords as the basis. We used the success/failure system to explore its research guidelines XAI in business management.FindingsThe study found significant growth in XAI research within business management. This research will be discussed from various management disciplinary perspectives to help scholars understand the current research directions. Additionally, we utilize a success/failure system to explore how this theory can be applied to artificial intelligence and business management research.Originality/valueThe success/failure system offers a comprehensive framework encompassing the evolution of the cosmos, nature, and ecology. This theory can offer valuable insights for business management in XAI and competitive societies, governments, and enterprises, enabling them to formulate effective strategies for the future.

Leveraging artificial intelligence to enhance psychological capital in learning organizations: a viewpoint

Purpose This viewpoint article presents perspectives on the intersection of AI and the diverse elements of Psychological Capital (PsyCap) and is a foundation for discussion on this juncture. It also exhibits the advantages of integrating AI and PsyCap in learning organizations and the challenges and future directions associated with the same. Design/methodology/approach The article exhibits the juncture of Artificial Intelligence (AI) and Psychological Capital (PsyCap) in context to learning organizations. It also highlights the various AI tools that can be used to manage diverse PsyCap attributes in learning organizations. The approach is to present a starting point for discussion on the intersection of the two concepts and how learning organizations can leverage them to create sustained competitive advantage. Findings Artificial Intelligence Systems can be used to enhance self-efficacy, promote optimism, cultivate hope and build resilience in learning organizations. A strategic approach with continued evaluation of AI tools is the key. However, challenges like privacy and unethical issues, biasness and management of human- AI intervention have to be taken into consideration for effective integration of AI tools and Psychological Capital. Originality/value The research in the domain of Artificial Intelligence and related technologies is not a new phenomenon. However, there is a possibility to understand that AI is not a competitor to human ingenuity but a facilitator to it. Through this article an effort is made to ignite the conversation on the topic and it can be a starting point for future research on the integration of the two perspectives.

Boosting the efficacy of green accounting for better firm performance: artificial intelligence and accounting quality as moderators

Purpose This study aims to deepen our understanding of how conventional technologies and robust accounting education standards can impact the effectiveness of green accounting practices in enhancing firm performance. To achieve this, the paper explores the moderating effects of artificial intelligence (AI) and accounting education quality on the relationship between green accounting and firm performance. Design/methodology/approach Using generalized method of moments estimation, this research uses a comprehensive dataset comprising 32,680 firm-year observations of listed companies from ten prominent countries – Canada, the UK, the USA, China, France, Germany, India, Japan, South Korea and Italy – over the period from 2012 to 2022. These countries, selected based on their high gross domestic product rankings as reported by the International Monetary Fund, ensure a diverse representation of economic strengths and capture a wide range of green accounting practices. Findings The study shows that green accounting practices positively impact current firm performance. Country-level AI positively moderates this relationship, suggesting that advanced AI infrastructure enhances the benefits of green accounting through improved data accuracy and decision-making. However, country-level accountancy education quality negatively moderates the relationship, indicating that stringent implementation of green accounting standards in these regions may introduce complexities and costs that reduce firm performance. Practical implications Integrating AI enhances data processing, predictive analytics and decision-making, improving green accounting effectiveness. High-quality accounting education ensures accurate reporting and greater transparency. These insights, when applied, can empower businesses to optimize sustainability strategies, assist policymakers in developing targeted regulations and guide educators in preparing accountants for the evolving demands of green accounting. Originality/value To the best of the authors’ knowledge, this study is the first to explore the combined moderating effects of AI and accounting education quality on the relationship between green accounting and firm performance. By highlighting the synergistic role of digital innovation and robust educational standards, this research offers novel insights into how these factors can enhance the effectiveness of green accounting practices and improve financial outcomes.

Unveiling the potential of digital human avatars in modern marketing strategies

PurposeThis study aims to develop a theoretical framework that marketing practitioners and scholars can adopt to enhance their understanding of how firms can effectively deploy and use digital human avatars as part of their global digital marketing strategy. By doing so, we inform investors of ongoing digital transformations of marketing practices that will equip marketeers to provide scalable, tailored, reliable and relevant digital self-service interactions to users, consequently improving the user/customer experience.Design/methodology/approachThematic analysis was used to discover factors to enable the successful implementation of digital human avatars, drawing on in-depth interviews with fourteen executives of digital human avatars developer companies worldwide and analysis of ten podcasts and webinars with artificial intelligence (AI) experts.FindingsDigital human avatars revitalise the international dynamic marketing capabilities (IDMCs) of firms by integrating advanced technologies that transform user interactions, improve engagement and facilitate knowledge acquisition, dissemination and usage across various sectors and business units globally. This integration promotes a dynamic approach to international brands, customer relationships and marketing knowledge management capabilities, offering profound value to users and firms.Research limitations/implicationsOur first limitation is a lack of diversity in data sources. As digital human avatars are an emerging field, we had to limit our study to 14 experts in AI and 10 podcasts. While this method provides deep insights into the perspectives of those directly involved in the development and implementation of digital human avatars, it may not capture the views of end-users or consumers who interact with these avatars, which can be an avenue for further research. Our second limitation is the potential bias in the interpretation of our interview data and podcasts. This study’s approach to data analysis, where themes are derived from the data itself, carries a risk of subjective interpretation by the researchers. Future studies are encouraged to investigate the impact of digital human avatars across different organisational contexts and ecosystems, especially focusing on how these technologies are integrated and perceived in various international markets.Practical implicationsThe novel framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.Social implicationsThe adoption of digital human avatars can alleviate loneliest elderly and vulnerable people by being a companion. The human-like characteristics can impact sense of presence and attachment.Originality/valueThe novelty of our study lies in exploring the characteristics of technologies and practical factors that maximise the successful adoption of digital human avatars. We advance and contribute to the emerging theory of avatar marketing, IDMCs and absorptive capacity by demonstrating how digital human avatars could be adopted as part of a firm’s global digital marketing strategy. We focus specifically on six dimensions: outcomes and benefits, enhancements and capabilities, applications and domains, future implications, foundational elements and challenges and considerations. This framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

Breeding distrust during artificial intelligence (AI) era: how technological advancements, job insecurity and job stress fuel organizational cynicism?

PurposeThis study examines how technological advancements and psychological capital contribute to job stress. Furthermore, the paper examines how job insecurity, job stress and job involvement influence the cynicism of recently laid-off employees. Despite various research studies, there is a lack of understanding of employees’ views on their work future and its probable influence on their job behaviors in this era of technology.Design/methodology/approachA quantitative method was used to collect a sample of 403 recently laid-off employees. The research tool of this study was a questionnaire, and the sampling technique was stratified random sampling. IBM SPSS and AMOS software were utilized to ensure the trustworthiness and accuracy of constructs via factor analysis. The proposed hypotheses were tested using structural equation modeling.FindingsThe analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism. Job involvement is negatively associated with employee’s cynicism.Practical implicationsThe current study adds to the comprehension of shifts in the perceived behavior of employees toward their organizations due to factors like the adoption of new technology in the organization, job stress, job insecurity and job involvement. Accordingly, there will be a need to form a favorable working atmosphere so that employees can perform their jobs with positive psychology and without any insecurity or stress.Originality/valueThe study is thought to contribute to the literature in terms of measuring organizational cynicism while layoffs continue due to AI advancements.

Learning for an uncertain future: artificial intelligence a challenge for Pakistani education system in the era of digital transformation

PurposeThe rapid advancement of AI in education brings both significant challenges and opportunities. In Pakistan, where the educational system is facing numerous issues, AI integration has raised significant concerns. Therefore, this study aims to explore the challenges AI poses to the Pakistani education system in the era of digital transformation.Design/methodology/approachA focus group was conducted with 13 participants, including educators and heads of departments from universities located in Rawalpindi and Islamabad. Thematic analysis was used to generate themes from the data.FindingsThe study findings indicated that AI integration in education can provide personalized and adaptive learning and teaching experiences by bridging the educational divide through easy access to quality education. Participants also highlighted the anxieties related to the digital divide as a great challenge for Pakistan. The integration of AI and automation in education is also raising concerns about job displacement for educators.Originality/valueIt highlighted that the integration of AI into the educational system can also bring both challenges and opportunities. To tackle these issues, potential strategies such as professional development and training programs to develop AI literacy among educators need to be introduced.

Benefits and Risks of AI in Health Care: Narrative Review

Background The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. Objective This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Methods Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non–peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher’s Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. Results The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. Conclusions In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors

This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a start-up competition that current large language models can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for the key cognitive processes underlying SDM—search, representation, and aggregation. Our analysis suggests that AI has the potential to enhance the speed, quality, and scale of strategic analysis, while also enabling new approaches, like virtual strategy simulations. However, the ultimate impact on firm performance will depend on competitive dynamics as AI capabilities progress. We propose a framework connecting AI use in SDM to firm outcomes and discuss how AI may reshape sources of competitive advantage. We conclude by considering how AI could both support and challenge core tenets of the theory-based view of strategy. Overall, our work maps out an emerging research frontier at the intersection of AI and strategy. History: This paper has been accepted for the Strategy Science Special Issue on Theory-Based View. Funding: The authors are grateful to their collaborating organizations and to the University of Michigan, Bocconi University Junior Researchers’ Grant, and the INSEAD eLab Research Fund for financial support.

Analysis of public perceptions on the use of artificial intelligence in genomic medicine

Abstract Purpose Next generation sequencing has led to the creation of large pools of genomic data with analysis rather than data generation now the limiting factor. Artificial intelligence (AI) may be required to optimize the benefits of these data, but little is known about how the public feels about the use of AI in genomics. Methods We conducted focus groups with members of the Australian public. Participants were recruited via social media advertisements. We explored potential uses of AI in genomic medicine, the benefits, risks, and the possible social implications of its use. Results Participants (n = 34) largely felt comfortable with AI analysing their own genomic data and generally agreed about its benefits. Concerns were raised over data security, the potential for misdiagnosis, and bias AI may perpetuate. Many participants wanted checking mechanisms for when results were generated using AI. Conclusions The insights gained from these discussions help to understand public concerns around the use of AI in genomic medicine. Our findings can help to inform both policies around genomic AI and how to educate the public on its use. Graphical abstract

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.

Artificial intelligence in hospitality services: examining consumers’ receptivity to unmanned smart hotels

PurposeWith recent advances in artificial intelligence, the hospitality industry has introduced the concept of unmanned smart hotels staffed by service robots instead of human employees. Research is needed to understand consumers’ receptivity to such an innovation. This paper examines factors associated with consumers’ potential resistance to using automated service hotels via two sequential studies. Given that younger generations of consumers are typically early adopters of advanced technology and innovative services, our sampling approach focused on this consumer group.Design/methodology/approachTwo studies were conducted. Study 1 proposed and empirically tested a theoretical model. Results revealed that attitude, subjective norms and perceived behavioral control each positively influenced individuals’ intentions to use unmanned smart hotels. In Study 2, we further investigated aspects informing perceived security, a key variable in the use of unmanned smart hotels.FindingsFindings showed how people’s beliefs about unmanned smart hotels and security control assurances led to perceived security. These perceptions were shaped by perceived physical risks, privacy concerns, website design and hotel reputation. Overall, this research provides theoretical and practical implications for various stakeholders associated with unmanned smart hotels.Practical implicationsFindings of this study suggested that managers of unmanned smart hotels should design user-friendly, secure processes and offer comprehensive support resources to enhance customer experience and usage.Originality/valueThe findings provide a holistic understanding of consumers’ receptivity to unmanned smart hotels.

Artificial intelligence in assessing progression of age-related macular degeneration

AbstractThe human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related macular degeneration (AMD). Health care systems are confronted with an increasing burden with rising patient numbers accompanied by ongoing developments of therapeutic approaches. Concurrent advances in imaging modalities provide eye care professionals with a large amount of data for each patient. Furthermore, with continuous progress in therapeutics, there is an unmet need for reliable structural and functional biomarkers in clinical trials and practice to optimize personalized patient care and evaluate individual responses to treatment. A fast and objective solution is Artificial intelligence (AI), which has revolutionized assessment of AMD in all disease stages. Reliable and validated AI-algorithms can aid to overcome the growing number of patients, visits and necessary treatments as well as maximize the benefits of multimodal imaging in clinical trials. Therefore, there are ongoing efforts to develop and validate automated algorithms to unlock more information from datasets allowing automated assessment of disease activity and disease progression. This review aims to present selected AI algorithms, their development, applications and challenges regarding assessment and prediction of AMD progression.

Artificial Intelligence and Image Analysis-Assisted Diagnosis for Fibrosis Stage of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Ultrasonography: A Pilot Study

Purpose: Elastography increased the diagnostic accuracy of liver fibrosis. However, several challenges persist, including the widespread utilization of equipment, difficulties in measuring certain cases, and the influence of viscosity factors. A rough surface and a blunted hepatic margin have long been acknowledged as valuable characteristics indicative of hepatic fibrosis. The objective of this study was to conduct an image analysis and quantitative assessment of the contour of the sagittal section of the left lobe of the liver. Methods: Between February and October 2020, 486 consecutive outpatients underwent ultrasound examinations at our hospital. A total of 214 images were manually annotated by delineating the liver contour to create annotation images. U-Net was employed for liver segmentation, with the dataset divided into training (n = 128), testing (n = 42), and validation (n = 44) subsets. Additionally, 43 Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) cases with pathology data from between 2015 and 2020 were included. Segmentation was performed using the program developed in the first step. Subsequently, shape analysis was conducted using ImageJ. Results: Liver segmentation exhibited high accuracy, as indicated by Dice loss of 0.044, Intersection over Union of 0.935, and an F score of 0.966. The accuracy of the classification of the liver surface as smooth or rough via ResNet 50 was 84.6%. Image analysis showed MinFeret and Minor correlated with liver fibrosis stage (p = 0.046, 0.036, respectively). Sensitivity, specificity, and AUROC of Minor for ≥F3 were 0.571, 0.862, and 0.722, respectively, and F4 were 1, 0.600, and 0.825, respectively. Conclusion: Deep learning segmentation of the sagittal cross-sectional contour of the left lobe of the liver demonstrated commendable accuracy. The roughness of the liver surface was correctly judged by artificial intelligence. Image analysis showed the thickness of the left lobe inversely correlated with liver fibrosis stage.

Exploring Changing Practices and Influence of Artificial Inteligence on Modern Journalism

Journalism in the rapidly evolving modern world is undergoing significant transformation due to the rise of artificial intelligence applications. The study explores the impact of artificial intelligence (AI) on modern journalism, including its transformative effects on news production, distribution, and consumption. This study explores the intricate relationship between AI applications and evolving journalism practices. Through an analysis of industry trends and case studies, we have identified the multidimensional impact of the applications on newsrooms, information collections, and reporting. We have highlighted both the opportunities and challenges brought about by this technologıcal paradigm shift in the field. The study aims to offer a detailed comprehension of the developing relationship between artificial intelligence (AI) applications and contemporary journalism. The results provide significant insights for scholars, practitioners, and stakeholders concerning the dynamic intersection of technology and media.

Objective classification for solid hydrometeor particles using deep learning

AbstractVarious small particles are present in the clouds. Hydrometeor videosonde (HYVIS) is an in situ instrument to observe such particles. The movies were used to capture hydrometeors with approximately 110,000 frames per sounding. When the particles in such movies were manually classified and measured sizes of particles for every several frames, it took an unrealistically long time to perform statistical analysis because of the large number of observed frames. Particle classification is subjective for the observers. This study developed a technique to classify cloud particles objectively using deep learning to overcome these problems and investigated the statistical microphysical characteristics of clouds. This study used the deep learning method You Only Looking Once for detection and classification. The training data were obtained using HYVIS in the Republic of Palau in 2013. The results trained using only HYVIS images showed low validity because some types of particles in the training data were insufficient. The data for typical particle shapes were augmented to improve the classification. Thus, the validity of classification using augmented data was improved. We used the Yonaguni Island HYVIS observation results from manual and artificial intelligence (AI) classification. The AI tended to classify the less irregular type than the manual, but no other significant differences were found. We believe this AI classification system will be for cloud microphysical studies on solid-phase particles.

Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia

BACKGROUND AND OBJECTIVES: Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heterogeneous clinical and radiological presentations that offer challenges related to diagnosis and management. Artificial intelligence (AI) techniques have been used in other fields of medicine to identify different phenotypic clusters that guide clinical care. In this study, we use a novel, combined data-driven and clinician input feature selection process and AI clustering to differentiate presenting phenotypes of CM1 + SM. METHODS: A total of 1340 patients with CM1 + SM in the Park Reeves Syringomyelia Research Consortium registry were split a priori into internal and external cohorts by site of enrollment. The internal cohort was used for feature selection and clustering. Features with high Laplacian scores were identified from preselected groups of clinically relevant variables. An expert clinician survey further identified features for inclusion that were not selected by the data-driven process. RESULTS: The feature selection process identified 33 features (28 from the data-driven process and 5 from the clinician survey) from an initial pool of 582 variables that were incorporated into the final model. A K-modes clustering algorithm was used to identify an optimum of 3 clusters in the internal cohort. An identical process was performed independently in the external cohort with similar results. Cluster 1 was defined by older CM1 diagnosis age, small syringes, lower tonsil position, more headaches, and fewer other comorbidities. Cluster 2 was defined by younger CM1 diagnosis age, more bulbar symptoms and hydrocephalus, small syringes, more congenital medical issues, and more previous neurosurgical procedures. Cluster 3 was defined by largest syringes, highest prevalence of spine deformity, fewer headaches, less tonsillar ectopia, and more motor deficits. CONCLUSION: This is the first study that uses an AI clustering algorithm combining a data-driven feature selection process with clinical expertise to identify different presenting phenotypes of CM1 + SM.

Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence

Agriculture is dealing with numerous challenges of increasing production while decreasing the amount of chemicals and fertilizers used. The intensification of agricultural systems has been linked to the use of these inputs which nevertheless have negative consequences for the environment. With new technologies, and progress in precision agriculture associated with decision support systems for farmers, the objective is to optimize their use. This review focused on the progress made in utilizing machine learning and remote sensing to detect and identify crop diseases that may help farmers to (i) choose the right treatment, the most adapted to a particular disease, (ii) treat diseases at early stages of contamination, and (iii) maybe in the future treat only where it is necessary or economically profitable. The state of the art has shown significant progress in the detection and identification of disease at the leaf scale in most of the cultivated species, but less progress is done in the detection of diseases at the field scale where the environment is complex and applied only in some field crops.

Comparative analysis of AI-based search algorithms in solving 8 puzzle problems

Abstract Background The 8-puzzle problem is a well-known combinatorial search problem, often used to test the effectiveness of various artificial intelligence (AI) algorithms. This study aims to evaluate the performance of three AI-based search algorithms—Breadth-First Search (BFS), Depth-First Search (DFS), and A* Search—in solving the 8-puzzle problem. The algorithms were implemented in Java, and their performance was measured in terms of solution length, the number of expanded nodes, and execution time. Results Experiments were conducted on eight randomly generated instances of the 8-puzzle problem. The findings reveal that both BFS and A* Search consistently identify optimal solutions in all cases. However, BFS was found to use more memory and operate slower than A* Search. Conversely, DFS demonstrated faster execution times and lower memory usage compared to BFS but was less reliable in finding optimal or feasible solutions. The study also explored the impact of different heuristic functions on the performance of A* Search. Among the heuristics tested, the Manhattan distance heuristic was determined to be the most effective, offering the best balance between accuracy and efficiency. Conclusions In conclusion, both BFS and A* Search are effective in finding optimal solutions, with A* Search being more efficient in terms of speed and memory usage. DFS, while faster and more memory-efficient, is less consistent in producing optimal solutions. The Manhattan distance heuristic significantly enhances the performance of A* Search, making it the preferred heuristic for the 8-puzzle problem. These results suggest that A* Search, particularly with the Manhattan distance heuristic, is a highly efficient choice for solving the 8-puzzle problem, especially in contexts where computational resources are limited.

QuPath Edu and OpenMicroanatomy: Open‐source virtual microscopy tools for medical education

AbstractVirtual microscopy is becoming increasingly common in both medical education and routine clinical practice. Virtual microscopy software is typically designed either for (1) training students in anatomy, histology, and histopathology, or (2) quantitative analysis—but not both simultaneously. QuPath is one of the most widely used software applications for histopathology image analysis in research and provides a comprehensive set of computational tools to evaluate histology slides. We have enhanced QuPath by developing a new extension, QuPath Edu, which adapts the software to function as an intuitive microanatomy learning environment. Additionally, we have created an entirely new, complementary software platform called OpenMicroanatomy, which provides an alternative way to access QuPath Edu teaching content through a web interface. These tools have been used in teaching of first year medical and dentistry students at the University of Oulu Medical Faculty, and we conducted a user survey for the Class of 2023 to assess the usability and student experience. In general, the introduced annotation and quiz features were appreciated by the students and the system usability of OpenMicroanatomy was considered excellent (SUS score 84.8). Together, these freely available tools enable teachers to develop and deploy innovative training material for anatomy, histopathology, quantitative analysis, and artificial intelligence in a wide range of contexts. This unique combination can provide the next generation of students with essential multidisciplinary skills.