Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2025 12 22 When Scientometric Metrics Become the Goal of Research When Scientometric Metrics Become the Goal of Research 33326 10.48305/him.2026.46314.1402 FA نامه Name Isfahan University of Medical Sciences 0000-0002-1904-0999 Ghasem Zarei 1- مرکز تحقیقات فیزیولوژی کاربردی، پژوهشکده تحقیقاتی قلب و عروق، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران. Elham Shahzeidi دانشجوی دکترای فلسفه تعلیم تربیت، گروه علوم تربیتی، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، اصفهان، ایران. Journal Article 2026 05 23 Criticizing indexation does not mean completely rejecting scientometric indicators. If used correctly, these indicators can be useful tools for analyzing patterns of science production and helping research policy-making. The main issue is when these tools replace scientific judgment and qualitative assessment. A balanced strategy is to consider quantitative indicators alongside criteria such as clinical effectiveness, impact on health policies, solving local problems, and the extent of scientific collaboration. Ultimately, it seems that the challenge of indexation in the life sciences should be seen as a symptom of a deeper problem: The crisis of meaning and responsibility in scientific activity. Returning to the social mission of science requires reforming evaluation systems, strengthening the culture of research integrity, and emphasizing the teaching of professional ethics alongside technical skills. In this regard, the role of professors as behavioral models is of particular importance, because scientific culture is transmitted through practical models rather than being shaped through regulations. If prominent researchers can demonstrate that reaching the frontiers of knowledge and responding to the real needs of society are aligned goals, perhaps the path of science can be guided from a mere competition for indicators to a real impact on human lives. Criticizing indexation does not mean completely rejecting scientometric indicators. If used correctly, these indicators can be useful tools for analyzing patterns of science production and helping research policy-making. The main issue is when these tools replace scientific judgment and qualitative assessment. A balanced strategy is to consider quantitative indicators alongside criteria such as clinical effectiveness, impact on health policies, solving local problems, and the extent of scientific collaboration. Ultimately, it seems that the challenge of indexation in the life sciences should be seen as a symptom of a deeper problem: The crisis of meaning and responsibility in scientific activity. Returning to the social mission of science requires reforming evaluation systems, strengthening the culture of research integrity, and emphasizing the teaching of professional ethics alongside technical skills. In this regard, the role of professors as behavioral models is of particular importance, because scientific culture is transmitted through practical models rather than being shaped through regulations. If prominent researchers can demonstrate that reaching the frontiers of knowledge and responding to the real needs of society are aligned goals, perhaps the path of science can be guided from a mere competition for indicators to a real impact on human lives.

Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2025 12 22 Leveraging AI Predictive Models and Genomic Biomarkers in Prostate Cancer Management Leveraging AI Predictive Models and Genomic Biomarkers in Prostate Cancer Management 33325 10.48305/him.2026.46294.1398 FA نامه Name Isfahan University of Medical Sciences 0000-0002-1904-0999 Hossein Taheri 1 – گروه فیزیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران 2 – مرکزتحقیقات فیزیولوژی کاربردی، پژوهشکده قلب و عروق، دا Mohammad Bagher Tavakoli گروه فیزیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Hossein Salehi گروه علوم تشریحی و بیولوژی تولیدمثل، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Simin Hemati گروه رادیوآنکولوژی، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Hamed Taheri گروه علوم تشریحی و بیولوژی تولیدمثل، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران Journal Article 2026 05 16 Prostate cancer is one of the major challenges facing healthcare systems and requires the achievement of personalized medicine for men in order to properly address treatment response heterogeneity and biochemical recurrence rates. Drawing on the results of applying advanced machine learning models and deep neural networks such as DeepSurv, this policy brief explains the potential of radiogenomics in accurately predicting treatment outcomes. Evidence indicates that the simultaneous use of quantitative features extracted from MRI images and key genetic biomarkers, including Ki-67, PTEN, and the Decipher index, provides a valuable opportunity for non-invasive prediction of disease recurrence with high accuracy. Focusing on performance indicators, the findings of this study confirm that replacing or augmenting conventional diagnostic methods with interpretable AI tools can enable intelligent classification of patients into high-risk and low-risk groups before the initiation of radiotherapy. Implementing this approach at the level of macro health policy can lead to a reduction in unnecessary treatments, better management of side effects, and optimized financial resources. Moreover, by enabling adaptive and personalized radiotherapy, it can significantly improve survival rates and patients’ quality of life. In this regard, three policy strategies were proposed and analyzed: “defining radiogenomics in screening protocols,” “personalizing radiotherapy dose based on DeepSurv risk,” and “establishing a national prostate radiogenomics data network.” Ultimately, this document emphasizes the need to strengthen clinical decision-making and employ intelligent decision support systems to achieve treatment equity and efficient cancer management. Prostate cancer is one of the major challenges facing healthcare systems and requires the achievement of personalized medicine for men in order to properly address treatment response heterogeneity and biochemical recurrence rates. Drawing on the results of applying advanced machine learning models and deep neural networks such as DeepSurv, this policy brief explains the potential of radiogenomics in accurately predicting treatment outcomes. Evidence indicates that the simultaneous use of quantitative features extracted from MRI images and key genetic biomarkers, including Ki-67, PTEN, and the Decipher index, provides a valuable opportunity for non-invasive prediction of disease recurrence with high accuracy. Focusing on performance indicators, the findings of this study confirm that replacing or augmenting conventional diagnostic methods with interpretable AI tools can enable intelligent classification of patients into high-risk and low-risk groups before the initiation of radiotherapy. Implementing this approach at the level of macro health policy can lead to a reduction in unnecessary treatments, better management of side effects, and optimized financial resources. Moreover, by enabling adaptive and personalized radiotherapy, it can significantly improve survival rates and patients’ quality of life. In this regard, three policy strategies were proposed and analyzed: “defining radiogenomics in screening protocols,” “personalizing radiotherapy dose based on DeepSurv risk,” and “establishing a national prostate radiogenomics data network.” Ultimately, this document emphasizes the need to strengthen clinical decision-making and employ intelligent decision support systems to achieve treatment equity and efficient cancer management.

Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2026 12 22 Graphical Modeling and Analysis for Discovering Hidden Relationships between Diseases and Symptoms: A Data-driven Approach for Differential Diagnosis and Symptom Co-occurrence Graphical Modeling and Analysis for Discovering Hidden Relationships between Diseases and Symptoms: A Data-driven Approach for Differential Diagnosis and Symptom Co-occurrence 33297 10.48305/him.2026.45792.1346 FA Sepideh Ghaffari Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran 0009-0008-3653-0093 Roghaye Khasha Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran 0000-0002-7460-0549 Journal Article 2025 10 12 Abstract Introduction: Understanding hidden relationships between diseases and symptoms is one of the fundamental challenges in differential diagnosis and comorbidity analysis in medicine. Given the complexity of clinical interactions, identifying shared patterns among diseases can play a crucial role in improving the diagnostic process and clinical decision-making. This study proposes a data-driven, graph-based analytical framework for modeling and uncovering hidden relationships between diseases and symptoms. Methods: This study utilized the publicly available Disease–Symptom Prediction Dataset from the Kaggle platform, consisting of 4920 records covering 41 diseases and 131 unique symptoms. Using a linear combination of occurrence frequency indices and the kappa coefficient, a weighted bipartite disease–symptom graph was constructed. Disease–disease and symptom–symptom unipartite graphs were then extracted, and their network structures were analyzed using the Louvain, Greedy Modularity, and Girvan–Newman clustering algorithms. Results: The results indicated that the symptom fatigue and the disease dengue fever played a key role in the disease–symptom network. Disease clustering using the Louvain and Greedy Modularity algorithms identified meaningful clusters of related diseases. Symptom clustering with the Louvain algorithm revealed six clinically interpretable clusters, which could be useful for rapid disease identification. Moreover, seemingly unrelated symptoms could be associated with a common disease, while specific symptom clusters can guide the diagnosis of particular diseases. Conclusion: The findings indicate that graph-based analysis can serve as an effective tool for uncovering hidden relationships between diseases and symptoms and can play an important role in improving differential diagnosis and designing intelligent decision-support systems. Abstract Introduction: Understanding hidden relationships between diseases and symptoms is one of the fundamental challenges in differential diagnosis and comorbidity analysis in medicine. Given the complexity of clinical interactions, identifying shared patterns among diseases can play a crucial role in improving the diagnostic process and clinical decision-making. This study proposes a data-driven, graph-based analytical framework for modeling and uncovering hidden relationships between diseases and symptoms. Methods: This study utilized the publicly available Disease–Symptom Prediction Dataset from the Kaggle platform, consisting of 4920 records covering 41 diseases and 131 unique symptoms. Using a linear combination of occurrence frequency indices and the kappa coefficient, a weighted bipartite disease–symptom graph was constructed. Disease–disease and symptom–symptom unipartite graphs were then extracted, and their network structures were analyzed using the Louvain, Greedy Modularity, and Girvan–Newman clustering algorithms. Results: The results indicated that the symptom fatigue and the disease dengue fever played a key role in the disease–symptom network. Disease clustering using the Louvain and Greedy Modularity algorithms identified meaningful clusters of related diseases. Symptom clustering with the Louvain algorithm revealed six clinically interpretable clusters, which could be useful for rapid disease identification. Moreover, seemingly unrelated symptoms could be associated with a common disease, while specific symptom clusters can guide the diagnosis of particular diseases. Conclusion: The findings indicate that graph-based analysis can serve as an effective tool for uncovering hidden relationships between diseases and symptoms and can play an important role in improving differential diagnosis and designing intelligent decision-support systems.

Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2026 12 22 An Improved VGG Based Deep Learning Framework for Glioma Classification in Brain MRI Images An Improved VGG Based Deep Learning Framework for Glioma Classification in Brain MRI Images 33298 10.48305/him.2026.45796.1348 FA Asieh Khosravanian Department of Computer Engineering, University of Larestan, Lar, Iran 0000-0002-2436-9756 Journal Article 2025 10 13 Introduction: Early detection of brain tumors can play a significant role in treatment planning. In brain magnetic resonance imaging (MRI), there are considerable similarities between healthy and cancerous tissues, making accurate tumor detection a major challenge. Furthermore, manual classification of these images is time‑consuming and prone to human error; therefore, developing automated classification methods can provide substantial support to physicians. Methods: In this study, a novel deep neural network architecture is proposed for automatic classification of brain tumors in MRI images. In the proposed method, the VGG‑16 deep neural network architecture has been redesigned. To preserve the spatial resolution of features in deeper layers and to enable detection of small tumor regions as well as identification of ambiguous boundaries, the max‑pooling layer in the original VGG‑16 architecture was removed. Results: The proposed method was evaluated using the BraTS2020 dataset with a 10‑fold cross‑validation approach. The evaluation results, based on the metrics Accuracy, Precision, Recall, Specificity, F1 Score, and AUC, showed that the proposed architecture achieved better performance compared to the original VGG‑16 neural network. Specifically, it attained values 0.9512 ± 0.0035, 0.9739 ± 0.0026, 0.9524 ± 0.0044, 0.9490 ± 0.0051, 0.9630 ± 0.0027, and 0.9514 ± 0.0044, respectively, for the mentioned metrics. Conclusion: The results obtained from the proposed method in this study confirm that preserving spatial information and identifying ambiguous boundaries can play a significant role in improving classification accuracy. In addition, the applied modifications led to faster convergence and reduced over‑downsampling. Introduction: Early detection of brain tumors can play a significant role in treatment planning. In brain magnetic resonance imaging (MRI), there are considerable similarities between healthy and cancerous tissues, making accurate tumor detection a major challenge. Furthermore, manual classification of these images is time‑consuming and prone to human error; therefore, developing automated classification methods can provide substantial support to physicians. Methods: In this study, a novel deep neural network architecture is proposed for automatic classification of brain tumors in MRI images. In the proposed method, the VGG‑16 deep neural network architecture has been redesigned. To preserve the spatial resolution of features in deeper layers and to enable detection of small tumor regions as well as identification of ambiguous boundaries, the max‑pooling layer in the original VGG‑16 architecture was removed. Results: The proposed method was evaluated using the BraTS2020 dataset with a 10‑fold cross‑validation approach. The evaluation results, based on the metrics Accuracy, Precision, Recall, Specificity, F1 Score, and AUC, showed that the proposed architecture achieved better performance compared to the original VGG‑16 neural network. Specifically, it attained values 0.9512 ± 0.0035, 0.9739 ± 0.0026, 0.9524 ± 0.0044, 0.9490 ± 0.0051, 0.9630 ± 0.0027, and 0.9514 ± 0.0044, respectively, for the mentioned metrics. Conclusion: The results obtained from the proposed method in this study confirm that preserving spatial information and identifying ambiguous boundaries can play a significant role in improving classification accuracy. In addition, the applied modifications led to faster convergence and reduced over‑downsampling.

Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2026 12 22 Strategies to improve the implementation of patient safety-friendly hospital standards in Iran, a mixed-method study Strategies to improve the implementation of patient safety-friendly hospital standards in Iran, a mixed-method study 33296 10.48305/him.2026.45901.1360 FA Mohammadreza Tavakoli بیمارستان الزهرا، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران 0000-0001-7162-058X Mohammadreza Amiresmaili Professor, Health Services Management, Health Services Management Research Center, Institute for Future Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Mahmood Nekoeimohgadam Professor, Health Services Management, Health Services Management Research Center, Institute for Future Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Reza Kazemi Department of ..orology....., Al-Zahra Hospital, Isfahan University of Medical Sciences, Isfahan, Iran Journal Article 2025 12 07 Background and Objective: The Patient Safety Friendly Hospitals Program includes the implementation of a set of patient safety standards in hospitals. The implementation of these standards ensures that patient safety is accepted as an essential priority in hospitals and that staff from all units are working towards this goal. The purpose of this study was to investigate strategies for improving the implementation of patient safety friendly hospital standards in Iranian hospitals. Methodology: This study was conducted in a sequential manner. First, strategies for improving patient safety friendly hospital standards were identified by conducting a qualitative study, then the strategies were approved or rejected by experts using a two-stage fuzzy Delphi method. Findings: In this study, 30 main issues were identified as strategies for improving the implementation of patient safety friendly hospital standards, of which one strategy was eliminated by the experts and 29 main issues were approved. Conclusion: It is better to implement the strategies presented in this study and achieve the goal of improving the quality of services provided and better implementation of patient safety standards in centers to make the implementation of standards as effective as possible. Background and Objective: The Patient Safety Friendly Hospitals Program includes the implementation of a set of patient safety standards in hospitals. The implementation of these standards ensures that patient safety is accepted as an essential priority in hospitals and that staff from all units are working towards this goal. The purpose of this study was to investigate strategies for improving the implementation of patient safety friendly hospital standards in Iranian hospitals. Methodology: This study was conducted in a sequential manner. First, strategies for improving patient safety friendly hospital standards were identified by conducting a qualitative study, then the strategies were approved or rejected by experts using a two-stage fuzzy Delphi method. Findings: In this study, 30 main issues were identified as strategies for improving the implementation of patient safety friendly hospital standards, of which one strategy was eliminated by the experts and 29 main issues were approved. Conclusion: It is better to implement the strategies presented in this study and achieve the goal of improving the quality of services provided and better implementation of patient safety standards in centers to make the implementation of standards as effective as possible.

Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2026 12 22 Analyzing Research Trends in the Field of Artificial Intelligence and Health Literacy: A Scientometric Approach Analyzing Research Trends in the Field of Artificial Intelligence and Health Literacy: A Scientometric Approach 33295 10.48305/him.2026.45888.1359 FA Seifallah Andayesh Assistant Professor, knowledge and information science , Persian Gulf University, Bushehr, Iran. 0000-0002-0095-4272 Journal Article 2025 11 14 Objective: This study aims to analyze the knowledge structure, scientific trends, and thematic evolution of research on artificial intelligence and health literacy using a scientometric approach. Methods: This study was conducted using a bibliometric approach. Data were retrieved from the Scopus database without time restrictions. A total of 2,864 documents published between 1983 and 2025 were selected for analysis. The data were imported into Bibliometrix software. Results: The findings indicate that this field has experienced sustained and accelerated growth with an annual growth rate of 4.95%. The scientific collaboration network shows that the United States, with the highest number of links, is at the center of the network. The co-occurrence map identified four main themes: health education and language models, clinical applications, methodology and technical assessment, and mental health. Thematic evolution analysis revealed three distinct phases: expert systems (1983-2005), digital technologies and modern infrastructures (2005-2018), and generative AI and large language models (2018-2024). In the strategic diagram, the combination of "AI-ChatGPT-health literacy" was identified as a motor theme with high centrality and impact. Conclusion: The field of AI and health literacy has evolved from expert systems to large language models, and the integration of ChatGPT with health literacy has been identified as a strategic research front. Keywords: Artificial Intelligence, Health Literacy, Large Language Models, Health Education Key Message: AI in healthcare has evolved from expert systems to large language models. Advancing this emerging field at the intersection of health literacy requires a comprehensive approach to ethics, interdisciplinary collaboration, and modern infrastructure. Objective: This study aims to analyze the knowledge structure, scientific trends, and thematic evolution of research on artificial intelligence and health literacy using a scientometric approach. Methods: This study was conducted using a bibliometric approach. Data were retrieved from the Scopus database without time restrictions. A total of 2,864 documents published between 1983 and 2025 were selected for analysis. The data were imported into Bibliometrix software. Results: The findings indicate that this field has experienced sustained and accelerated growth with an annual growth rate of 4.95%. The scientific collaboration network shows that the United States, with the highest number of links, is at the center of the network. The co-occurrence map identified four main themes: health education and language models, clinical applications, methodology and technical assessment, and mental health. Thematic evolution analysis revealed three distinct phases: expert systems (1983-2005), digital technologies and modern infrastructures (2005-2018), and generative AI and large language models (2018-2024). In the strategic diagram, the combination of "AI-ChatGPT-health literacy" was identified as a motor theme with high centrality and impact. Conclusion: The field of AI and health literacy has evolved from expert systems to large language models, and the integration of ChatGPT with health literacy has been identified as a strategic research front. Keywords: Artificial Intelligence, Health Literacy, Large Language Models, Health Education Key Message: AI in healthcare has evolved from expert systems to large language models. Advancing this emerging field at the intersection of health literacy requires a comprehensive approach to ethics, interdisciplinary collaboration, and modern infrastructure.

Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2025 12 22 Investigating the Relationship Between Digital Skills and Information Entrepreneurship Intention Among Medical Library and Information Science Students Investigating the Relationship Between Digital Skills and Information Entrepreneurship Intention Among Medical Library and Information Science Students 33327 10.48305/him.2026.45802.1349 FA Leila Fakher Dizavandi Master's student, Medical Library and Information Science, Faculty of Medical Management and Information Science, Kerman University of Medical Sciences, Kerman, Iran 0009-0000-2487-2493 Journal Article 2025 10 13 This study investigated the relationship between digital skills (digital literacy and digital fluency) and information entrepreneurial intention among students of Medical Librarianship and Information Science in Iran. Recognizing that these future health information managers require advanced digital competencies to identify and capitalize on entrepreneurial opportunities, the research aimed to determine how these skills influence their entrepreneurial ambitions. A descriptive-analytical, cross-sectional study was conducted in the second semester of the 2024–2025 academic year. From a statistical population of 459 students across undergraduate, master’s, and doctoral levels, 210 participants were selected via stratified random sampling. Data were collected using a validated and reliable four-part questionnaire (Cronbach’s alpha = 0.964) measuring demographic information, information entrepreneurial intention (19 items), digital fluency (16 items), and digital literacy (31 items). The data were analyzed with SPSS 26 using descriptive statistics, Pearson correlation, and other inferential tests. Results showed that students possessed a high level of digital literacy (mean score 119.41 ± 22.56) but only a moderate level of digital fluency (57.49 ± 12.12). Their information entrepreneurial intention was also at a moderate level (64.19 ± 14.20). Critically, Pearson’s correlation revealed a positive and significant relationship between both digital literacy and entrepreneurial intention, and digital fluency and entrepreneurial intention. While significant gender and age-based differences were found in digital skills, no significant differences in entrepreneurial intention were observed based on gender, age, or academic level. The findings conclude that digital skills are a significant facilitating factor for entrepreneurial intention. The moderate levels of fluency and intention suggest a gap between theoretical knowledge and practical application. The absence of demographic influences on entrepreneurial intention implies it is shaped more by psychological and contextual factors. These results provide a foundation for developing targeted educational programs to foster an entrepreneurial culture within medical universities. This study investigated the relationship between digital skills (digital literacy and digital fluency) and information entrepreneurial intention among students of Medical Librarianship and Information Science in Iran. Recognizing that these future health information managers require advanced digital competencies to identify and capitalize on entrepreneurial opportunities, the research aimed to determine how these skills influence their entrepreneurial ambitions. A descriptive-analytical, cross-sectional study was conducted in the second semester of the 2024–2025 academic year. From a statistical population of 459 students across undergraduate, master’s, and doctoral levels, 210 participants were selected via stratified random sampling. Data were collected using a validated and reliable four-part questionnaire (Cronbach’s alpha = 0.964) measuring demographic information, information entrepreneurial intention (19 items), digital fluency (16 items), and digital literacy (31 items). The data were analyzed with SPSS 26 using descriptive statistics, Pearson correlation, and other inferential tests. Results showed that students possessed a high level of digital literacy (mean score 119.41 ± 22.56) but only a moderate level of digital fluency (57.49 ± 12.12). Their information entrepreneurial intention was also at a moderate level (64.19 ± 14.20). Critically, Pearson’s correlation revealed a positive and significant relationship between both digital literacy and entrepreneurial intention, and digital fluency and entrepreneurial intention. While significant gender and age-based differences were found in digital skills, no significant differences in entrepreneurial intention were observed based on gender, age, or academic level. The findings conclude that digital skills are a significant facilitating factor for entrepreneurial intention. The moderate levels of fluency and intention suggest a gap between theoretical knowledge and practical application. The absence of demographic influences on entrepreneurial intention implies it is shaped more by psychological and contextual factors. These results provide a foundation for developing targeted educational programs to foster an entrepreneurial culture within medical universities.

Isfahan University of Medical Sciences Health Information Management 1735-7853 22 Number 4 (Winter) 2026 12 22 The Experience of Holding the First Medis Smart Health Hackathon with a Look at Achievements, Challenges, and Lessons Learned The Experience of Holding the First Medis Smart Health Hackathon with a Look at Achievements, Challenges, and Lessons Learned 33182 10.48305/him.2026.46034.1378 FA نامه Name Isfahan University of Medical Sciences 0000-0002-1904-0999 Maryam Jahanbakhsh Associate Professor of Health Information Management, Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Health Information Technology Research Center, Isfahan University of Medical Scie 0000-0003-0876-5422 Journal Article 2025 12 22 The purpose of this experience statement is to review the planning process, implementation, and results of the first Medis Smart Health Hackathon, which was held at Isfahan University of Medical Sciences with a focus on three topics: “Digital Health in Chronic Diseases,” “Health Data Management with Artificial Intelligence,” and “Biocomputing in the Development of Smart Medicines.” The event was held with the participation of 23 interdisciplinary student teams from medical, engineering, and basic sciences. The most important achievements of the event included creating innovative ideas in the field of health technology, strengthening teamwork and interdisciplinary communication skills, creating scientific-professional networks, and motivating students to work at the frontiers of knowledge. However, the event faced major challenges, especially in the area of providing financial resources and administrative obstacles, which affected the quality of implementation and support. However, despite the effectiveness of such events in fostering innovation and operational skills, their continuity and development require more structured organizational support, streamlining administrative processes, and designing sustainable mechanisms for communication with industry and financial sponsors. This experience emphasizes the need to integrate interdisciplinary education and support innovative student ideas in line with the transformation of the health system. The purpose of this experience statement is to review the planning process, implementation, and results of the first Medis Smart Health Hackathon, which was held at Isfahan University of Medical Sciences with a focus on three topics: “Digital Health in Chronic Diseases,” “Health Data Management with Artificial Intelligence,” and “Biocomputing in the Development of Smart Medicines.” The event was held with the participation of 23 interdisciplinary student teams from medical, engineering, and basic sciences. The most important achievements of the event included creating innovative ideas in the field of health technology, strengthening teamwork and interdisciplinary communication skills, creating scientific-professional networks, and motivating students to work at the frontiers of knowledge. However, the event faced major challenges, especially in the area of providing financial resources and administrative obstacles, which affected the quality of implementation and support. However, despite the effectiveness of such events in fostering innovation and operational skills, their continuity and development require more structured organizational support, streamlining administrative processes, and designing sustainable mechanisms for communication with industry and financial sponsors. This experience emphasizes the need to integrate interdisciplinary education and support innovative student ideas in line with the transformation of the health system.