eng دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 2026-03 12 4 298 314 article Nasser Shoeibi ShoeibiN@mums.ac.ir 1 Mojtaba Hashemi Hasani hashemihasani-sm@kaums.ac.ir 2 Arash Shirdel derakhshana@mums.ac.ir 3 Razieh Farrahi Farrahir1@gmail.com 4 Full Professor, MD in Ophthalmology, Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Ph.D. Student in Health Information Management, Kashan University of Medical Sciences, Kashan, Iran Assistant Professor, PhD in Healthcare Services Management, Department of Health Information Technology, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran Assistant Professor, PhD in Health Information Management, Department of Health Information Technology, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran Introduction: The unique characteristics of clinical workflows and ophthalmic data management processes necessitate a distinct design for ophthalmic electronic medical record (EMR) systems compared to those developed for other medical specialties. This study aimed to identify the functional requirements of an ophthalmic EMR system specifically tailored to the operational needs of ophthalmology hospitals and specialized eye-care clinics. Method: This descriptive cross-sectional study was conducted in 2019 at Khatam Al-Anbia Eye Hospital in Mashhad, Iran. Functional requirements for the ophthalmic EMR system were identified using a multimethod approach, which included brainstorming sessions, field surveys, literature reviews, semi-structured interviews, focus group discussions, and the Delphi method. Results: The identified functional requirements were categorized into four main domains: clinical documentation, physician order management, clinical results management, and reporting. Within the clinical documentation domain, the most important requirements were “creating electronic records for patients” (mean: 4.93) and “scanning and displaying previous outpatient records” (mean: 4.79). For physician order management, the highest-rated requirements were “scheduling surgical procedures within the system” and “tracking consultations and referrals” (mean: 4.53). In the clinical results management domain, the most important requirements included “recording laboratory test results in the patient record and enabling physician access to these results” and “capturing and storing special disease cases to establish ophthalmic disease registries”(mean: 4.67).Overall, clinical results management received the highest mean score (4.56), followed by clinical documentation (4.47), reporting (4.40), and physician order management (4.38), indicating the highest and lowest priorities among the identified functional requirements. Conclusion: Clinical results management has emerged as the highest-priority functionality for ophthalmic EMR systems. Ophthalmology is a high-volume specialty characterized by complex workflows and team-based patient care models. Timely diagnosis and effective treatment of ocular diseases heavily rely on a wide range of diagnostic and paraclinical investigations. Consequently, immediate access to clinical results is essential for enhancing the quality of care, enabling timely diagnosis, informed clinical decision-making, and early therapeutic interventions.   http://jhbmi.ir/article-1-970-en.pdf Electronic health record Health information system Functional requirement Eye diseases Ophthalmology per دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 2026-03 12 4 315 333 article Zahra Ahmadi Rad a.ahmadirad@vru.ac.ir 1 Hossein Ghayoumi Zadeh h.ghayoumizadeh@vru.ac.ir 2 Ali Fayazi a.fayazi@vru.ac.ir 3 Khosro Rezaee kh.rezaee@meybod.ac.ir 4 M.Sc. Student, Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran Ph.D. in Biomedical Engineering, Associate Professor, Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran Ph.D. in Control Engineering, Associate Professor, Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran Ph.D. in Biomedical Engineering, Associate Professor, Department of Biomedical Engineering, Meybod University, Meybod, Iran Introduction: Diabetic foot is a serious chronic complication of diabetes that can lead to ulcers and limb amputation if not diagnosed early. while non-invasive techniques like thermography can prevent severe complications, traditional methods often fail to identify subtle thermal changes.  This study developed an intelligent system for diabetic foot detection using thermographic images and numerical features. Method: The Plantar Thermogram Database, containing 167 samples, was divided into training (116 samples), validation (25 samples), and test (26 samples) sets. A multimodal Siamese architecture based on EfficientNetB3 processed left and right foot thermograms simultaneously.  Outputs were combined using ∣L−R∣ and L⊙R to model thermal asymmetry, then fused with normalized tabular features. Implementation used TensorFlow and Keras, with class weighting to address data imbalance. Results: The model achieved 96.15% accuracy, 95% sensitivity, 100% specificity, 100% precision, 85.71% NPV, 97.44% F1-score, and 97.5% balanced accuracy on the test set.  Learning curves showed rapid convergence with no overfitting and stable performance. Conclusion: The proposed Siamese architecture, utilizing both image and tabular data, offers a non-invasive, rapid, and accurate approach for screening and monitoring diabetic patients, potentially facilitating early diagnostic clinical systems for diabetic foot detection. http://jhbmi.ir/article-1-953-en.pdf Diabetic foot Thermography Siamese architecture Multimodal data Early detection EfficientNetB3 per دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 2026-03 12 4 334 347 article Masoumeh Ayeneh masomehayneh@gmail.com 1 Mahdi Hosseinpour M.hosseinpour@razi.ac.ir 2 Korush Veisi korushveisi1357@gmail.com 3 Ph.D. Student of Entrepreneurship, Department of Management and Entrepreneurship, Faculty of Social Sciences, Razi University, Kermanshah, Iran Ph.D. in Strategic Management, Assistant Professor, Department of ‎Interdisciplinary Sciences, Faculty of ‎Strategic Technologies, Razi University, Kermanshah, Iran M.S.c of Business Administration (Executive Management), Islamic Azad University, Kermanshah Branch, Kermanshah, Iran Introduction: At the heart of the digital revolution, a profound transformation is taking place, erasing the boundaries between technology, health, and daily life. Here lies the new realm of entrepreneurship, where the goal is not merely to build startups, but to create intelligent ecosystems that are deeply intertwined with human health. The driving force behind this shift is the emergence of wearable technology and digital health. Today, devices like smartwatches are no longer just for telling time; they have become guardians of our health, monitoring heart rate, sleep patterns, and stress levels. Method: At the heart of the digital revolution, a profound transformation is taking place, erasing the boundaries between technology, health, and daily life. Here lies the new realm of entrepreneurship, where the goal is not merely to build startups, but to create intelligent ecosystems that are deeply intertwined with human health. The driving force behind this shift is the emergence of wearable technology and digital health. Today, devices like smartwatches are no longer just for telling time; they have become guardians of our health, monitoring heart rate, sleep patterns, and stress levels. Results: The causal factors were identified as technological, environmental & market-related, organizational & entrepreneurial, and socio-cultural factors. Intervening conditions included legal & regulatory, economic & financial, and technical & infrastructural factors. Contextual conditions encompassed geographical & climatic, systemic & structural, knowledge-based & research, and industrial & manufacturing factors. The strategies consisted of technological & innovation strategies, commercial & market-oriented strategies, internationalization strategies, financial & investment strategies, and legal & regulatory strategies. The outcomes were categorized into three main types: the creation of new economic value, socio-behavioral transformations, and emergent & forward-looking factors. Conclusion: This research demonstrated that the integration of these technologies not only enables the provision of personalized healthcare solutions but also creates a novel framework for entrepreneurial business models. By leveraging the capabilities of real-time data analytics, artificial intelligence, and the Internet of Things (IoT), startups can respond to the growing market demands in the fields of disease prevention, diagnosis, and management. http://jhbmi.ir/article-1-963-en.pdf Entrepreneurship Wearable Technology Digital Health Grounded Theory Innovation per دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 2026-03 12 4 348 358 article Elahe Hoseinnia elahehoseinnia27@gmail.com 1 Mahboobeh Zarrabi mzarrabi@alzahra.ac.ir 2 Zahra Hajimahdi z.hajimahdi@sbmu.ac.ir 3 M.Sc. in Biophysics, Department of Biotechnology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran Associate Professor, Ph. D. in Biophysics, Department of Biotechnology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran Associate Professor, Ph. D. in Medicinal Chemistry, Department of Pharmaceutical Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran Introduction: Cancer cell membranes exhibit altered lipid composition and asymmetry, influencing tumor progression and drug resistance. These lipidomic changes can significantly influence membrane structure, biophysical properties, and permeability. This study aims to investigate the biophysical consequences of lipidomic changes in breast cell membranes during cancer development. Method: Normal and cancer breast membrane models were built using reported lipidomic compositions. The normal model had an asymmetric lipid distribution, while the cancer model was symmetric, with increased phosphatidylserine (PS) and phosphatidylethanolamine (PE) in the outer leaflet. Molecular dynamics simulations were conducted at all-atom (CHARMM36m,200 ns) and coarse-grained (Martini 2.2,5 µs) resolutions. Structural and dynamical parameters, including bilayer thickness, area per lipid, acyl chain interdigitation, order parameter, and lateral diffusion coefficient, were calculated and compared. Results: The cancer membrane showed increased thickness, reduced area per lipid, higher acyl chain order parameter, and lower lateral diffusion compared to the normal membrane, consistent with reduced chain interdigitation. Conclusion: Changes in cancer cell profiles lead to a more compact, rigid membrane with decreased lateral mobility. These physicochemical alterations may impact therapeutic permeability, suggesting that targeting the biophysical properties of cancer cell membranes could be a promising strategy for novel treatment.   http://jhbmi.ir/article-1-980-en.pdf All-Atom molecular dynamics Cancer Coarse-Grained simulation Lipid asymmetry Membrane per دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 2026-03 12 4 359 371 article Seyed Younes Hosseini youneshosein@gmail.com 1 Mahnaz Mohseni Mohseni92@pnu.ac.ir 2 Meisam Dastani meisam.dastani@gmail.com 3 MSc of Psychiatric Nursing, Department of Nursing, Tems.c, Islamic Azad University, Tehran, Iran Faculty Member, Department of Knowledge and Information science, Payame Noor University, Tehran, Iran PhD in Knowledge and Information Science (Knowledge and Information Retrieval), Infectious Diseases Research Center, Gonabad University of Medical Sciences, Gonabad, Iran Introduction: Advances in information and communication technologies have transformed healthcare delivery in recent years, positioning telemedicine as an effective approach to improving accessibility, reducing costs, and supporting continuous patient care. While researchers in Iran have shown increased interest in this field, policy, infrastructural, and cultural barriers persist. This study aimed to identify the trends and topics of telemedicine- related scientific production in Iran. Method: This literature review utilized text mining techniques. Data consisted of 267 scientific records by Iranian researchers, related to telemedicine, retrieved from the Scopus database on September 1, 2025. After preprocessing—including the removal of symbols, stop words, and other irrelevant elements—the data were analyzed. The Latent Dirichlet Allocation (LDA) algorithm was then used to identify the topics of these scientific productions. Results were visualized through word clouds, thematic clusters, and temporal trends. Results: The analysis revealed nine main thematic clusters. The largest share of publications was dedicated to “evaluation and usability of mobile health systems and applications.” Other clusters included policymaking and standardization, mobile health interventions, disease management and self-care, data security and privacy, and methodological development. Conclusion: The findings show that telemedicine-related scientific productions in Iran have grown significantly in recent years, shifting from a sole focus on clinical applications toward broader policy, technological, and managerial dimensions. Nonetheless, limited attention to areas such as data security, cultural acceptance, and equity in access highlights critical gaps that future studies should address. http://jhbmi.ir/article-1-960-en.pdf Telemedicine mHealth Text Mining Topic Modeling Iran per دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 2026-03 12 4 372 375 article Elaheh Shafiei e.shafiei.sh@gmail.com 1 Farzan Madadizadeh statlearningeasy@gmail.com 2 Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran http://jhbmi.ir/article-1-973-en.pdf Explainable AI Clinical Decision Support Systems Primary Health Care PHC per دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 2026-03 12 4 376 379 article Arefeh Ameri ameri1373@gmail.com 1 Farzad Salmanizadeh salmanifarzad@gmail.com 2 Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran The article is a Letter to the Editor; therefore, it does not include an abstract. http://jhbmi.ir/article-1-984-en.pdf Multimodal Artificial Intelligence Heterogeneous Medical Data Precision Clinical Decision-Making