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Scopus Q: Q1Subject: Artificial Intelligence

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    Review
    Citation - WoS: 247
    Citation - Scopus: 446
    Transformative Effects of Iot, Blockchain and Artificial Intelligence on Cloud Computing: Evolution, Vision, Trends and Open Challenges
    (Elsevier, 2019) Gill, Sukhpal Singh; Tuli, Shreshth; Xu, Minxian; Singh, Inderpreet; Singh, Karan Vijay; Lindsay, Dominic; Garraghan, Peter
    Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies' interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing. (C) 2019 Elsevier B.V. All rights reserved.
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    Review
    Citation - WoS: 6
    Citation - Scopus: 9
    Monkeypox: a Comprehensive Review of Virology, Epidemiology, Transmission, Diagnosis, Prevention, Treatment, and Artificial Intelligence Applications
    (Shaheed Beheshti University of Medical Sciences and Health Services, 2024) Rahmani,E.; Bayat,Z.; Farrokhi,M.; Karimian,S.; Zahedpasha,R.; Sabzehie,H.; Farrokhi,M.
    Monkeypox (Mpox), an uncommon zoonotic Orthopoxvirus, is commonly manifested by blisters on the skin and has a mortality rate of approximately 0-10%. Approximately two decades after the cessation of global smallpox vaccination, the number of confirmed cases of Mpox has been growing, making it the most common Orthopoxvirus infection. Therefore, in this narrative review, we aimed to shed light on recent advancements in the pathophysiology, transmission routes, epidemiology, manifestations, diagnosis, prevention, and treatment of Mpox, as well as the application of artificial intelligence (AI) methods for predicting this disease. The clinical manifestations of Mpox, including the onset of symptoms and dermatologic characteristics, are similar to those of the infamous smallpox, but Mpox is clinically milder. Notably, a key difference between smallpox and Mpox is the high prevalence of lymphadenopathy. Human-to-human, animal-to-human, and animal-to-animal transmission are the three main pathways of Mpox spread that must be considered for effective prevention, particularly during outbreaks. PCR testing, as the preferred method for diagnosing Mpox infection, can enhance early detection of new cases and thereby improve infection control measures. JYNNEOS and ACAM2000 are among the vaccines most commonly recommended for the prevention of Mpox. Brincidofovir, Cidofovir, and Tecovirimat are the primary treatments for Mpox cases. Similar to other viral infections, the best approach to managing Mpox is prevention. This can, in part, be achieved through measures such as reducing contact with individuals displaying symptoms, maintaining personal safety, and adhering to practices commonly used to prevent sexually transmitted infections. © This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).
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    Article
    Citation - WoS: 1
    Citation - Scopus: 2
    Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review
    (MDPI, 2025) Ayhan, Cagri; Mekhaeil, Marina; Channawi, Rita; Ozcan, Alp Eren; Akargul, Elif; Deger, Atakan; Soliman, Osama
    Acute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today's data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models-particularly ensemble algorithms and biomarker-integrated approaches-have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI's growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics-enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia-showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling-potentially through the development of digital twins-will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA.
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    Article
    Artificial Intelligence Based Resuscitation Simulation: A Pilot Study of a Novel Approach to Team Leadership Training
    (BMC, 2026) Kanbakan, Altug; Berikol, Goksu Bozdereli; Ilhan, Bugra; Altintas, Emel; Doganay, Fatih
    Introduction Team leadership training is essential alongside with technical training for effective resuscitation management. Addressing this gap, we developed a novel simulation system leveraging Large Language Models (LLMs) to create Artificial Intelligence (Al) agents simulating team members in Advanced Cardiovascular Life Support (ACLS) scenarios. This pilot study aimed to to develop a novel LLM-based ACLS simulation training platform and evaluate its performance in simulated resuscitation scenarios on established protocols.
    Method Using the Claude 3.5 Sonnet API, we designed a simulation system with four Al agents assigned specific roles as healthcare staff within an ACLS team. Each agent strictly followed the 2020 American Heart Association (AHA) ACLS guidelines while interacting with an ACLS certified emergency medicine specialist user. The ten patient scenario transcripts were evaluated with three blinded emergency medicine specialists whether all the recommended steps are completed. Inter-rater reliability was assessed using Kendall's W and Krippendorff's Alpha statistics to evaluate agreement both within raters and the model.
    Results Al agents consistently adhered to the AHA 2020 ACIS algorithm across scenarios, with a high inter-rater reliability (Kendall's W > 0.75 ) . Krippendorff's Alpha values for agreement ranged from substantial (0.84) to almost perfect (0.99), indicating robust compliance with guidelines and effective simulation of resuscitation responses.
    Conclusion This study highlights the potential of LL.M-powered simulations as an adjunct to traditional resuscitation training. The system effectively supported team leadership training by providing consistent and guideline-compliant responses. While the results are promising, further research with larger participant samples is necessary to evaluate the long-term educational impact and scalability of such systems.