AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Alibrahim, Y.; Ibrahim, M.; Gurdayal, D.; Munshi, M.
AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings Journal Article
In: Intelligence-Based Medicine, vol. 11, 2025, ISSN: 26665212 (ISSN).
Abstract | Links | BibTeX | Tags: 3D segmentation, AI speechbots, Article, artificial intelligence chatbot, ChatGPT, computer assisted tomography, Deep learning, headache, human, Image segmentation, interventional radiology, Large language model, Likert scale, nausea, Proof of concept, prospective study, radiology, radiology on call training, resource limited setting, Teaching, Training, ultrasound, Virtual Reality, voice recognition
@article{alibrahim_ai_2025,
title = {AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings},
author = {Y. Alibrahim and M. Ibrahim and D. Gurdayal and M. Munshi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001472313&doi=10.1016%2fj.ibmed.2025.100245&partnerID=40&md5=623a0ceaa07e5516a296420d25c3033b},
doi = {10.1016/j.ibmed.2025.100245},
issn = {26665212 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Intelligence-Based Medicine},
volume = {11},
abstract = {Objective: Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents. Methods: Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences. Results/discussion: Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time. Conclusion: AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise. © 2025},
keywords = {3D segmentation, AI speechbots, Article, artificial intelligence chatbot, ChatGPT, computer assisted tomography, Deep learning, headache, human, Image segmentation, interventional radiology, Large language model, Likert scale, nausea, Proof of concept, prospective study, radiology, radiology on call training, resource limited setting, Teaching, Training, ultrasound, Virtual Reality, voice recognition},
pubstate = {published},
tppubtype = {article}
}
Objective: Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents. Methods: Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences. Results/discussion: Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time. Conclusion: AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise. © 2025
2024
Chandrashekar, N. Donekal; Lee, A.; Azab, M.; Gracanin, D.
Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms Journal Article
In: Information (Switzerland), vol. 15, no. 12, 2024, ISSN: 20782489 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors
@article{donekal_chandrashekar_understanding_2024,
title = {Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms},
author = {N. Donekal Chandrashekar and A. Lee and M. Azab and D. Gracanin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213435167&doi=10.3390%2finfo15120814&partnerID=40&md5=134c43c7238bae4923468bc6e46c860d},
doi = {10.3390/info15120814},
issn = {20782489 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Information (Switzerland)},
volume = {15},
number = {12},
abstract = {In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.},
keywords = {Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors},
pubstate = {published},
tppubtype = {article}
}
In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.
Scott, A. J. S.; McCuaig, F.; Lim, V.; Watkins, W.; Wang, J.; Strachan, G.
Revolutionizing Nurse Practitioner Training: Integrating Virtual Reality and Large Language Models for Enhanced Clinical Education Proceedings Article
In: G., Strudwick; N.R., Hardiker; G., Rees; R., Cook; R., Cook; Y.J., Lee (Ed.): Stud. Health Technol. Informatics, pp. 671–672, IOS Press BV, 2024, ISBN: 09269630 (ISSN); 978-164368527-4 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, 3D models, 3d-modeling, adult, anamnesis, clinical decision making, clinical education, Clinical Simulation, Computational Linguistics, computer interface, Computer-Assisted Instruction, conference paper, Curriculum, Decision making, E-Learning, Education, Health care education, Healthcare Education, human, Humans, Language Model, Large language model, large language models, Mesh generation, Model animations, Modeling languages, nurse practitioner, Nurse Practitioners, Nursing, nursing education, nursing student, OSCE preparation, procedures, simulation, Teaching, therapy, Training, Training program, User-Computer Interface, Virtual Reality, Virtual reality training
@inproceedings{scott_revolutionizing_2024,
title = {Revolutionizing Nurse Practitioner Training: Integrating Virtual Reality and Large Language Models for Enhanced Clinical Education},
author = {A. J. S. Scott and F. McCuaig and V. Lim and W. Watkins and J. Wang and G. Strachan},
editor = {Strudwick G. and Hardiker N.R. and Rees G. and Cook R. and Cook R. and Lee Y.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199593781&doi=10.3233%2fSHTI240272&partnerID=40&md5=90c7bd43ba978f942723e6cf1983ffb3},
doi = {10.3233/SHTI240272},
isbn = {09269630 (ISSN); 978-164368527-4 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Stud. Health Technol. Informatics},
volume = {315},
pages = {671–672},
publisher = {IOS Press BV},
abstract = {This project introduces an innovative virtual reality (VR) training program for student Nurse Practitioners, incorporating advanced 3D modeling, animation, and Large Language Models (LLMs). Designed to simulate realistic patient interactions, the program aims to improve communication, history taking, and clinical decision-making skills in a controlled, authentic setting. This abstract outlines the methods, results, and potential impact of this cutting-edge educational tool on nursing education. © 2024 The Authors.},
keywords = {3D modeling, 3D models, 3d-modeling, adult, anamnesis, clinical decision making, clinical education, Clinical Simulation, Computational Linguistics, computer interface, Computer-Assisted Instruction, conference paper, Curriculum, Decision making, E-Learning, Education, Health care education, Healthcare Education, human, Humans, Language Model, Large language model, large language models, Mesh generation, Model animations, Modeling languages, nurse practitioner, Nurse Practitioners, Nursing, nursing education, nursing student, OSCE preparation, procedures, simulation, Teaching, therapy, Training, Training program, User-Computer Interface, Virtual Reality, Virtual reality training},
pubstate = {published},
tppubtype = {inproceedings}
}
This project introduces an innovative virtual reality (VR) training program for student Nurse Practitioners, incorporating advanced 3D modeling, animation, and Large Language Models (LLMs). Designed to simulate realistic patient interactions, the program aims to improve communication, history taking, and clinical decision-making skills in a controlled, authentic setting. This abstract outlines the methods, results, and potential impact of this cutting-edge educational tool on nursing education. © 2024 The Authors.
2018
Caggianese, Giuseppe; Chirico, Andrea; Pietro, Giuseppe De; Gallo, Luigi; Giordano, Antonio; Predazzi, Marco; Neroni, Pietro
Towards a Virtual Reality Cognitive Training System for Mild Cognitive Impairment and Alzheimer's Disease Patients Proceedings Article
In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 663–667, 2018.
Abstract | Links | BibTeX | Tags: Alzheimer's disease, Cognitive training, Dementia, Healthcare, Mild Cognitive Impairment, Monitoring, Training, Virtual Reality
@inproceedings{caggianeseVirtualRealityCognitive2018,
title = {Towards a Virtual Reality Cognitive Training System for Mild Cognitive Impairment and Alzheimer's Disease Patients},
author = { Giuseppe Caggianese and Andrea Chirico and Giuseppe De Pietro and Luigi Gallo and Antonio Giordano and Marco Predazzi and Pietro Neroni},
doi = {10.1109/WAINA.2018.00164},
year = {2018},
date = {2018-05-01},
booktitle = {2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)},
pages = {663--667},
abstract = {The rapid growth of the aged population has stimulated research directed at designing interventions to support the associated social, economic and health challenges in an elderly population. Environmental interventions, like cognitive rehabilitation, stimulation and training can significantly improve cognitive functioning, so mitigating the cognitive decline. In this area, the adoption of state-of-the-art virtual reality technologies can provide a cost-effective, flexible and comprehensive solution for realizing complex cognitive training environments. With the aim of preserving mnestic and logical-praxic functions of patients with MCI or Alzheimer's disease at the early stages, in this paper we describe our ongoing work in designing a novel, fullyequipped virtual reality cognitive training system. The system is characterized by a high degree of realism and interactivity, to provide the patient with an adequate sense of presence within the virtual environment. Moreover, it is able to monitor the patient's biomedical signals and collect quantitative data on the training sessions, so allowing the therapist to analyze and tailor the training strategies to the patient.},
keywords = {Alzheimer's disease, Cognitive training, Dementia, Healthcare, Mild Cognitive Impairment, Monitoring, Training, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid growth of the aged population has stimulated research directed at designing interventions to support the associated social, economic and health challenges in an elderly population. Environmental interventions, like cognitive rehabilitation, stimulation and training can significantly improve cognitive functioning, so mitigating the cognitive decline. In this area, the adoption of state-of-the-art virtual reality technologies can provide a cost-effective, flexible and comprehensive solution for realizing complex cognitive training environments. With the aim of preserving mnestic and logical-praxic functions of patients with MCI or Alzheimer's disease at the early stages, in this paper we describe our ongoing work in designing a novel, fullyequipped virtual reality cognitive training system. The system is characterized by a high degree of realism and interactivity, to provide the patient with an adequate sense of presence within the virtual environment. Moreover, it is able to monitor the patient's biomedical signals and collect quantitative data on the training sessions, so allowing the therapist to analyze and tailor the training strategies to the patient.
Caggianese, Giuseppe; Chirico, Andrea; Pietro, Giuseppe De; Gallo, Luigi; Giordano, Antonio; Predazzi, Marco; Neroni, Pietro
Towards a Virtual Reality Cognitive Training System for Mild Cognitive Impairment and Alzheimer's Disease Patients Proceedings Article
In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 663–667, 2018.
Abstract | Links | BibTeX | Tags: Alzheimer's disease, Cognitive training, Dementia, Healthcare, Mild Cognitive Impairment, Monitoring, Training, Virtual Reality
@inproceedings{caggianese_towards_2018,
title = {Towards a Virtual Reality Cognitive Training System for Mild Cognitive Impairment and Alzheimer's Disease Patients},
author = {Giuseppe Caggianese and Andrea Chirico and Giuseppe De Pietro and Luigi Gallo and Antonio Giordano and Marco Predazzi and Pietro Neroni},
doi = {10.1109/WAINA.2018.00164},
year = {2018},
date = {2018-05-01},
booktitle = {2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)},
pages = {663–667},
abstract = {The rapid growth of the aged population has stimulated research directed at designing interventions to support the associated social, economic and health challenges in an elderly population. Environmental interventions, like cognitive rehabilitation, stimulation and training can significantly improve cognitive functioning, so mitigating the cognitive decline. In this area, the adoption of state-of-the-art virtual reality technologies can provide a cost-effective, flexible and comprehensive solution for realizing complex cognitive training environments. With the aim of preserving mnestic and logical-praxic functions of patients with MCI or Alzheimer's disease at the early stages, in this paper we describe our ongoing work in designing a novel, fullyequipped virtual reality cognitive training system. The system is characterized by a high degree of realism and interactivity, to provide the patient with an adequate sense of presence within the virtual environment. Moreover, it is able to monitor the patient's biomedical signals and collect quantitative data on the training sessions, so allowing the therapist to analyze and tailor the training strategies to the patient.},
keywords = {Alzheimer's disease, Cognitive training, Dementia, Healthcare, Mild Cognitive Impairment, Monitoring, Training, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid growth of the aged population has stimulated research directed at designing interventions to support the associated social, economic and health challenges in an elderly population. Environmental interventions, like cognitive rehabilitation, stimulation and training can significantly improve cognitive functioning, so mitigating the cognitive decline. In this area, the adoption of state-of-the-art virtual reality technologies can provide a cost-effective, flexible and comprehensive solution for realizing complex cognitive training environments. With the aim of preserving mnestic and logical-praxic functions of patients with MCI or Alzheimer's disease at the early stages, in this paper we describe our ongoing work in designing a novel, fullyequipped virtual reality cognitive training system. The system is characterized by a high degree of realism and interactivity, to provide the patient with an adequate sense of presence within the virtual environment. Moreover, it is able to monitor the patient's biomedical signals and collect quantitative data on the training sessions, so allowing the therapist to analyze and tailor the training strategies to the patient.