Cetinje, 27 July – 1 August 2026 – Members of the AI-AGE project team took part in the Eastern European Machine Learning Summer School (EEML 2026), one of the largest AI and machine learning gatherings in the region, held in Cetinje with around 300 participants from across the EU.
AI-AGE team members were involved on several levels. They participated in the EEML training programme, strengthening their skills in machine learning and AI methods relevant to the project’s biomedical research. They also contributed as volunteers in the organisation of the school and its activities. In addition, through cross-project collaboration with NCC HPC Montenegro, they provided help and support to participants, including discussions on the use of high-performance computing (HPC) for AI and machine learning.
This participation reflects the close cooperation between AI-AGE and NCC HPC Montenegro, and their shared goal of building AI and HPC capacity in Montenegro and the wider region through both training and hands-on support.
Cetinje, 28 July 2026 – A member of the AI-AGE project team took part in a high-level meeting on artificial intelligence hosted by the President of Montenegro and the President’s Cabinet. Our representative was among a group of around ten AI experts from Montenegro invited to this important discussion, alongside the President of the Montenegrin Academy of Sciences and Arts (CANU), representatives of universities, the diaspora, the Montenegrin AI Association (MAIA), and industry.
The discussion focused on directions for the development of artificial intelligence in Montenegro, including the need for data centres and high-performance computing (HPC) infrastructure, and the role of research and education in building national AI capacity. These themes are closely aligned with the mission of AI-AGE, which brings together expertise in AI, HPC and biomedical research to develop non-invasive biomarkers of aging and to strengthen research and educational capacity in the country.
Being invited to this meeting was an important opportunity for AI-AGE to contribute to the national conversation on AI at the highest level of public administration. As part of the discussion, our representative proposed that domain experts — including professionals from the medical field — be included in the strategic dialogue on artificial intelligence in Montenegro, ensuring that AI development is guided by real needs in healthcare and other application areas. The team looks forward to taking part in the further steps that follow from this discussion.
Oxford, 14–18 July 2026 – A member of the AI-AGE project team participated in the Oxford Machine Learning School 2026 (OxML), attending the MLx Representation Learning & Generative AI module. The intensive programme brought together more than 150 participants and lecturers from leading academic and industrial research environments, including Oxford, Cambridge, Imperial College London and Google DeepMind.
Participation in the Oxford Machine Learning School 2026 (OxML)
Over four days, the programme covered advanced topics in modern machine learning, ranging from frontier models for vision and language to multimodal representation learning, generative AI and agentic systems. Particular value came from the strong theoretical foundations underlying these methods, complemented by discussions and exchange with researchers from across Europe and beyond.
This was also an opportunity to network with researchers from all over the World
Participation in OxML contributes directly to the capacity-building objectives of the AI-AGE project, strengthening expertise in advanced AI and machine learning methods relevant to the project’s research activities. Such training is particularly important for developing the methodological knowledge required to apply state-of-the-art AI approaches to biomedical data, representation learning and the identification of non-invasive biomarkers of aging.
Dr Stevan Cakic
Participation was co-financed through the 2026 Call for Co-financing Scientific Research Activities provided by the Ministry of Education, science and Innovation.
On June 29, 2026, MSc candidate Anesa Abazović successfully defended her thesis entitled “Machine Learning and AI Model Development for Medical Applications” within the Artificial Intelligence Master’s programme at the University of Donja Gorica. Through its support for the programme, mentoring activities, and development of competencies in artificial intelligence and high-performance computing, NCC Montenegro contributes to preparing young researchers to apply advanced AI methods in medicine and other socially relevant domains. The thesis investigates the application of machine learning and deep learning to medical image analysis and clinical data classification, while also considering the technical, ethical, and practical challenges of integrating AI systems into healthcare.
Ms Anesa Abazovic during her MSc defence
ABSTRACT – This thesis explores the potential of machine learning (ML) and deep learning (DL) models in the detection of ovarian cancer and the prediction of pneumonia. In the first part, a YOLO model was used to identify tumor lesions in medical images, while in the second part, XGBoost, Random Forest, and neural network models were applied for the classification of clinical data. Model performance was evaluated using metrics such as precision, recall, accuracy, specificity, F1-score, ROC-AUC, MCC, mAP50, and mAP50-95. The experimental analysis demonstrated that AI models can achieve promising performance in both clinical scenarios, with certain limitations that require further validation. In addition to technical aspects, ethical considerations were also examined, including model interpretability, data privacy, and the integration of AI systems into healthcare information systems. It is concluded that AI can provide significant support to modern diagnostics, with the need for further improvements and clinical validation.
On June 29, 2026, MSc candidate Nikola Kavarić successfully defended his thesis entitled “Synergy of Computer Vision and Natural Language Processing in Tuberculosis Diagnostics and Education” within the Artificial Intelligence Master’s programme at the University of Donja Gorica. Through its support for the programme, mentoring activities, and development of competencies in artificial intelligence and high-performance computing, NCC Montenegro contributes to preparing young researchers to develop interdisciplinary AI solutions for healthcare. The thesis investigates the combination of computer vision and Retrieval-Augmented Generation approaches for detecting signs of tuberculosis and providing educational explanations of medical findings.
Mr. Kavaric during his MSc defence (NCC Montenegro)
ABSTRACT – The aim of this thesis is the development and evaluation of a system that combines computer vision and Retrieval-Augmented Generation (RAG) models for the automatic detection of signs of tuberculosis in chest X-ray images and the educational explanation of findings. The initial hypothesis was that it is possible to develop a functional prototype capable of recognizing pathological changes in X-ray images and generating informative, literature-grounded responses for users. Within this research, a CNN model for binary classification and YOLO models for the localization of pathological changes were developed and evaluated. The CNN model achieved an accuracy of 97% on the test set, representing a solid and measurable contribution. The YOLO models adequately demonstrated the concept of localization, with certain limitations related to dataset size and class imbalance. In addition to the visual module, a RAG prototype was implemented, utilizing a local medical document base to generate responses to user queries. The integration was implemented at the prototype level, without clinical validation. Based on the obtained results, the hypothesis was partially confirmed — to a significant extent for the CNN classification component within the test dataset used, while the YOLO and RAG components, due to dataset limitations and the absence of expert-verified reference answers, should be treated as proof-of-concept components. The thesis demonstrates that a modular combination of these technologies can serve as a useful foundation for the development of educational tools in the field of medical diagnostics.
Podgorica, 18 June 2026 – Members of the AI-AGE project team participated in the scientific event “AI Economy”, held at the Montenegrin Academy of Sciences and Arts (MASA). The event brought together representatives of academia, researchers, experts and stakeholders to discuss the impact of artificial intelligence on the economy, education, professions, healthcare, digital transformation and broader societal development.
Dr Stevan Cakic discussing the AI and opportunities in small economies
The participation of the AI-AGE team focused on the importance of building local knowledge, research capacity and educational infrastructure for the responsible and effective use of artificial intelligence. Particular attention was given to the role of AI in education, the development of interdisciplinary skills, and the need to prepare students, researchers and institutions for an environment in which AI tools are becoming part of everyday professional and scientific practice.
Mr Igor Culafic presentng a paper on importance of niche development to stay competitive
The event also provided an opportunity to highlight the relevance of high-performance computing (HPC) for AI research and innovation. Access to advanced computing infrastructure is increasingly important for training and testing AI models, processing large datasets, supporting biomedical and data-driven research, and strengthening the overall capacity of universities and research teams in Montenegro.
Prof. Tomo Popovic talking about the competences and infrastructure needed for AI economy
Through its activities, the AI-AGE project continues to contribute to capacity building in the fields of AI, HPC and applied data science, with a particular focus on education, research excellence and the development of solutions that can support healthcare, science and society. Participation in events such as “AI Economy” is an important step in strengthening cooperation between academia, research projects and wider institutional stakeholders in Montenegro.
The AI-AGE project was presented at the round table “Artificial Intelligence in Healthcare – Challenges and Opportunities”, held on 24 April 2026 at the Montenegrin Academy of Sciences and Arts (CANU) in Podgorica. The event gathered experts from Montenegro and Bosnia and Herzegovina to discuss the role of AI in healthcare, including clinical applications, digital transformation, ethics, medical imaging, NLP, and AI assistants.
AI-AGE presented at CAN Round Table
AI-AGE was presented by Prof. Dr Nataša Popović, Faculty of Medicine, University of Montenegro, in the session dedicated to AI in clinical practice. The presentation highlighted key findings of the project and demonstrated how AI can support early detection and screening of chronic diseases, including examples related to colorectal cancer detection and the use of biomarkers.
Opportunity to present goals and results of the project
The event was also an opportunity to promote EuroCC activities and the role of NCC Montenegro in strengthening national capacities in HPC, HPDA, and AI. Participation in this round table further positioned AI-AGE within the broader regional discussion on responsible and clinically relevant use of artificial intelligence in medicine.
Key findings and potential benefits of the use of AI models developed in AI-AGE
Our team presented a paper titled “Interpretable ML for Diabetes and Prediabetes Screening Using Self-Reported Health Indicators” by S. Lazic, S. Cakic, I. Rubezic Lukic, N. Popovic, and T. Popovic at the 30. Annual Conferenc on Information Technology IT 2026. This was part of mentoring activities and efforts related to development of young researchers.
The paper was presented at the conference by Ms. Sanja Lazic (MSc candidate)
ABSTRACT – Early identification of type 2 diabetes (T2D) and prediabetes enables timely interventions, yet screening often relies on self-reported data rather than laboratory testing. This work compares lightweight Machine Learning (ML) models: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) trained on 21 self-reported indicators from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) dataset for three-class classification (no diabetes, prediabetes, diabetes). We propose a screening-oriented evaluation where a probability threshold is selected to achieve a target sensitivity (recall) of 0.80. LightGBM achieves balanced accuracy of 0.52 and precision of 0.33 at the target sensitivity, with 38% of cases flagged. Tree SHapley Additive exPlanations (TreeSHAP) highlight general health status, age category, body mass index (BMI), and hypertension as dominant predictors. A FastAPI web application provides individual risk estimates and instance-level explanations. The pipeline demonstrates feasibility of interpretable, calibrated screening from non-laboratory data.
AI-AGE team represenatives participated in the various events and activities at IT2026 conference in Zabljak. We ook part in discussions related to Lessons Learned for HPC and AI applications in various domains in Montenegro. We also participated in the special session dedicated to project results presentations where we had a chance to discuss the project with researchers from Montenegro and the region.
AI-AGE was featured at the project results presentations
We particiapted in EuroCC4SEE panel on Lessons Learned for HPC and AI applications
High-Performance Computing (HPC) and Artificial Intelligence (AI) are rapidly transforming the landscape of healthcare — moving far beyond research prototypes into solutions that can shape clinical practice and improve patient outcomes. This transition from strategy to real-world impact was the focus of the recent EuroCC2 initiative “Symposium: HPC and AI Driven Innovation in Healthcare”. AI-AGE team participated in organization, coordination, and presentations at this event.
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The Vision: Bridging Strategy and Clinical Practice
The event was organized in cross-collaboration with EuroCC2 & EuroCC4SEE
AI-AGE project was presented and further collaboration with EuroCC3 was discussed
Healthcare is today generating vast volumes of data — from medical imaging to electronic health records, genomics, wearable sensors and beyond. HPC provides the computational power needed to process and analyse this data at scale, while AI techniques such as deep learning unlock patterns that are invisible to traditional analysis methods. Together, HPC and AI form a powerful synergy for healthcare innovation:
Accelerated diagnostics: AI models trained on large annotated datasets can assist clinicians by accurately identifying disease signs in imaging and other modalities.
Biomarker discovery and precision medicine: High-throughput computing enables the discovery of subtle biological signals indicative of disease progression or treatment response.
Predictive and personalised care: HPC-enabled AI workflows can predict patient outcomes and support real-time clinical decision making.
Symposium included presentations of successs stories from the region
This strategic capability — from data to insights to impact — was the core theme explored through EuroCC2 activities in Montenegro and the wider South-Eastern Europe region.
Key Takeaways for Healthcare Innovation
From Research to Clinical Utility HPC and AI solutions are no longer confined to laboratories. With appropriate infrastructure, data governance and clinical integration pathways, these technologies are being translated into tools that support healthcare professionals in diagnosis and treatment.
Regional and Cross-Institutional Collaboration The EuroCC2 framework — including the National Competence Centre Montenegro — brings together academic institutions, healthcare providers, and technology partners to share resources, expertise, and training. These collaborative ecosystems are essential for building sustainable HPC-AI capacity in healthcare.
Capacity Building and Skills Development One of the crucial pillars of impactful HPC and AI adoption is training. Workshops, seminars, and hands-on sessions equip researchers, clinicians, and students with the skills to leverage HPC and AI tools effectively in their domains.
Enabling Infrastructure Access Through EuroCC2 and related programmes, researchers and practitioners gain access to European HPC resources — reducing barriers to entry for high-end computing and enabling complex analyses that were previously impractical.
What This Means for Montenegro and Beyond
Montenegro, alongside partner regions across Europe, is building the foundation for a healthcare ecosystem that integrates HPC and AI into everyday clinical workflows. By investing in strategic computing infrastructure, enabling cross-sector collaboration, and fostering technical expertise, the potential to improve patient outcomes, streamline clinical processes, and support data-driven medicine is growing stronger.
The event gathered representatives from Healthcare and IT sectors already involved in research and development of HPC and AI driven solutions for healthcare and medical research. More info at NCC Montenegro site: [link].
Over 20 participants in the Symposium, important discussion of next steps
