Artificial intelligence applications in Medicine:ImagingRadiotherapyDiagnosticsOptimisationSimulation
AI4MED Research Group
The AI4MED Research Group, led by Prof. Asoc. Dafina Xhako of the Department of Physics Engineering at Polytechnic University of Tirana, is committed to advancing the development and application of artificial intelligence in bioengineering and medical diagnostics. Our team comprises a diverse and interdisciplinary assembly of researchers, educators, and doctoral students, collaborating across physics, medical imaging, radiotherapy, and computational modeling
Our Mission
We strive to design and implement cutting-edge simulation and predictive models tailored for training, diagnostic, and therapeutic applications. Through AI-enhanced image analysis, radiomics, and machine learning methods, we aim to elevate accuracy, streamline clinical procedures, and reduce human error in medical workflows
- Advance Research: Develop AI approaches for segmentation, classification, image reconstruction, and predictive analytics within radiotherapy and diagnostics.
- Drive Impactful Projects: Partner with healthcare institutions and industry to bring AI innovations from the lab to the clinic.
- Empower Learning & Training: Deliver hands-on workshops, seminars, and educational materials in medical AI, imaging, and data science.
- Engage Students & Young Researchers: Mentor and involve students at all levels in research projects, publications, and academic conferences.
- Foster Strong Partnerships: Collaborate with universities, clinical centers, and tech organizations to advance AI integration in medicine.
- Promote Innovation & Translational Impact: Develop open-source tools, frameworks, and pathways for commercialization and clinical use
Main Objectives
- Develop Advanced Simulation & Predictive Models
- Implement state-of-the-art machine learning for biomedical simulation and training environments.
- Model biological systems, treatment responses, and patient-specific workflows.
- Enhance Diagnostic & Therapeutic Accuracy
- Improve image segmentation, tumor detection, and quantitative imaging techniques.
- Integrate radiomics to better inform diagnosis, prognosis, and treatment monitoring.
- Promote Ethical & Effective Clinical Integration
- Ensure AI solutions align with clinical workflows, safety protocols, and regulatory standards.
- Validate tools via pilot studies and prospective clinical trials.
- Advance Training & Educational Resources
- Create interactive simulators and AI modules for clinicians and researchers.
- Offer workshops, tutorials, and maintain resources like code repositories and datasets.
- Foster Student & Early-Career Researcher Development
- Provide research roles, mentorship, and support for conference engagement and publications.
- Build Strong, Multidisciplinary Partnerships
- Collaborate with hospitals, academic institutions, industry, and international networks for shared research and innovation.
- Promote Innovation & Translational Impact
- Translate research into clinical software, decision-support tools, and aid commercialization efforts through patents and spin-offs
Supporting Institutions
We are proudly supported by:
- National Agency of Scientific Research and Innovation (NASRI/AKKSHI)
- Research Expertise from the Academic Diaspora (READ)
- University of Medicine Tirana
- Polytechnic University of Tirana
- NanoBalkan Institute, Academy of Science of Albania
- European Federation of Organisations For Medical Physics (EFOMP)
- Albanian Association of Medical Physics (AAMP)
- Albanian Medical Technology (Albmedtech)
- Institute for Quality in Education (IQE)
The future we envision
AI4MED aspires to shape a future where AI-driven tools transform medical diagnostics, training, and therapeutic planning—empowering clinicians with generation-defining resources to improve patient care, foster innovation, and elevate healthcare efficiency and precision.
AI for medicine
AI is playing an increasingly important role in medicine, with the potential to revolutionize healthcare in numerous ways. AI is currently being used in medicine:
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Diagnosis and screening: AI algorithms can assist physicians in diagnosing and screening patients for various medical conditions, including cancer, heart disease, and neurological disorders. AI can analyze patient data, including medical images and electronic health records, to identify patterns and detect early warning signs of disease.
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Treatment planning and personalized medicine: AI can help physicians create personalized treatment plans for patients, based on their unique medical history, genetics, and other factors. AI can analyze large amounts of patient data to identify the most effective treatments and predict patient outcomes.
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Drug discovery and development: AI can accelerate the drug discovery and development process by analyzing large amounts of data and identifying potential drug candidates. AI can also help identify patients who are most likely to respond to a particular treatment.
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Patient monitoring: AI can monitor patients in real-time, analyzing patient data to detect changes in vital signs and alerting physicians to potential problems. This can improve patient outcomes and reduce the risk of complications.
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Medical research: AI can help researchers analyze large amounts of medical data to identify new insights and discoveries. AI can also help identify patients who are most likely to benefit from clinical trials.
AI and impact in medical imaging
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The use of AI in medical imaging has the potential to revolutionize the field by improving accuracy, efficiency, and patient outcomes. However, there are still challenges to be addressed, such as ensuring the accuracy and reliability of AI algorithms and addressing ethical and legal concerns related to patient privacy and data protection.Image interpretation: AI algorithms can assist radiologists in interpreting medical images, such as X-rays, MRIs, and CT scans. These algorithms can learn to recognize patterns and detect abnormalities that may be difficult to spot by human experts.
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Image acquisition and processing: AI can help optimize image acquisition and processing, ensuring that the images are of high quality and suitable for diagnostic purposes.
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Workflow optimization: AI can help streamline the radiology workflow, by automating certain tasks such as image annotation and report generation. This can reduce the workload of radiologists and free up time for more complex tasks.
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Clinical decision support: AI can provide decision support to radiologists, by suggesting appropriate follow-up imaging or recommending alternative imaging modalities.
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Predictive analytics: AI can analyze large amounts of patient data to predict the likelihood of certain health outcomes, such as the development of certain diseases or response to treatment.
Our vision and future
The use of AI in medical image diagnosis has great potential to improve healthcare outcomes by providing more accurate, efficient, and personalized care to patients. Precision medicine: AI algorithms can analyze patient data, including medical images, genetic information, and electronic health records, to create personalized treatment plans that take into account the patient's unique medical history and genetics. Enhanced diagnostic accuracy: AI can assist radiologists and other medical professionals in analyzing medical images to detect early signs of disease, identify abnormal growths or lesions, and provide accurate diagnoses. This can lead to faster and more effective treatment, and ultimately better patient outcomes. Reduced healthcare costs: AI can help reduce healthcare costs by streamlining the diagnostic process and enabling more accurate and efficient diagnosis and treatment. This can also reduce the need for unnecessary tests and procedures, resulting in cost savings for both patients and healthcare systems. Improved patient outcomes: AI can improve patient outcomes by enabling earlier and more accurate diagnosis, reducing the risk of misdiagnosis and unnecessary treatment, and enabling personalized treatment plans that are tailored to the patient's individual needs. Continuous learning and improvement: AI can continuously learn and improve by analyzing large amounts of patient data and feedback from medical professionals. This can lead to more accurate and efficient diagnosis and treatment over time.
