This research group aims to conduct advanced studies on the development and use of artificial intelligence models in bioengineering and diagnostics as a result of the rapid development of technology in the field of biomedical sciences, market demand for the adaptation of new research in the field of biomedical engineering. The focus is to study new areas of application of artificial intelligence in biomedical technology, develop new algorithms and models for prediction and advanced simulation methods for automation in these fields, implementation of technology in medicine, especially 3D virtual simulation models, imaging diagnostics, calculation and processing.

1️⃣ Develop Advanced Simulation & Predictive Models

• Design and implement state-of-the-art machine learning and deep learning models for simulation and prediction in biomedical, diagnostic, and therapeutic contexts.

• Create virtual environments and simulation platforms to replicate complex medical procedures and workflows for training and research purposes.

• Improve modeling of biological systems, tumor response, radiotherapy dose distribution, and patient-specific treatment outcomes.

2️⃣ Enhance Diagnostic & Therapeutic Accuracy

• Improve the precision of medical image analysis (segmentation, classification, reconstruction) for applications such as tumor detection, organ delineation, and treatment monitoring.

• Develop radiomics and quantitative imaging methods to extract meaningful biomarkers from imaging data for better diagnosis and prognosis.

• Reduce diagnostic and therapeutic errors by integrating predictive models into clinical decision support systems.

3️⃣ Promote Ethical & Effective Clinical Integration

• Collaborate with clinical experts to ensure that AI models are aligned with clinical needs, workflows, and safety standards.

• Validate AI tools in real-world settings through prospective studies, pilot trials, and clinical partnerships.

• Address ethical, regulatory, and data privacy challenges related to AI deployment in healthcare settings

4️⃣ Advance Training & Educational Resources

• Develop interactive AI-based training modules and simulators for clinicians, radiologists, radiotherapists, and medical physicists.

• Offer structured training programs (courses, workshops, hackathons) for students, researchers, and healthcare professionals to build competency in medical AI applications.

• Produce educational materials (tutorials, manuals, datasets, code repositories) to foster broader knowledge sharing and skill development.

5️⃣ Foster Student & Early-Career Researcher Development

• Provide research opportunities for undergraduate, master’s, and PhD students through hands-on project work, research assistantships, and thesis supervision.

• Encourage student-led publications, conference presentations, and participation in collaborative grant-funded projects.

• Create mentorship programs to guide students in academic and industry career pathways at the intersection of AI and healthcare.

6️⃣ Build Strong, Multidisciplinary Partnerships

• Establish collaborations with hospitals, research institutions, medical imaging companies, radiotherapy equipment manufacturers, and AI technology firms.

• Secure joint research projects, funding opportunities, and technology transfer initiatives with academic and industry partners.

• Engage with international networks and consortia to amplify the group’s global impact and visibility.

7️⃣ Promote Innovation & Translational Impact

• Translate research findings into clinically usable software, decision-support tools, and medical devices.

• Contribute to the development of open-source tools, datasets, and frameworks to accelerate innovation in the field.

• Support commercialization pathways, patents, and spin-offs to bring novel AI applications to the healthcare market.