Student at University of Genoa, Italy
Alaa Wehbe is a forward-thinking AI Engineer whose work is centered on integrating artificial intelligence into medical imaging to support personalized healthcare solutions. With a strong academic foundation and hands-on experience in both research and practical applications, Wehbe focuses on developing impactful technologies that address critical healthcare challenges such as lung cancer detection, fibrosis staging, and rehabilitation monitoring. His interdisciplinary expertise blends deep learning, signal processing, and embedded systems, allowing him to engineer end-to-end AI-driven diagnostic tools. Currently based in Genoa, Italy, he is committed to advancing AI applications in the medical domain through academic research, collaborative projects, and innovative software development.
Profile
Orcid
Education
Alaa Wehbe is pursuing a Ph.D. in Applied AI Solutions in the Medical Field at the University of Genoa, Italy (2022–2025). His doctoral research focuses on “Personalized Medicine and Process Optimization,” where he analyzes and implements intelligent tools to enhance the clinical process, particularly in cancer detection, fibrosis staging, and patient rehabilitation. His supervisor, Prof. Silvana Dellipiane, oversees projects that combine large language models (LLMs), fine-tuning techniques, and medical imaging technologies. Prior to his Ph.D., Wehbe earned a Master 2 degree in Applied Science—Signal, Telecoms, Images & Speech—from the Lebanese University, where he ranked 2nd out of 9. He developed a strong foundation in signal processing, pattern recognition, and image and speech processing. His Master 1 studies in Electronics and Bachelor of Science in Electronics from the same university highlight his consistent academic excellence, ranking 1st and 2nd in his cohorts respectively. His educational background offers a robust blend of theoretical knowledge and practical skills, especially in AI for healthcare applications.
Experience
Wehbe’s practical experience includes academic research and applied engineering projects. During his Master’s internship at the Lebanese University, he implemented image processing and deep learning algorithms on embedded boards such as Raspberry Pi 3+ and 4, with and without neural sticks. He developed and tested real-time object detection models, which laid the groundwork for his current Ph.D. research. As part of his doctoral work, he has been involved in multiple interdisciplinary projects at the University of Genoa. These include AI tools for lung cancer detection using CT scans, liver fibrosis staging through image analysis, and machine learning-based rehabilitation monitoring systems. He also engages in AI model development using advanced frameworks, and his contributions are marked by their translational impact on real-world clinical workflows.
Research Interest
Alaa Wehbe’s research interests lie at the intersection of artificial intelligence, medical imaging, and personalized medicine. He is particularly focused on applying deep learning techniques such as YOLOv8 and LLMs to solve complex diagnostic problems in healthcare. His recent efforts aim to optimize clinical processes by developing AI-driven systems capable of supporting doctors with real-time insights. His interests also extend to signal and image processing, object detection, and movement classification using AI in rehabilitation contexts. Moreover, he is deeply invested in fine-tuning AI models for specific use cases, contributing to the growing field of AI for healthcare personalization.
Award
While still early in his professional journey, Alaa Wehbe has gained academic recognition through consistent high rankings in his undergraduate and postgraduate studies. His involvement in high-impact international conferences and journals like IEEE ACCESS and Springer Nature is a testament to the relevance and quality of his research. His work has been presented and appreciated at several prestigious events, contributing to knowledge dissemination in medical AI.
Publication
Alaa Wehbe has authored several significant publications in the field of AI and medical imaging. Notably, in IEEE ICECS 2024, he co-authored “Integrating YOLO for Advanced Content-Based Image Retrieval in Lung Cancer Imaging”, where he developed a system that integrates YOLOv8 and TNM staging for lung cancer classification ([Cited by 12 articles]). In IEEE ACCESS 2024, his paper “Enhanced Lung Cancer Detection and TNM Staging Using YOLOv8 and TNMClassifier” introduced an integrated deep learning approach for CT imaging ([Cited by 20+ articles]). At the International Conference on Applications in Electronics Pervading Industry, Environment and Society (2024) in Italy, he contributed to “Evaluation of Machine Learning Models for Movement Classification in Exergame-Based Rehabilitation”, focusing on AI applications for patient rehabilitation ([Cited by 8 articles]). His collaboration with researchers at Springer Nature led to “Plane-Wave Ultrasound Imaging: Implementation and Evaluation of Different Interpolation Schemes”, showcasing AI-assisted analysis for ultrasound imaging ([Cited by 5 articles]). These publications emphasize his interdisciplinary skills and the translational value of his research in clinical AI solutions.
Conclusion
Alaa Wehbe stands out as a promising AI engineer whose commitment to integrating artificial intelligence into medical practices is already yielding meaningful contributions to healthcare innovation. His strong academic credentials, practical experience with embedded AI systems, and growing portfolio of peer-reviewed publications demonstrate a clear trajectory toward impactful research and development. With a focus on personalized medicine and intelligent diagnostic tools, Wehbe is poised to influence the next generation of AI-driven healthcare technologies.