Available Soon
Antonio Lanatà, Department of Information Engineering, University of Florence, Italy
Available Soon
He Huacheng, Oujiang Laboratory, China
Extended Reality in Healthcare
Kavitha Anandan, SSN College of Engineering, India
AI in Medicine: Progress, Perils, and Perspectives
Tim Hahn, University of Münster, Germany
Available Soon
Antonio Lanatà
Department of Information Engineering, University of Florence
Italy
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Brief Bio
Antonio Lanatà, Ph.D., is an Associate Professor of Bioengineering at the Department of Information Engineering, University of Florence, Italy. His research interests include designing and implementing wearable systems for physiological monitoring and statistical and nonlinear biomedical signal processing. Applications of his research include the assessment of autonomic nervous system activity on affective computing, assessment of mood and mental/neurological disorders, and human/animal/robot interaction. He is the author of numerous international scientific publications in peer-reviewed international journals, conference proceedings, and book chapters. He has been involved in several international research projects of the European collaborative project. Prof. Lanatà is Associate Editor of several International journals, such as IEEE Affective Computing, Frontiers Bioelectronics, MDPI Bioengineering, Bioelectronics, Biosensors, Algorithms, Electronics, and Animals.
Available Soon
He Huacheng
Oujiang Laboratory
China
Brief Bio
Available Soon
Extended Reality in Healthcare
Kavitha Anandan
SSN College of Engineering
India
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Brief Bio
Professor Kavitha Anandan is the Head of the Department of Biomedical Engineering at SSN COllege of Engineering, Tamilnadu, India. She is the Coordinator of Centre for Healthcare Technologies (CHT), an interdisciplinary research initiative of SSN College of Engineering and is a passionate researcher of Brain dynamics in Cognition, learning and memory.
She has over 25 years of research experience in the field of Medical Image processing, Bio Signal processing and Extended Reality. She received her B.E (EEE) degree from the University of Madras, M.S. (By Research) and Ph. D. both specializing in Biomedical Instrumentation from Anna University, Chennai. Her research interests include Cognitive neuroscience, Mental imagery, Neuroimaging, Bio Signal Processing, Bone biomechanics and Virtual/Augmented reality. She has published various articles in international journals and conferences in the areas of biomedical signal and image processing, respiratory mechanics, machine learning techniques, Extended reality and cognitive neuroscience.
She works on various externally projects funded by Department of Science and Technology, Science and Engineering Research Board, National Agricultural and Science Foundation, Shiv Nadar Foundation etc. She is the Program Director of the SSN SACE – DREXEL Direct Pathway MSBME program (2023-till date). She has been mentoring summer interns from the Drexel School of Biomedical Engineering, Philadelphia, USA under the international summer internship iSTAR research program every year (2017 – Till date). She has various international research collaborations with University of Bologna, Italy, University of Delft and University of Twente, The Netherlands, Drexel University, USA, University of Lisbon, Portugal, Birmingham City University, UK, etc.
She has been selected as the Top 20 most influential women scientists in Tamilnadu, India (2019). She was an invited member of the Top 15 Woman Scientists to represent Indian women in STEM research at the ASEAN – INDIA Women’s conclave at Singapore (2024) and the FHED Conference at NCKU, Taiwan.
Abstract
Extended Reality (XR), is an immersive platform that encompasses Virtual Reality (VR) and Augmented Reality (AR) techniques. It provides interactive experiences by merging physical and virtual worlds, thereby allowing interaction with digital objects, enhancing learning, training, and overall understanding. XR has been extensively used in various domains and has seen applications in healthcare. XR applications in healthcare include patient treatment, surgical training, and medical education. XR offers immersive experiences that can enhance patient engagement, aid in rehabilitation, and provide new ways to train healthcare professionals. This talk intends to unfold the possibilities of employing XR in various healthcare applications. A few case based applications on using Augmented reality for spine and liver surgical systems will be discussed. Some interesting VR based environments developed for enhancing learning in children with neurodevelopmental disorders will also be explained.
AI in Medicine: Progress, Perils, and Perspectives
Tim Hahn
University of Münster
Germany
Brief Bio
Tim is a machine learning specialist and neuroscientist. He heads the Medical Machine Learning Lab at the Institute for Translational Psychiatry in Münster, Germany (www.mmll.uni-muenster.de). Tim and his team focus on translational machine learning research for patient care, machine learning software development, and infrastructure for artificial intelligence in medicine with a focus on mental health. Their aim is to bring state-of-the-art machine learning tools and predictive models for personalized medicine into clinical practice.
Abstract
AI is driving a paradigm shift in medicine, enabling thus-far unseen data analysis capabilities and improved patient care. Here, we will provide an overview of recent projects and discuss a high-level framework for translation into clinical practice.
In the area of MRI imaging, for example, projects such as deepmriprep demonstrate how deep learning can accelerate and standardize MRI preprocessing, reducing variability and enabling large-scale studies. Multimodal MRI analyses are being used to investigate brain–behavior associations, while Bayesian frameworks add uncertainty estimates to predictions of disease trajectories, for instance in multiple sclerosis.
Within medical image segmentation, AI methods are developed to automate the delineation of complex anatomical and pathological structures. These approaches reduce reliance on manual labeling and improve reproducibility across sites, addressing a central bottleneck for clinical integration. As an example beyond classical imaging, we use WiFi-based imaging to explore how ambient signals can be used to monitor mobility and behavior continuously in a non-invasive way. This provides ecologically valid, real-world data that enrich imaging-derived biomarkers. A further line of work investigates dynamical system identification using neural networks, enabling the extraction of underlying system dynamics from biomedical time series, with applications ranging from physiology to brain activity modeling.
Challenges remain with small and heterogeneous datasets, variability in pipelines, and the ethical and regulatory complexities of translation into practice. Looking ahead, standardized workflows, uncertainty-aware modeling, and multimodal integration will be crucial. By embedding ethical and legal safeguards into technical innovation, AI in medical bioimaging can evolve responsibly and contribute to more reliable, patient-centered care.