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Welcome to the McMaster Biomedic.AI Lab

The Biomedic.AI Lab focuses on research regarding our interaction with computational machines for the augmentation, rehabilitation, and enhancement of human attributes. The study of communication and control between the animal and machine not only refines our knowledge about the capabilities of artificial intelligence but also highlights the synergistic capacity between humans and such intelligent systems to address complex issues in healthcare and beyond.

With the recognition that biological systems exhibit inherent variability, the role of machine learning and artificial intelligence is a natural fit for the interfacing of learning devices and systems. Additionally, health risks prediction and classification based on medical and related data has become an area of great interest for medical providers, patients, and resource allocation. The computation from the smallest embedded devices, to cloud based services, to the high-performance computing all have role in our research at the Biomedic.AI Lab.

A current research focus is quantifying trust in autonomous medical advisory systems (AMAS).  This micronet consists of four pillars of AMAS investigation: i) Trust in Hardware and Software (Led by Dr. Doyle) with an emphasis on quantifying how sensing hardware and associated medical advisory model performance are impacted by development and deployment considerations, ii) Privacy and Security (Led by Dr. Samavi), iii) Trust in Medical Imaging (Led by Dr. Noseworthy), and iv) Joint Cognitive Systems (Led by Dr. Yule). Quantifying elements that assist the user in better understanding the model outcomes are fundamental to trusting medical AI decisions, and thus have relevant applications to clinical practice by revolutionizing care delivery, medical decision-making, and diagnostic support through teaming with professionals.  

Current/recent projects in the Biomedic.AI Lab include:

  • Individual and Team Trust in Medical AI
  • Embedded and Mobile Machine Learning Models
  • Active Machine Learning Models
  • AI-Based Personalized Predictive Model for Medical Imaging Radiation
  • Segmentation and Classification of Liver Imaging
  • Augmented Reality Based Brain Tumor 3D Visualization
  • Cognitive Priority Model for Temporal Orientation of Advanced Telemedical Support in Limited Bandwidth Applications
  • Distributed Telemedicine Simulation Platform for Team Medical Event Management During Space Exploration
  • Predictive Indicators for Static and Dynamic Visualization
  • Technical research interests include biomedical signal processing, health informatics, human-computer interfacing (HCI), and machine learning / AI for the augmentation, rehabilitation, and enhancement of human attributes.