Artificial Intelligence Based Platform for Medical Training, Skill Optimization, and Drug Use Prevention


Tech Id: D2020-06


Currently, training or education across multiple domains and professions is completed using a “one size fits all” curriculum, where there is no dynamic personalization for a given learner. Instead, mission critical professions such as healthcare, emergency first responders, and the military often use pass/fail criteria or checklists to evaluate performance and competence. Learning management systems (LMSs) and advanced performance assessment systems currently exist, however, measures derived or generated by these systems are non-standardized, insufficient, and do not characterize the complete picture of knowledge/skill acquisition over time. To this end, current LMS platforms suffer from a lack of multi-modal assessment capabilities required to personalize education/training. Furthermore, measures from current LMS platforms cannot be directly mapped to or associated with real-world performance criteria. There is a need for new and improved systems and methods for assessing and conducting and personalizing training/education activities.

Invention Description:

Researchers at the University of Toledo have developed a novel multi-modal assessment platform that leverages artificial intelligence (AI) to personalize training, enhance real-world performance, and predict relapse in patients with drug addiction. The novel platform provides a comprehensive measurement and assessment framework designed to personalize training/education within any professional domain to ensure peak real-world performance. The system combines 24/7 continuous neurophysiological, behavioral/lifestyle, and environmental assessment to collect data related to learner state before, during, and following training or real-world operations. The system is designed to provide data-driven recommendations providing insight into what, when, and how much training/education is required to maintain or improve upon knowledge/skills and avoid decay. Lastly, our architecture of our platform is multi-purpose to accommodate a number of use cases (training, clinical decision support, automated therapy, telemedicine, and others) where system intelligence can be substituted via deploying different software services.

IP Status: Patent Pending


  1. Artificial Intelligence based Education for training and performance
  2. Medical Simulation

Additional Embodiments Use Cases of Technology:

  1. Prediction of relapse in patients struggling with opioid addiction.
  2. Automation of therapy and providing clinical decision support through continuous monitoring of patients with diabetes, psychiatric disorders (post traumatic stress disorder, generalized anxiety disorder, autism).
  3. Detection of COVID-19 infection and contact tracing through 24/7 noninvasive monitoring
  4. Continuous monitoring to mitigate incidence of infant mortality.
  5. Continuous patient monitoring after hospitalization to prompt telemedicine interventions.


  1. The novel invention is capable of monitoring subjects throughout the day including lifestyle (e.g. sleep wake cycles, sleep quality, daily routines, etc.), active status (e.g., fatigue, exercise, etc.), emotional states (stress/anxiety, frustration, depression), etc.
  2. The fused AI based invention serves as a virtual coach to personalize training across diverse fields including healthcare, emergency first responders, law enforcement/army, etc.
  3. The systems utilizes novel wearable device and software capable of monitoring and collecting a broad range of multimodal neurophysiological, lifestyle, and behavioral assessment data inputs in an unobtrusive and automated manner.
Patent Information:
For Information, Contact:
Stephen Snider
AVP Tech Transfer
The University of Toledo
419 530 6225
Brent Cameron
Scott Pappada
Mahmoud Eladawi
Mohammad Owais