VR/AI in Education

Traditional coursework often relies on large quantities of data and complex equations, making it challenging for students to grasp concepts tactically. Augmented reality (AR) and artificial intelligence (AI) can revolutionize education by transforming these datasets and equations into physiologically accurate simulations of real processes, providing students with an intuitive understanding of scientific theories and clinical datasets. For example, within simulated clinical scenarios, a student could use a mobile device to visualize a drug’s distribution through patient’s bodyin real time (figure to the right). These innovative tools help bridge the conceptual gap that makes traditional coursework difficult, offering a more immersive and intuitive learning experience. In the long term, AR and AI could serve as medical assistants, enabling clinicians to navigate complex data analysis effortlessly without having to perform intricate mathematical calculations themselves. This integration not only enhances learning but also equips future professionals with the skills to handle advanced medical technologies effectively.

Clinical knowledge to  digital tools workflow

A major goal of SMDA’s VR/AI Education working group is to unlock the datasets underlying traditional healthcare curricula to enable accurate simulation of clinical scenarios and patient physiology. As illustrated in the figure to the right, patient-specific datasets can be curated from clinical literature to enable quantitative simulations of patient cases, patient diagnostics, and clinical scenarios. Additionally, the raw text from this clinical literature can be used to build automated teaching assistants that have “read” the entire body of clinical literature relevant to a particular course or topic. In the long term, we envision these teaching assistants serving as medical assistants, helping practicing clinicians navigate the millions of scientific and clinical papers available in areas like cancer and infectious disease research. This integration of VR and AI into healthcare education aims to enhance both learning and practical application in clinical settings.

 

 

---

Tactical Priorities Recruit Workforce:  with interests in clinical data science,  AI, and AR/VR Train Workforce:  to use emerging tools of data-science artificial intelligence Build Collaborations:  between relevant disciplines educational researchers data scientists clinicians scientists Automate:   laborious and iterative processes to: increase efficiency of research increase access to advanced computational methods

---

Strategic Priorities Digital Teaching Assistants:  software tools that facilitate clinical coursework Classroom management/activities Practice problem generation/grading ChatBots as personalized tutors AR/VR simulations: incorporated  into traditional coursework Clinical Scenarios as Role Playing Games 3D Physiology models to make diagnostic data more conceptual Physiological Measurements: to better quantify real-time effects of educational interventions Neuroscience of Learning Wearables for diagnostic simulations   …

---

Current Members

• Russ Palmer
• Katie Smith
• Matthew Schmidt
• Richard Lamb
• James Moore
• Eugene Douglass
• Joshua Caballero

 

---

Software Tools

• Patient Generators: 
  • Simulate Drug Blood Concentrations for input regimens (PHRM4211)
• Case Generators: 
• Lecture Assistant Apps:
• Class Room Management Apps:
  • Random Team Selector

 

…

---

Cleaned Datasets

• Normal Patient Physiology Ranges
• Case Reports Datasets
• 2D Graphics for Lecture Design
• 3D models for Lecture Design
  • IV Pharmacokinetics Model
• Animations for Lecture Design
  • IV Pharmacokinetics Animation

 

…

…

---

Key Performance Indicators: summary statistics

 

KPI Category	KPI	Current Value
Research and Publications	Number of Published Papers
	Impact Factor of Journals
	Citations
	Conference Presentations
Collaboration and Engagement	Interdisciplinary Projects
	External Collaborations
	Collaborative Publications	%
	Workshops and Seminars
Funding and Grants	Research Grants Received	$
	Grant Applications Submitted
	Grant Success Rate	%
Data and Tools	Datasets Published
	Software Tools Developed
	Tool Adoption	downloads
Training and Development	Students Supervised
	Training Programs
	Skill Development	certificates
Impact and Outreach	Societal Impact
	Media Mentions
	Public Engagement	events
Operational Efficiency	Project Completion Rate
	Data Management Practices	% compliance
	Resource Utilization	% efficiency
Innovation and Excellence	Awards and Recognitions
	Innovative Solutions	breakthroughs
Feedback and Improvement	Stakeholder Feedback	satisfaction
	Continuous Improvement	# iterations

 

 

 

---

Key Performance Indicators: specific items list

 

KPI List: password protected

 

…

…

---

…

…