Call for Abstracts is Now Open!
The AI Symposium invites abstracts that explore how artificial intelligence is transforming health care, research, education, operations, and human performance. Submissions should highlight innovative ideas, practical applications, emerging evidence, and lessons learned that advance responsible, effective, and scalable use of AI across the health ecosystem. Abstracts may align with one or more of the following symposium tracks.
Abstracts may be submitted for consideration as either poster presentations or conference talks. All submissions will be reviewed by a UT review group with relevant expertise. Selected abstracts will be invited for oral presentations as part of the symposium program. The review process will consider the abstract’s relevance to the symposium themes, clarity of purpose, innovation, potential impact, and contribution to advancing thoughtful and responsible use of AI.
The Future Patient Journey (Track 1)
This track explores how AI is reshaping the patient experience across the full continuum of care—from first digital touchpoint to personalized follow-up, remote monitoring, and hybrid care models. Abstracts should highlight approaches that make care more accessible, responsive, equitable, and aligned with evolving consumer expectations.
- Digital front doors, personalized care pathways, patient engagement, and navigation
- Remote, hybrid, and virtual care models, including applications for rural and underserved communities
- AI-enabled approaches that respond to changing consumer expectations for access, convenience, transparency, and continuity
The AI-Enabled Clinician, Care Team, and Next-Generation Care Delivery (Track 2)
This track focuses on how AI, robotics, sensors, and ambient intelligence can support clinicians and care teams while advancing new models of care delivery. We welcome abstracts that demonstrate practical, near-term clinical value as well as forward-looking models for hospitals, homes, and intelligent care environments.
- Clinician copilots, documentation support, decision support, workflow automation, and burnout reduction
- Robotics, sensors, and ambient intelligence with clear clinical or operational value
- Hospital of the future concepts, intelligent care environments, hospital-at-home, and AI as an integrated member of the care team
The Value of AI (Track 3)
This track examines how organizations can define, measure, and realize the value of AI in the health care system. Abstracts should move beyond experimentation to address return on investment, sustainable implementation, operational impact, payment and regulatory considerations, and value created through integration into care delivery.
- Revenue cycle, prior authorization, staffing, scheduling, throughput, supply chain, and other operational use cases
- Methods for assessing ROI, outcomes, adoption, and organizational readiness
- CMS, regulatory, reimbursement, and payment model considerations for AI-enabled care
Leveraging AI to Improve Health, Wellness, and Performance (Track 4)
This track highlights the use of AI to improve health, wellness, human performance, and resilience across clinical, community, athletic, and everyday settings. Abstracts may address personalized prevention, diagnosis, treatment, coaching, monitoring, and smart environments that support better outcomes over time.
- Personalized prevention, diagnosis, treatment, and care planning
- Digital coaching, continuous monitoring, ambient sensing, and smart environments
- Training optimization, recovery support, injury prediction, prevention, and performance enhancement
From Discovery to Real-World Impact (Track 5)
This track focuses on the pathway from AI discovery and development to real-world adoption, scaling, and measurable impact. Abstracts should address the translational work required to move promising tools across the gap between research, validation, regulation, implementation, and sustained use.
- Strategies, teams, and partnerships that bridge the gap between discovery and implementation
- Validation, evaluation, deployment, and scaling of AI tools in real-world settings
- FDA, regulatory, ethical, operational, and governance considerations that influence adoption and impact
AI and the Future of Education and Training (Track 6)
This track examines how AI is changing education, training, and workforce development across health care, higher education, research, and industry. Abstracts should explore both the opportunities and challenges of preparing learners and professionals for an AI-enabled future.
- Training the workforce of the future for AI-enabled environments
- AI’s impact on higher education, clinical education, continuing education, and professional development
- New learning models, assessment approaches, simulation, tutoring, and skill development enabled by AI
