Why AI matters at this scale

The Ohio State University College of Dentistry is a large academic institution that combines dental education, patient care, and research. With over 500 employees and a century-old legacy, it operates a significant clinical practice serving thousands of patients while training the next generation of dentists. At this scale, inefficiencies in clinical operations, educational delivery, and administrative processes can compound, affecting financial sustainability and educational outcomes. AI presents a transformative lever to enhance precision in diagnostics, personalize learning, optimize resource use, and improve patient access—critical for maintaining competitiveness and fulfilling its public mission in an era of tightening budgets and rising expectations for healthcare and education.

Concrete AI opportunities with ROI framing

1. AI-enhanced diagnostic imaging: Implementing machine learning algorithms for analyzing dental radiographs and cone-beam CT scans can increase diagnostic accuracy, reduce interpretation time, and serve as a training aid for students. ROI comes from improved patient outcomes (reducing costly complications), higher clinic throughput, and enhanced educational effectiveness, potentially attracting more students and patients.

2. Predictive analytics for clinic operations: Using historical appointment data, patient demographics, and external factors (e.g., weather, local events), AI models can forecast no-shows and last-minute cancellations. By optimizing scheduling and enabling proactive interventions, the clinic can reduce lost revenue (estimated at $200–$500 per missed appointment) and improve provider utilization, directly boosting operational margins.

3. Intelligent adaptive learning platforms: Deploying AI-driven simulation and assessment tools in preclinical training can provide personalized feedback to students, identifying skill gaps and recommending targeted practice. This accelerates competency development, reduces faculty time spent on repetitive coaching, and may improve licensure exam pass rates—key metrics for program reputation and enrollment.

Deployment risks specific to this size band

As a large public university entity, the college faces unique risks in AI adoption. Budget and procurement cycles are often annual and rigid, making it difficult to fund experimental pilots or subscribe to emerging SaaS AI tools. Legacy system integration is a major hurdle, as clinical EHRs, student information systems, and financial platforms may be outdated or siloed, requiring costly middleware or custom API development. Change management across a diverse workforce—from tenured faculty to clinical staff—demands extensive training and clear communication of AI's value to overcome resistance. Data governance and compliance are complex, given the blend of HIPAA-protected health information, FERPA-protected student records, and research data; ensuring ethical AI use and avoiding bias in models requires dedicated oversight. Finally, talent gaps in AI expertise may necessitate partnerships with OSU's engineering or data science departments, or external vendors, introducing coordination challenges and potential loss of institutional control.