Research
AI and orthodontic treatment planning
This study evaluated whether general-purpose large language models could produce orthodontic treatment plans comparable to human standards. Cases were presented to LLMs in a published-style format; board-certified orthodontists scored outputs using a custom rubric. Results indicated that AI may serve as an adjunct for ideation but does not replace specialist clinical judgment. Work was presented at the CSDA Annual Meeting (2024) and UConn Medical/Dental Research Day, and received the Dental Student Research Society Award and an International Research Star Award.
The study is peer-reviewed and published in Cureus (July 31, 2025). DOI: 10.7759/cureus.89149.
Clear aligners and micro-CT
With mentor Dr. Niloufar Azami, I contributed to fabrication of in-house aligners from 3D-printed models. Micro-CT imaging and AI-assisted superimposition were used to quantify how closely the aligner intaglio surface matched the digital treatment plan.
Vertical facial pattern and occlusion
With mentor Dr. Aditya Tadinada, I used cephalometric analysis to categorize vertical facial patterns and examine their relationship to Angle’s classification, using diagnostic measures including overbite, overjet, crowding, and eruption patterns from orthodontic records. Presentation scheduled for the CSDA Annual Meeting (2026).
Additional research interests
- AI applications in orthodontic case monitoring and follow-up
- Orthodontic biomechanics, wire systems, and bracket design
- Oral biomaterials and clinical performance
Clinical informatics
Collaborated on development of an LLM-based dental question-and-answer tool to explore responsible use of generative AI in patient education contexts. A demo is linked in the site navigation and at makarasorel.com/chatbot.html.
