Chenyuan Li

Chenyuan Li

DDS-trained, PhD researcher, Focus on digital dentistry, healthcare data science, and AI product innovation.

Personal Statement

I work on Department of Health Data Science and Artificial Intelligence at UTHealth Houston, focusing on foundation models for dental electronic health records (EHR), healthcare data science, and AI-driven clinical decision support. My research interests include multimodal embeddings, RAG for clinical QA, dental treatment path prediction, and applied machine learning in healthcare.

My first-author and co-author research has been presented at international meetings, including IADR, AADOCR,FDI, and JASD, and published in International Journal of Bioprinting, Polymers, and other peer-reviewed journals, covering CAD/CAM, 3D printing in sports dentistry, dental biomaterials, and digital dentistry workflows.

My research has contributed to CAD/CAM innovation, 3D printed sports mouthguards, and dental polymer durability evaluation, influencing both clinical workflow optimization and material science development. It has also been recognized by awards such as Wise Scholarship for Pioneer Research in JST and the Neo Pharmaceutical Industry Award in JASD.

I serve as a researcher at alma mater Institute of Science Tokyo, supporting professors and junior researchers in project design, manuscript development, and peer review.

Current Research Focus: From Foundation Models to Fabrication

  1. Dental Foundation Models (The BigMouth Project)[Project number:BM-DR43]: Developing the first large-scale foundation model for dentistry trained on 4M+ longitudinal EHRs. Leveraging MedEncoder and Mamba architectures to enable multi-institutional clinical prediction and patient trajectory analysis.
  2. AI-Driven Automated Fabrication Copereated with Computer Science Department of University of Houston: Engineered an end-to-end pipeline for 3D-printed oral appliances (e.g., mouthguards). My approach decouples learning from geometry, using AI to predict control parameters that guide deterministic operations, ensuring 100% manufacturability and precise fit.
  3. Cognitive Pattern Analysis (Mental Health AI): Investigating Beck’s Cognitive Theory in social media using NLP. My recent work systematically evaluated DeBERTa (97.9% accuracy) versus ChatGPT-5 for identifying risk patterns in unstructured text, establishing a new benchmark for AI-assisted CBT research.
  4. Patient-Centric AI (DORIN-UT): Developing the Dental and Oral Intelligence Network, an interactive conversational agent designed to bridge the gap between complex clinical data and patient understanding (e.g., explaining implant procedures and costs).

For more information, please check out my CV and research updates.