3. Language Model Integration in Dental Practice (restructuring data)

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Last updated 7:46 PM on 9/3/26
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10 Terms

1
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define language model (LM)

Any tool, system or approach that predicts the next word from a sequence of words

  • givens → LM → prediction


2
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what does it mean to train an LM?

when LM is “rewarded” when its top predictions are close to the true value

3
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what are CNN language models?

deep learning models that apply convolutional neural networks to text sequences instead of images to process and understand natural language

ML to interpret CBCT scans, intraoral photos, radiographs to assist w diagnostics

transformer-based models like YOLOv8 been used to identify certain features

4
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CNN vs transformer-based language modeling

A transformer language model uses a self-attention mechanism to process entire sequences of text at once, while a Convolutional Neural Network (CNN) uses sliding filters to scan data piece-by-piece for local spatial patterns

rn older CNN predominates but transformer-based models like YOLOv8 have been used to identify certain features in imagery

5
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which image format for AI in diagnostic imaging and analysis?

  • deep algorithms

  • automated toth segmentation

  • generative AI for report simplification


CBCT interpretation

6
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which image format for AI in diagnostic imaging and analysis?

  • machine learning algorithm

  • detection of abnormalities, cysts, tumors

  • AI-assisted workflow optimization


intraoral photos

7
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which image format for AI in diagnostic imaging and analysis?

  • dental radiographs

  • caries and perio dx detection

  • super-resolution processing


dental radiographs

8
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how can AI help with EHRs

massive amnt of EHRs require lots of human labor to sort and classify, especially bc it is unstructured and AI can not only sort and classify but read handwritten notes so you can look up pt name and keyword to find info

9
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limitations of AI in dentistry

  • privacy

  • cost

  • validation (tx plans)


10
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opportunities for AI in dentistry

  • as models get smaller, integration will become easier

  • Creation and curation of more diverse datasets

  • robotic and embodied AI in sx and tx, augmented and virtual reality for training