# Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning

arXiv: https://arxiv.org/abs/2402.15755 · HTML: https://mlsanity.com/paper/2402.15755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 67 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2308.09975/) (2023)
- [A Survey on Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2304.08130/) (2023)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [Instruct Me More! Random Prompting for Visual In-Context Learning](https://mlsanity.com/paper/2311.03648/) (2023)
- [Class Binarization to NeuroEvolution for Multiclass Classification](https://mlsanity.com/paper/2308.13876/) (2023)
- [Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation](https://mlsanity.com/paper/2306.08487/) (2023)
- [BioBERT Based SNP-traits Associations Extraction from Biomedical Literature](https://mlsanity.com/paper/2308.02569/) (2023)

## Built on by (most cited first, 1 of 1)

- [Enhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts from Clinical Texts](https://mlsanity.com/paper/2403.09722/) (2024)

## Abstract (arXiv)

Dental diseases have a significant impact on a considerable portion of the population, leading to various health issues that can detrimentally affect individuals' overall well-being. The integration of automated systems in oral healthcare has become increasingly crucial. Machine learning approaches offer a viable solution to address challenges such as diagnostic difficulties, inefficiencies, and errors in oral disease diagnosis. These methods prove particularly useful when physicians struggle to predict or diagnose diseases at their early stages. In this study, thirteen different machine learning, deep learning, and large language models were employed to determine the severity level of oral health issues based on radiologists' reports. The results revealed that the Few-shot learning with SBERT and Multi-Layer Perceptron model outperformed all other models across various experiments, achieving an impressive accuracy of 94.1% as the best result. Consequently, this model exhibits promise as a reliable tool for evaluating the severity of oral diseases, enabling patients to receive more effective treatment and aiding healthcare professionals in making informed decisions regarding resource allocation and the management of high-risk patients.