# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 4 Discussion
### Limitations  
At the time of conducting this review, there is limited original research on the application of ChatGPT in dentistry.  
The most significant limitation in comparing the selected research results lies in the methods used. Different LLMs, or different versions of the same LLM, vary in the quality of their responses. This issue, along with the automatic \"contextual refinement\" performed by LLMs, makes comparisons between papers difficult, if not impossible, unless each material and methods section specifies the exact version and build of the software used and details any contextual refinement or prompt engineering applied, if any. Another limitation is the model's tendency to generate \"hallucinations\", that is, responses that seem plausible but are actually incorrect or invented. This phenomenon represents a significant challenge and can lead to erroneous diagnoses or inaccurate medical advice if the model is used without human supervision. To address this issue, various methods can be employed. One is fine-tuning on curated data, using high-quality datasets to ensure reliable information. Retrieval-augmented generation (RAG) combines the model's generative abilities with external sources for more accurate responses. Model calibration adjusts prediction confidence levels for reliability. Prompt engineering guides models to better outputs by crafting precise questions and instructions. Human-in-the-loop feedback involves humans correcting hallucinations, aiding the model's learning. Post-processing techniques filter out hallucinated content for more accurate final responses.  
ChatGPT is not designed to interpret medical images such as intraoral X-rays or orthopantomographs, which limits its usefulness as a direct diagnostic tool. Another critical aspect is the necessity for constant system updates to keep the knowledge up-to-date with recent developments in the medical field, thereby avoiding the dissemination of outdated or incorrect information. The lack of direct access to scientific databases limits its ability to provide responses based on the updated medical literature, making the experts' intervention indispensable to ensure the accuracy of the information used and generated by the model.  
Prompt engineering, while a powerful tool for guiding AI systems, does come with its own set of challenges that might be seen as limitations to AI's use and to study comparison. The end-user should be aware of the training datasets used, since their quality may impact the performance of the model or may present biases. The model may not behave as intended due to prompt formulation, or communication barriers may rise when it does not fully understand the intent or the subtle distinctions behind the prompts. It can be challenging to obtain the exact output you want from the AI model in the first attempt.  
\"Contextual understanding\" or \"contextual refinement\" is when LLMs refine their answers using the context from earlier interactions to provide more accurate and relevant responses. If research studies do not account for this automatic adjustment, the results may be biased. Therefore, determining the best way to initiate a prompt, and in the end, understanding and evaluating the reasoning behind the generated responses can be tricky. These challenges highlight the complexity of prompt engineering and the need for careful consideration and ongoing refinement to achieve optimal, comparable results.