ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

In plain words

Dentists collect a lot of information: patient notes, X-rays, scans, and even recordings of how a patient speaks. Most of it is hard for a computer to use, because the notes are free text and the images and sounds are in separate formats. This review asks what happens if one large language model, of the kind behind ChatGPT, can read text, look at images and listen to sounds at the same time.

The authors walk through many possible uses. A model could pull key facts out of old records, write reports from a short list of keywords, and make up artificial patient records so that real ones need not be shared. It could also look at an X-ray and answer questions about it, or look at a recording of a patient's voice and suggest what might be wrong. They sketch a full system that combines all of these inputs into a diagnosis and a treatment plan.

The paper does not build or test such a system with measurements. Its examples are single demonstrations, and it names the main risks: wrong answers, biased training data, patient privacy, and the cost of running large models.

TL;DR

A review and perspective article, not a method paper. It describes how multi-modal LLMs could support dental text mining, report generation, synthetic data, image and audio diagnosis, and treatment planning p. 3-10. It gives example outputs but no evaluation, no training details and no code fig. 1-12. Use it as a list of possible applications and open problems, not as evidence that any of them works.

Problem

Dental records mix free text with structured data, so simple analysis misses much of it p. 3. Documentation is written by hand, and clinical text is hard to share because of privacy rules p. 4. The authors also say that AI in dentistry is slower and more limited than in other fields, mainly because patient data is hard to obtain, and that single-input, single-output models need retraining whenever new data arrives p. 1, p. 4.

Method

The paper has no method of its own. It is organised as a survey of applications and then one illustrative system p. 3-10.

Text-based deployments p. 3-4

• Record analysis with text mining. An LLM extracts facts such as a patient's illnesses or symptoms from free-text notes p. 3 fig. 1.
• Treatment planning with natural language reasoning (NLR). The model answers questions about a patient's history, such as likely causes of pain or possible side effects of a drug p. 3 fig. 2.
• Medical documentation with natural language generation (NLG). The model writes a formal report from a list of keywords p. 3-4 fig. 3.
• Synthetic quasi-EHR data. A model generates artificial patient records, which the authors present as a way to share data while protecting privacy p. 4-5 fig. 4. They say LLMs can be guided to produce different patient profiles, histories and outcomes p. 5.

Cross-modal deployments p. 5-9

• Visual grounding. A vision-language model (ALBEF) matches text prompts to regions of a dental image and shows them as heat maps p. 6 fig. 5.
• Visual question answering. A BLIP-2 model is given an X-ray and questions, and it produces captions and answers p. 6 fig. 6.
• Segmentation with 3D reconstruction. A segmentation model finds the nasal cartilage in MRI slices, and the slices are rebuilt into a 3D model p. 6-7 fig. 7.
• Visual data generation. A diffusion model, which the authors describe as DALL-E 2, makes synthetic panoramic dental X-rays from a short text description such as "an alveolar cleft" p. 8 fig. 8.
• Audio-language deployment. Waveforms and spectrograms of speech, such as "baba" and "mama", are plotted with TorchAudio. They are compared between normal speakers and patients with velopharyngeal insufficiency p. 8 fig. 9. An LLM is given such plots and a question about possible causes p. 8-9 fig. 10.
• Other possibilities. Biopsy images, blood test results and gene sequences could be given to a model. These are described in general terms only p. 9.

Illustrative fully automatic system p. 9-10

The authors propose three input modules: images (dental X-ray or cone-beam CT), patient voice, and old records. The voice goes through speech-to-text, waveform and spectrogram plots, and amplitude and frequency analysis. The results feed a single LLM that outputs a diagnosis and a treatment fig. 11 p. 9.

# Illustrative multi-modal dental pipeline  [fig. 11] [p. 9-10]
# Input:  dental image (X-ray or CBCT), patient voice, old record
# Output: diagnosis and treatment text

def dental_pipeline(image, voice, old_record):
    # 1. Evaluate the tooth in the image with a vision-language model  [p. 9]
    image_findings = vision_language_model(image)

    # 2. Plot the voice as waveform and spectrogram  [p. 9] [fig. 11]
    waveform, spectrogram = plot_audio(voice)

    # 3. Analyse amplitude and frequency of the voice  [p. 9] [fig. 11]
    audio_findings = amplitude_and_frequency(waveform, spectrogram)

    # 4. Turn the patient's spoken narrative into text  [p. 9]
    narrative = speech_to_text(voice)

    # 5. Extract and summarise facts from the old record  [p. 9] [fig. 11]
    record_facts = natural_language_reasoning(old_record)

    # 6. The LLM combines all text inputs into a diagnosis  [fig. 11]
    diagnosis = large_language_model(
        image_findings, audio_findings, narrative, record_facts)

    # 7. Ask the LLM for a treatment plan from the diagnosis  [fig. 12] [p. 10]
    treatment = large_language_model(diagnosis)

    return diagnosis, treatment

Step 7 is not spelled out in the paper. The caries case passes the instruction "As a dentist, if you diagnose dental caries (tooth decay) in a patient, what should I do?" to the LLM, and the output lists seven steps fig. 12 p. 10.

Synthetic record generation (ChatGPT 3.5) p. 4 fig. 3 fig. 4

# Synthetic dental report from keywords  [fig. 3] [fig. 4] [p. 4]
# Input:  keyword list (complaint, history, examination, diagnosis, treatment)
# Output: synthetic narrative report

def synthetic_report(keywords):
    # 1. Build the prompt from the keywords; format not stated  [fig. 3]
    prompt = build_prompt(keywords)

    # 2. Generate text with top-p = 1; other settings not stated  [p. 4]
    report = llm_generate(prompt, top_p=1)

    # 3. Remove generation artifacts with manual rules  [p. 4]
    report = post_process(report)

    # 4. The framework adds a human evaluation step  [fig. 4]
    return report

Hyperparameters

Only the generation settings for synthetic records are given, and most values are missing. The illustrative system has no training settings.

Results

Author-reported. The paper has no quantitative results: there is no dataset, no baseline, no model size and no metric. Each figure shows one example output, chosen by the authors. The table lists them.

Dataset: none. Baseline: none. Hardware: not stated.

Paper vs code

No official code. The paper has no repository link, the arXiv metadata has no code link, and there is no project page. A GitHub repository search for "ChatGPT dentistry" returned no results. No code was cloned.

Pitfalls

• Not a method. The "fully automatic" system is a concept diagram fig. 11 with no design, training data or evaluation. Do not implement it as described.
• Examples are not results. Every figure is a single illustrative output. The paper says nothing about accuracy, error rate or how the examples were picked fig. 1-12.
• Validity claim without numbers. The text says generated reports have been validated and cites two papers p. 3. This paper gives no validation data.
• Synthetic-data settings are incomplete. Five of the six generation settings are missing values, and the post-processing rules are not given p. 4 fig. 4.
• Model identity is inconsistent. The synthetic records use "ChatGPT 3.5" p. 4, one audio example uses GPT-4 p. 8, and the other figures name only "a ChatGPT-like LLM", "LLM" or no model at all fig. 1-3 fig. 12. The abstract describes ChatGPT as a variant of GPT-4 p. 1. Model and version must be taken per figure, and most are not stated.
• Acronym error. The text expands ALBEF as "A Lite BERT for Adaptive Embedding Factorization" p. 5. That expansion is for ALBERT. Figure 5 names the model "Align before fuse (ALBEF)" fig. 5.
• Missed findings. The caries example misses possible bone loss, which the authors note themselves p. 10. For the audio example the text says the answer shows little about velopharyngeal insufficiency, though it mentions muscle dysfunction p. 8.
• Privacy and bias are not tested. The paper proposes synthetic records, a local model (LLaMA is named as an example) and a human-in-the-loop check, but it does not measure any of them p. 4 p. 11.
• Static knowledge. The authors note that an LLM cannot learn about events after training and cannot check its outputs against current data p. 11.
• Cost claims. The paper says fine-tuning is not needed and that a sparse expert model would reduce compute p. 6 p. 11. Neither claim is measured.
• Ethics and consent. The authors recommend informing patients and getting consent before using their data p. 11. Any implementation should treat this as a legal requirement, not an option.

Lineage

• Builds on: general LLMs and multi-modal models the paper names: GPT-4 p. 2 [ref. 51], BLIP-2 (arXiv:2301.12597) p. 6 [ref. 39], ALBEF p. 6 fig. 5, and Whisper p. 2 [ref. 49].
• Compared against: none.
• Superseded by: not stated.