ArticleBMC medical informatics and decision making2024
Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- The role of large language models in dental diagnosis, decision-making, and communication: A systematic review.The Japanese dental science review · 2026Review
- Comparative Evaluation of Large Language Models for Reporting Jaw Lesions on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Comparison of the diagnostic accuracy of dentists and ChatGPT in jawbone lesions.BMC oral health · 2026Article
- Clinical reasoning with machines: evaluating the interpretive depth of AI in urological case assessments.BMC urology · 2026Observational
- Generative Pre-trained Transformer: Trends, Applications, Strengths and Challenges in Dentistry: A Systematic Review.Healthcare informatics research · 2025Article
- Performance of Large Language Models ChatGPT and Gemini on Workplace Management Questions in Radiology.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluating the performance of ChatGPT in patient consultation and image-based preliminary diagnosis in thyroid eye disease.Frontiers in medicine · 2025Article
- Article
- ChatGPT and radiology report: potential applications and limitations.La Radiologia medica · 2024Review
- Innovation and application of Large Language Models (LLMs) in dentistry - a scoping review.BDJ open · 2024Article
- Generative pre-trained transformer (GPT)-4 support for differential diagnosis in neuroradiology.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
7 authors.
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Abstract
aimThis study aimed to assess the performance of OpenAI's ChatGPT in generating diagnosis based on chief complaint and cone beam computed tomography (CBCT) radiologic findings. MATERIALS AND
methods102 CBCT reports (48 with dental diseases (DD) and 54 with neoplastic/cystic diseases (N/CD)) were collected. ChatGPT was provided with chief complaint and CBCT radiologic findings. Diagnostic outputs from ChatGPT were scored based on five-point Likert scale. For diagnosis accuracy, the scoring was based on the accuracy of chief complaint related diagnosis and chief complaint unrelated diagnoses (1-5 points); for diagnosis completeness, the scoring was based on how many accurate diagnoses included in ChatGPT's output for one case (1-5 points); for text quality, the scoring was based on how many text errors included in ChatGPT's output for one case (1-5 points). For 54 N/CD cases, the consistence of the diagnosis generated by ChatGPT with pathological diagnosis was also calculated. The constitution of text errors in ChatGPT's outputs was evaluated.
resultsAfter subjective ratings by expert reviewers on a five-point Likert scale, the final score of diagnosis accuracy, diagnosis completeness and text quality of ChatGPT was 3.7, 4.5 and 4.6 for the 102 cases. For diagnostic accuracy, it performed significantly better on N/CD (3.8/5) compared to DD (3.6/5). For 54 N/CD cases, 21(38.9%) cases have first diagnosis completely consistent with pathological diagnosis. No text errors were observed in 88.7% of all the 390 text items.
conclusionChatGPT showed potential in generating radiographic diagnosis based on chief complaint and radiologic findings. However, the performance of ChatGPT varied with task complexity, necessitating professional oversight due to a certain error rate.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.