ArticleBMC medical informatics and decision making2026
Comparative analysis of generative pre-trained transformers for text- and image-based cephalometric prompts using a novel Artificial Intelligence Based Diagnosis and Treatment Planning Index (AIDTI).
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Classification Performance of General-Purpose Multimodal Large Language Models Across Orthodontic Radiographic Tasks: A Comparative Study of ChatGPT, Gemini, and Claude.Medicina (Kaunas, Lithuania) · 2026Article
- Patient perspective regarding the role of social media, offline reputation, and practitioner characteristics in orthodontist selection: a cross-sectional study.BMC oral health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe aim of this study was to compare the response capabilities of generative pre-trained transformers (GPTs) for identical lateral cephalograms (LCs) based on text- and image-based prompts, using the newly developed Artificial Intelligence Based Diagnosis and Treatment Planning Index (AIDTI).
methodsA total of 90 LCs from 30 cases each with skeletal Class I, II, and III malocclusions were included. The LCs were presented to GPT-4o, GPT-o3 pro, GPT-5, and GPT-5 pro in two different formats: text-based (numerical data including cephalometric analysis measurements) and image-based (direct image upload) prompts. The responses of the GPTs were evaluated using the newly developed AIDTI, scored on a 0-10 scale and consisting of five criteria: diagnostic accuracy, capability for differential diagnosis, clinical appropriateness of the proposed treatment plan, disclosure of risks and complications associated with the treatment plan, and ability to offer alternative treatment options.
resultsHigher performance was observed in text-based prompts, with the highest score achieved by GPT-5 pro (9.62 ± 1.13). By contrast, the performance of all GPTs on image-based prompts was notably lower, with the highest score being 4.16 ± 4.12 for GPT-o3 pro. Additionally, all models showed a tendency to randomly categorize malocclusions as Class II, suggesting systematic bias in their predictions.
conclusionAIDTI provides a structured and multidimensional framework for the concise, balanced, and clinically meaningful interpretation of GPT performance. Because GPTs are unable to directly analyze LCs, their use as independent or reliable supportive tools in orthodontics remains limited at this stage. GPTs may contribute more reliably to orthodontic practice only when orthodontists prompt the model with text-based cephalometric measurement data. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
42226172What OpenQuestion holds
Registered trials
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.