ArticleScientific reports2023
Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining the Role of Large Language Models in Orthopedics: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Deriving the OTA/AO fracture classification from routinely collected radiology reports using a large language model.OTA international : the open access journal of orthopaedic trauma · 2026Article
- Evaluation of Context-Aware Prompting Techniques for Classification of Tumor Response Categories in Radiology Reports Using Large Language Model.Journal of imaging informatics in medicine · 2026Article
- Error Detection in Emergency Radiology Reports Using a Large Language Model: Multistage Evaluation Study.Journal of medical Internet research · 2026Article
- Insufficient reporting quality in large language model studies in the field of radiology.Insights into imaging · 2026Article
- A randomized controlled trial of a WeChat-based artificial intelligence agent for postoperative care in orthopedic patients.NPJ digital medicine · 2026Article
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Artificial intelligence for immunotherapy response assessment in lung cancer using PET/CT reports.Japanese journal of radiology · 2025Article
- In-context learning enables large language models to achieve human-level performance in spinal instability neoplastic score classification from synthetic CT and MRI reports.La Radiologia medica · 2025Article
- Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.Journal of the Korean Society of Radiology · 2025Review
- Large language models for error detection in radiology reports: a comparative analysis between closed-source and privacy-compliant open-source models.European radiology · 2025Article
- Retrieval-augmented generation improves precision and trust of a GPT-4 model for emergency radiology diagnosis and classification: a proof-of-concept study.European radiology · 2025Article
- Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline.Journal of medical Internet research · 2025Review
- Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS digital health · 2025Article
- Article
- Artificial intelligence in clinical practice: a cross-sectional survey of paediatric surgery residents' perspectives.BMJ health & care informatics · 2025Article
- [Optimized interaction with Large Language Models : A practical guide to Prompt Engineering and Retrieval-Augmented Generation].Radiologie (Heidelberg, Germany) · 2025Review
- Evaluating AI performance in nephrology triage and subspecialty referrals.Scientific reports · 2025Article
- Article
- ChatGPT and radiology report: potential applications and limitations.La Radiologia medica · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While radiologists can describe a fracture's morphology and complexity with ease, the translation into classification systems such as the Arbeitsgemeinschaft Osteosynthesefragen (AO) Fracture and Dislocation Classification Compendium is more challenging. We tested the performance of generic chatbots and chatbots aware of specific knowledge of the AO classification provided by a vector-index and compared it to human readers. In the 100 radiological reports we created based on random AO codes, chatbots provided AO codes significantly faster than humans (mean 3.2 s per case vs. 50 s per case, p < .001) though not reaching human performance (max. chatbot performance of 86% correct full AO codes vs. 95% in human readers). In general, chatbots based on GPT 4 outperformed the ones based on GPT 3.5-Turbo. Further, we found that providing specific knowledge substantially enhances the chatbot's performance and consistency as the context-aware chatbot based on GPT 4 provided 71% consistent correct full AO codes for the compared to the 2% consistent correct full AO codes for the generic ChatGPT 4. This provides evidence, that refining and providing specific context to ChatGPT will be the next essential step in harnessing its power.
Indexed as
Identifiers
What 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.