The research team, led by emergency physicians at Van Training and Research Hospital, University of Health Sciences, enrolled 412 consecutive patients aged 0 to 17 years who presented with acute elbow trauma between December 2025 and May 2026. This was not a retrospective exercise on curated images; it was a prospective, single-center diagnostic accuracy study designed to reflect the real conditions of an emergency department. De-identified anteroposterior and lateral radiographs were exported from the hospital’s picture archiving and communication system as PDF files and presented to GPT-5.2 Instant through the ordinary ChatGPT web interface, once per case, using a fixed prompt and no task-specific fine-tuning. In other words, the model was tested exactly as a curious clinician might use it today, without any specialized medical training or optimization.

To establish the truth against which the AI’s answers were judged, the researchers built a composite reference standard. Two emergency physician readers assessed each case independently, and when their assessments agreed, that consensus served as the final diagnosis. When the two readers disagreed, the case went to an independent radiologist for review and adjudication. This layered approach is important because pediatric elbow radiographs are notoriously tricky: growth plates, ossification centers that appear at different developmental stages, and subtle supracondylar fractures can all mimic or mask injuries. The final adjudicated reference standard identified fractures in 41 of the 412 patients, a prevalence of 10.0 percent, meaning that roughly nine out of every ten children in the cohort had no fracture at all.

Against that benchmark, GPT-5.2 Instant classified 74 cases, or 18.0 percent of the cohort, as fracture-positive, a substantial overcall. The model correctly identified 23 of the 41 true fractures but missed 18, yielding a sensitivity of 56.10 percent with a 95 percent confidence interval of 39.75 to 71.53. That figure means the model failed to detect nearly half of the actual fractures. Its specificity was 86.25 percent, which sounds respectable until one considers that in a population with only 10 percent prevalence, even modest overcalling produces many false positives. The positive predictive value, the probability that a fracture-positive call was truly a fracture, was just 31.08 percent, meaning roughly two out of three fracture alerts from the model were wrong.

The full statistical picture reinforces the sobering conclusion. The negative predictive value was 94.67 percent, which might tempt some to suggest the model could serve as a rule-out tool, but the authors explicitly warn against that interpretation. The negative likelihood ratio of 0.51 indicates that a negative result from the model reduces the probability of fracture only modestly, far from the near-certainty required for a safe rule-out strategy in pediatric trauma. The positive likelihood ratio of 4.08 is similarly unimpressive by diagnostic standards, where values above 10 are typically considered useful. Overall accuracy was 83.25 percent, and agreement with the reference standard, measured by Cohen’s kappa, was limited at 0.312, a level conventionally described as fair at best.

Why does a model capable of passing medical licensing examinations and generating fluent clinical summaries struggle so visibly with pediatric elbow X-rays? The technical answer likely lies in the nature of the task and the way the model was deployed. Pediatric elbow radiography is one of the most nuanced domains in musculoskeletal imaging. The developing skeleton presents multiple ossification centers whose appearance and fusion follow a well-known but variable sequence, and distinguishing a normal growth plate from a subtle fracture line requires pattern recognition honed on thousands of similar cases. General-purpose multimodal large language models are trained on broad internet-scale data rather than curated radiology archives, and they were not optimized here with task-specific fine-tuning. The PDF-based input pathway through a web interface may also have introduced compression and resolution constraints that degrade fine bony detail, though the study’s design deliberately reflected realistic, non-specialist access conditions.

The study’s secondary analyses add further caution. The researchers explored whether the model could classify fracture subtypes, including the three grades of supracondylar fracture, and whether it could offer sensible recommendations about surgical importance and immobilization. The supplementary data show that agreement on these management-related classifications was similarly limited, and the authors note that the surgical-importance analysis was exploratory because it included only ten physician-positive cases and three true-positive GPT classifications, making the resulting positive likelihood-ratio estimate unstable. Nursemaid’s elbow, a common pediatric injury that is primarily a clinical rather than radiographic diagnosis, was not evaluated as a radiographic target. These caveats matter because the appeal of a general-purpose AI assistant in the emergency department is precisely that it might help with triage and management, not just detection.

The authors are careful about what their findings do and do not show. They conclude that under the reported access and input conditions, GPT-5.2 Instant demonstrated limited sensitivity and agreement for pediatric elbow fracture detection, insufficient to support independent diagnostic or rule-out use. They also emphasize that clinician-AI collaboration was not evaluated in this study and requires prospective investigation. That distinction is crucial: the question is not whether AI can replace the radiologist’s eye, but whether a human-machine partnership could improve safety and efficiency. A model that misses nearly half of fractures on its own might still add value if it prompts a tired clinician to look more carefully, but proving that requires different study designs that measure outcomes when the model operates alongside, rather than instead of, human judgment.

The study also carries methodological strengths worth noting. It was prospective, meaning patients were enrolled according to a pre-specified protocol rather than selected retrospectively from archives. It used consecutive sampling, reducing selection bias. The ethics approval from the Non-Interventional Clinical Research Ethics Committee of Van Training and Research Hospital, granted on 24 October 2025, covered the radiographic image-input and reference-standard adjudication procedures before enrollment began, and written informed consent was obtained from a parent or legal guardian of every participant. The work was conducted in accordance with the Declaration of Helsinki, and the authors received no external funding, declaring no competing interests. Confidence intervals for predictive values were calculated using the logit-binomial method, likelihood-ratio intervals on the logarithmic scale, and F1-score intervals by nonparametric bootstrap resampling, reflecting careful statistical practice.

For the broader conversation about AI in medicine, the message is neither dismissal nor endorsement but calibration. The study was accepted for oral presentation at EUSEM 2026 in Paris, signaling that the emergency medicine community considers the question timely. Multimodal large language models are evolving rapidly, and future versions, or fine-tuned medical derivatives of them, may perform substantially better on this task. But this prospective, real-world evaluation demonstrates that the gap between impressive general capabilities and reliable diagnostic performance in a specialized pediatric imaging domain remains wide. Until models can demonstrate high sensitivity and strong agreement under realistic conditions, the reading of children’s elbow radiographs should remain firmly in human hands, with AI serving, at most, as an unproven assistant rather than an independent diagnostic authority.

Subject of Research: Diagnostic accuracy of the GPT-5.2 Instant multimodal large language model for detecting pediatric elbow fractures on radiographs

Article Title: Diagnostic performance of GPT-5.2 ınstant for pediatric elbow fracture detection on radiographs: a prospective single-center diagnostic accuracy study

Article References: Taş, O., Yorgun, M., Aktaş, R. S., Büyükkaya, M. Ş., & Şahin, M. (2026). Diagnostic performance of GPT-5.2 ınstant for pediatric elbow fracture detection on radiographs: a prospective single-center diagnostic accuracy study. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02788-0

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02788-0

Keywords: GPT-5.2 Instant, pediatric elbow fracture, radiography, diagnostic accuracy, large language model, emergency department, artificial intelligence, BMC Medical Imaging, supracondylar fracture, ChatGPT, medical imaging, prospective study