Summary: This study evaluated the post-deployment performance of Aidoc, an artificial intelligence (AI) system for intracranial haemorrhage (ICH) detection, and investigated the utility of ChatGPT-4 Turbo as an automated monitoring tool. Using over 330,000 head CT examinations across 37 radiology practices, the authors compared Aidoc’s classifications with radiologists’ reports and structured outputs generated by ChatGPT. Results demonstrated that ChatGPT achieved high diagnostic accuracy (AUC 0.996), effectively extracting ICH-related findings from radiology reports and identifying discrepancies between human and AI outputs. Scanner manufacturer and imaging artefacts strongly influenced Aidoc’s false positives, while radiologist performance improved when aided by the system. The findings underscore the importance of continuous monitoring of AI models in clinical practice and highlight large language models as scalable, cost-efficient tools for safeguarding diagnostic reliability.
Keywords: AI intracranial haemorrhage detection; ChatGPT medical imaging; Radiology performance monitoring; Large language models healthcare; Automated diagnostic evaluation; Post-deployment AI assessment.
Introduction
The study by Rohren et al. presents an ambitious and timely investigation into the post-deployment performance of Aidoc, a commercial AI system for intracranial haemorrhage (ICH) detection, while simultaneously evaluating the capacity of ChatGPT-4 Turbo to monitor this AI’s clinical reliability. The work is situated within an important area of radiological practice, where accurate and rapid diagnosis of ICH can directly influence patient outcomes, given the high mortality and morbidity associated with delayed or missed diagnoses.
Strengths of the Study
The most compelling strength lies in its scale. Analysing over 330,000 head CT examinations across 37 radiology practices provides rare breadth and external validity for a post-deployment evaluation. Most published AI validation studies rely on limited, single-centre datasets, often under idealised conditions. By contrast, this multi-centre design reflects the variability and imperfections of real-world practice, strengthening the credibility of the findings.
The integration of ChatGPT-4 Turbo as a monitoring tool is both innovative and pragmatic. By extracting structured information from free-text radiology reports, the large language model (LLM) demonstrated very high diagnostic accuracy (AUC 0.996) with minimal error rates. This suggests that such systems could offer a scalable, low-cost alternative to labour-intensive manual audits traditionally used to monitor AI drift in clinical practice. The study also identified specific factors contributing to false positives in Aidoc’s outputs, such as scanner manufacturer and imaging artefacts, providing actionable insights for developers and healthcare providers.
Equally significant is the demonstration that combining Aidoc with radiologist oversight produced improved diagnostic performance (sensitivity 0.936, specificity 1.0), reinforcing the value of human–AI collaboration rather than replacement.
Limitations and Areas of Concern
However, several limitations temper the conclusions. Firstly, although the dataset was vast, the ground truth was derived from a relatively small subsample of 200 cases. While this approach was practical, it inevitably introduces sampling variability and may not fully capture the spectrum of false negatives or subtle haemorrhages. The study’s statistical treatment of this issue was transparent, but larger validation cohorts would provide firmer confidence in the reported accuracy of both ChatGPT and Aidoc.
Secondly, the authors’ focus on Aidoc-positive flagged cases inflates the observed false positive rate and complicates comparisons with prior literature. A more balanced sampling strategy, incorporating both positive and negative cases, would have provided a clearer picture of overall system performance in practice.
The reliance on ChatGPT’s extraction from only the Impression section of reports also resulted in at least one missed ICH, highlighting the fragility of prompt design. This methodological choice underlines both the promise and the risk of using LLMs for clinical monitoring—small prompt adjustments can shift results meaningfully, and reproducibility across future model versions remains uncertain.
An additional concern is the potential for automation bias, where radiologists deferred to erroneous AI-positive classifications. While only a single case was noted, the broader implications are serious: without robust oversight and interpretive independence, AI integration could undermine rather than strengthen diagnostic safety.
Context and Clinical Relevance
The study contributes to the growing recognition that AI models are dynamic entities, vulnerable to performance drift due to evolving imaging protocols, scanner heterogeneity, and shifting patient demographics. The demonstration that ChatGPT can act as a real-time auditing mechanism is particularly noteworthy given the prohibitive costs of traditional quality assurance. Extracting structured data from 900 pathology reports for less than $30, as cited by the authors, contrasts starkly with the thousands of dollars required for radiologist review.
Nevertheless, the study stops short of addressing key implementation questions. How often should such monitoring be performed? What thresholds should trigger retraining or withdrawal of an AI model? And crucially, how should results be communicated to radiologists without fostering over-reliance on the system? These remain unanswered but are essential for safe clinical integration.
Conclusion
Rohren et al. provide valuable evidence that large language models such as ChatGPT-4 Turbo can enhance the monitoring of AI systems in radiology, offering scalability and efficiency that manual audits cannot. The findings highlight both the strengths and weaknesses of Aidoc in ICH detection, particularly the influence of scanner-specific factors on false positives.
The study’s innovative design will likely stimulate further exploration of LLMs as meta-AI tools in clinical environments. Yet, limitations in ground-truth sampling, methodology, and reproducibility underscore the need for cautious interpretation. The broader message is clear: AI in radiology is not static, and continuous, well-structured monitoring—augmented but not replaced by LLMs—is essential for maintaining diagnostic reliability.
For now, the integration of AI into neuroradiology should be seen not as a technological endpoint but as an evolving partnership between human expertise and machine learning, with oversight, transparency, and accountability at its core.
Reference
Rohren E, Ahmadzadeb M, Colella S, Zuluaga C, Ramis P, Ghasemi-Rad M, et al. Post-deployment monitoring of AI performance in intracranial hemorrhage detection by ChatGPT. Radiology. Published online August 11, 2025. doi:10.1148/radiol.253456
Disclaimer
This article is intended for educational and informational purposes only. It summarises and comments on the published study by Rohren et al. concerning the use of ChatGPT-4 Turbo to monitor the performance of Aidoc in detecting intracranial haemorrhage. The content does not constitute medical advice, regulatory guidance, or a substitute for professional clinical judgement.
Neither the author(s) of this commentary nor OpenMedScience make any warranties or representations regarding the accuracy, completeness, or reliability of the information discussed. Readers should interpret the findings in the context of the study’s stated limitations and the broader evidence base. The discussion of ChatGPT-4 Turbo or any other AI system does not imply endorsement or guarantee of performance in clinical practice.
Healthcare providers are advised to exercise independent professional judgement when making diagnostic or treatment decisions and to comply with applicable clinical governance, ethical, and regulatory requirements.
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