Hallucination filtering in radiology vision-language models using discrete semantic entropy

European Radiology, vol. 36, pp. 6107–6118

Abstract

OBJECTIVE: To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs) in radiologic image-based visual question answering (VQA). MATERIALS AND METHODS: This retrospective study evaluated DSE using two publicly available, de-identified datasets: the VQA-Med 2019 benchmark (500 images with clinical questions and short-text answers) and a diagnostic radiology dataset (206 cases: 60 computed tomography scans, 60 magnetic resonance images, 60 radiographs, 26 angiograms) with corresponding ground-truth diagnoses. GPT-4o and GPT-4.1 (Generative Pretrained Transformer) answered each question 15 times using a temperature of 1.0. Baseline accuracy was determined using low-temperature answers (0.1). Meaning-equivalent responses were grouped using bidirectional entailment checks, and DSE was computed from the relative frequencies of the resulting semantic clusters. Accuracy was recalculated after excluding questions with DSE > 0.6 or > 0.3. p values and 95% confidence intervals were obtained using bootstrap resampling and a Bonferroni-corrected threshold of p < 0.004 for statistical significance. RESULTS: Across 706 image-question pairs, baseline accuracy was 51.7% for GPT-4o and 54.8% for GPT-4.1. After filtering out high-entropy questions (DSE > 0.3), accuracy on the remaining questions was 76.3% (retained questions: 334/706) for GPT-4o and 63.8% (retained questions: 499/706) for GPT-4.1 (both p < 0.001). Accuracy gains were observed across both datasets and largely remained statistically significant after Bonferroni correction. CONCLUSION: DSE enables reliable hallucination detection in black-box VLMs by quantifying semantic inconsistency. This method significantly improves diagnostic answer accuracy and offers a filtering strategy for clinical VLM applications. KEY POINTS: Question Can DSE identify hallucination-prone questions and improve the reliability of black-box vision-language models in radiologic image-based VQA? Findings DSE filtering at a 0.3 threshold increased GPT-4o accuracy from 51.7% to 76.3% and GPT-4.1 from 54.8% to 63.8%, while answering fewer questions. Clinical relevance Integrating DSE as a black-box uncertainty filter enables selective answering and explicit uncertainty display for radiology vision-language tools, supporting safer diagnostic use, mitigating hallucinations, and improving clinicians' trust in AI-assisted image interpretation.

Authors 9

1. Patrick Wienholt corresponding Aachen Department of Diagnostic and Interventional Radiology Lab for Artificial Intelligence in Medicine

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. pwienholt@ukaachen.de

   Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. pwienholt@ukaachen.de

2. Sophie Caselitz Aachen Department of Diagnostic and Interventional Radiology Lab for Artificial Intelligence in Medicine

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

   Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

3. Robert Malte Siepmann Aachen Department of Diagnostic and Interventional Radiology Lab for Artificial Intelligence in Medicine

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

   Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

4. Philipp Bruners Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

5. Keno K. Bressem

   TUM Klinikum · Deutsches Herzzentrum München · Technical University of Munich

   Affiliation as printed

   Department of Cardiovascular Radiology and Nuclear Medicine, Technical University of Munich, School of Medicine and Health, German Heart Center, TUM University Hospital, Munich, Germany

   Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, Munich, Germany

6. Christiane Katharina Kuhl Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

7. Jakob Nikolas Kather

   University of Leeds · University Hospital Heidelberg · National Center for Tumor Diseases · University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden

   Affiliation as printed

   Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

   Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

   Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

   Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom

8. Sven Nebelung Aachen Department of Diagnostic and Interventional Radiology Lab for Artificial Intelligence in Medicine

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

   Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

9. Daniel Truhn Aachen Department of Diagnostic and Interventional Radiology Lab for Artificial Intelligence in Medicine

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

   Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany