Zero-shot evaluation of open vision-language models as System 1 decision models on radiology tasks
Bradley J Erickson, Sara Salehi
Abstract
Purpose: We compared two multimodal System 1 decision models, Clef 27B and d1 3B, with a traditional LLM (Qwen 3.8-27B) on seven radiology tasks covering text, radiographs, CT and MRI. Methods: Three models were compared: Clef, Qwen3.8-27B and d1. Three tasks were interpretation of text and 4 were detection of finding in images. The tasks were: 1. finding presence of finding in CT-RATE chest CT reports; 2. Presence of finding in PadChest chest radiograph reports; 3. the same 18 findings on CT-RATE chest CT but 3D volumes; 4. MR safety status of devices from AccessGUDID device records; 5. line and tube position on RANZCR CLiP chest radiographs; 6. motion-artifact severity on MR-ART T1-weighted brain MRI and 7. sequence identification on BraTS 2021 brain MRI. The primary endpoint was AUROC. There was no fine-tuning of the models on the tasks. Results: All models completed all tests without a failed request. All retrieved findings from reports almost perfectly (AUROC >0.95). Clef ranked higher than its Qwen backbone on CT-RATE volumes and RANZCR lower on MR-ART. The two did not differ on BraTS, GUDID or PadChest, and differed by 0.002 on CT-RATE reports. On BraTS, Clef correctly identified 90 of 100 FLAIR volumes while Qwen never chose it. d1 ranked lower than Clef on six of seven tasks. No model performed well on motion severity. Clef was faster per request than Qwen on tasks with many questions per input. d1 used 3 to 9 times less energy per request than Clef. Conclusions: System 1 models can perform important tasks in radiology with high accuracy, at lower compute cost than LLMs used in the usual way. When an LLM is constrained to a single answer token, its cost and performance is similar to System 1 models unless multiple questions are asked. A 3B System 1 model was close to the 27B models on report text but not on images. No model performed well on motion severity.