Towards a Radiologist Imitation Framework for 3D CT Diagnosis with Multimodal LLMs

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Health & Wellbeing — Clinical Care & Medical Practice, Medical Devices & Health Technology, Health & Medical Research · Depth: Expert, medium

Summary

A novel radiologist-simulating framework for selective and efficient 3D CT interpretation is proposed, addressing the inefficiency of uniformly analyzing CT slices. This framework, evaluated on a 3D CT dataset covering eight thoracic lesion types, was compared against multimodal large language models like GPT-4o and visual backbones including ViT and ResNet-50. The Screen-CLIP model within this framework achieved an AUC of 0.87 and an F1-score of 0.82 for diagnosis, surpassing ViT Base's AUC of 0.84. For automated report generation, the method outperformed M3D across all metrics, reaching a BLEU-Avg of 29.03 and securing the highest average Doctors' Score of 6.16/10 in human evaluation.

Key takeaway

For AI Scientists and Research Scientists developing medical imaging diagnostics, this framework demonstrates that moving beyond uniform CT slice analysis to a selective, radiologist-imitating approach significantly boosts both diagnostic accuracy and computational efficiency. You should consider integrating targeted processing and multimodal LLMs into your models to improve performance on sparse clinical data and enhance automated report generation, as evidenced by Screen-CLIP's superior metrics.

Key insights

Selective processing of 3D CT slices significantly improves diagnostic efficiency and accuracy by focusing on clinically relevant information.

Principles

• Clinically relevant information is sparsely distributed in CT volumes.
• Uniform CT slice analysis wastes computation and misses cues.
• Targeted processing enhances fine-grained cue detection.

Method

A radiologist-simulating framework for 3D CT interpretation that selectively processes slices, integrating multimodal LLMs and visual backbones like Screen-CLIP for diagnosis and report generation.

In practice

• Implement selective CT slice analysis in diagnostic AI.
• Utilize Screen-CLIP for improved 3D CT lesion detection.
• Integrate multimodal LLMs for automated medical report generation.

Topics

• 3D CT Diagnosis
• Multimodal LLMs
• Radiologist Workflow
• Medical Imaging AI
• Screen-CLIP
• Thoracic Lesions

Best for: AI Scientist, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.