Rad-Flamingo: A Multimodal Prompt driven Radiology Report Generation Framework with Patient-Centric Explanations.
| Authors | Md. Tousin Akhter et al. |
| Year | 2026 |
| Venue | EACL 2026 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
Rad-Flamingo is a multimodal framework that generates radiology reports from medical images using prompt-driven techniques, with a focus on providing patient-centric explanations alongside diagnostic findings. The paper addresses the gap between raw diagnostic outputs and clinically actionable, interpretable reports that patients and clinicians can understand together.
Key Engineering Insight
The core innovation is combining multimodal prompting (image + structured patient context) with explanation generation as a first-class output, rather than treating interpretability as an afterthought. This dual-output design (report + explanation) requires different architectural choices than standard image-to-text systems.
Why It Matters for Engineers
In production medical AI, explainability isn't optional—it's regulatory and clinical necessity. Most vision-to-report systems optimize for accuracy alone, creating a deployment gap when clinicians and patients need to understand why a recommendation was made. Building this into the model architecture from the start is cheaper and more reliable than bolting on post-hoc explanation layers.
Research Context
Prior work on radiology report generation focused on maximizing BLEU/CIDEr scores without patient context or explanation pathways. Rad-Flamingo advances the field by treating report generation as a structured output problem with multiple stakeholders (clinician needs vs. patient understanding), moving beyond single-audience, single-task framing that dominated earlier vision-language models in medical imaging.
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