# RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis

arXiv: https://arxiv.org/abs/2605.22833 · HTML: https://mlsanity.com/paper/2605.22833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 12 of 12)

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- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024)
- [MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models](https://mlsanity.com/paper/2410.13085/) (2024)
- [CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising](https://mlsanity.com/paper/2207.07798/) (2022)
- [Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping](https://mlsanity.com/paper/2312.06457/) (2023)
- [SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation](https://mlsanity.com/paper/2409.13321/) (2024)
- [Competitive Distillation: A Simple Learning Strategy for Improving Visual Classification](https://mlsanity.com/paper/2506.23285/) (2025)
- [KAE: A Property-based Method for Knowledge Graph Alignment and Extension](https://mlsanity.com/paper/2407.05320/) (2024)
- [Minuscule Cell Detection in AS-OCT Images with Progressive Field-of-View Focusing](https://mlsanity.com/paper/2503.12249/) (2025)
- [Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2511.13526/) (2025)
- [Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response](https://mlsanity.com/paper/2510.05196/) (2025)

## Abstract (arXiv)

Chronic osteomyelitis presents substantial prognostic challenges due to its high recurrence risk and complex postoperative recovery trajectories. Traditional assessment often relies on manual scoring systems, which limit scalability, efficiency, and consistency in clinical practice. Furthermore, the heterogeneous nature of clinical data poses challenges for current multimodal learning approaches that require aligned inputs and large annotated datasets. In this work, we propose RAG4Outcome, a retrieval-augmented generation (RAG) framework for prognostic prediction in chronic osteomyelitis. Our method integrates multimodal clinical data, including PET-CT imaging reports, structured surgical and diagnostic records, and unstructured follow-up notes, into a unified prediction pipeline. By combining a domain-specific retrieval corpus with expert-guided prompting, the framework enables more interpretable, evidence-grounded, and clinically reliable prognosis. Preliminary results on real-world cases demonstrate promising effectiveness and clinical alignment, highlighting the potential of RAG4Outcome for AI-assisted infection management and postoperative decision support.