mlsanityData to 14 Oct 2026

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

arXiv 2605.22833 ↗First posted 24 Apr 2026cs.IR cs.AI cs.LGcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

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.

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.

Builds on

12 of 33 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models17 Nov
  2. 2024MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models16 Oct
  3. 2024Benchmarking Retrieval-Augmented Generation for Medicine20 Feb
  4. 2023Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping11 Dec

Other references · 8

  1. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  2. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 93
  3. 2022CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoisingcited by 16
  4. 2024SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automationcited by 7
  5. 2025Competitive Distillation: A Simple Learning Strategy for Improving Visual Classificationcited by 4
  6. 2024KAE: A Property-based Method for Knowledge Graph Alignment and Extensioncited by 3
  7. 2025Minuscule Cell Detection in AS-OCT Images with Progressive Field-of-View Focusingcited by 3
  8. 2025Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Responsecited by 1

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  1. 2025LVLM-Aware Multimodal Retrieval for RAG-Based Medical Diagnosis with General-Purpose Models
  2. 2025ICA-RAG: Information Completeness Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis
  3. 2025Less Context, Same Performance: A RAG Framework for Resource-Efficient LLM-Based Clinical NLP
  4. 2025RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis
  5. 2025MIRA: A Novel Framework for Fusing Modalities in Medical RAG
  6. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
  7. 2024oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness
  8. 2026PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation