RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis
The article introduces RAG4Outcome, a new framework designed for prognostic prediction in chronic osteomyelitis. This framework integrates various multimodal clinical data to enhance the accuracy and reliability of prognosis. Preliminary results indicate its potential effectiveness in AI-assisted infection management and postoperative decision support.
- ▪Chronic osteomyelitis poses significant prognostic challenges due to high recurrence risk.
- ▪RAG4Outcome combines multimodal clinical data into a unified prediction pipeline.
- ▪The framework aims to improve the interpretability and reliability of prognostic predictions.
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Computer Science > Information Retrieval arXiv:2605.22833 (cs) [Submitted on 24 Apr 2026] Title:RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis Authors:Daqian Shi, Pei Han, Jishizhan Chen, Yang Wang, Xiaolei Diao, Xianyou Zheng, Pengfei Cheng View a PDF of the paper titled RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis, by Daqian Shi and 6 other authors View PDF HTML (experimental) Abstract: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.
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