# OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography

arXiv: https://arxiv.org/abs/2409.09052 · HTML: https://mlsanity.com/paper/2409.09052/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 23 of its 27 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 23 of 23)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://mlsanity.com/paper/2405.13576/) (2024)
- [Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://mlsanity.com/paper/2306.07831/) (2023)
- [MMedAgent: Learning to Use Medical Tools with Multi-modal Agent](https://mlsanity.com/paper/2407.02483/) (2024)
- [MedViT: A Robust Vision Transformer for Generalized Medical Image Classification](https://mlsanity.com/paper/2302.09462/) (2023)
- [MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory](https://mlsanity.com/paper/2404.11672/) (2024)
- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation](https://mlsanity.com/paper/1912.11975/) (2019)
- [A Continued Pretrained LLM Approach for Automatic Medical Note Generation](https://mlsanity.com/paper/2403.09057/) (2024)
- [MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis](https://mlsanity.com/paper/2407.04106/) (2024)
- [RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models](https://mlsanity.com/paper/2404.12065/) (2024)
- [MedGPT: Medical Concept Prediction from Clinical Narratives](https://mlsanity.com/paper/2107.03134/) (2021)
- [Investigating Mysteries of CoT-Augmented Distillation](https://mlsanity.com/paper/2406.14511/) (2024)
- [Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models](https://mlsanity.com/paper/2404.16385/) (2024)
- [SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models](https://mlsanity.com/paper/2404.17912/) (2024)
- [D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions](https://mlsanity.com/paper/2407.02604/) (2024)
- [Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions](https://mlsanity.com/paper/2404.13307/) (2024)
- [One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.19670/) (2024)
- [Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification](https://mlsanity.com/paper/2407.11573/) (2024)
- [LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task](https://mlsanity.com/paper/2407.12064/) (2024)

## Built on by (most cited first, 3 of 3)

- [Multimodal Large Language Models for Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2504.21051/) (2025)
- [Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras](https://mlsanity.com/paper/2510.09230/) (2025)
- [Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generation](https://mlsanity.com/paper/2602.15650/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) have achieved significant success in the general field of image processing. Their emerging task generalization and freeform conversational capabilities can greatly facilitate medical diagnostic assistance, helping patients better understand their conditions and enhancing doctor-patient trust. Computed Tomography (CT) is a non-invasive imaging technique used to capture the internal mechanisms of a patient's condition and is widely utilized. However, in past research, the complex textural features of this imaging data have made accurate interpretation by algorithms challenging, impeding the performance of general LLMs in diagnostic assistance. To address this, we developed OrthoDoc, a MLLM designed for CT diagnostics. OrthoDoc is trained on 120,000 CT images and diagnostic reports and includes a Retrieval-Augmented Generation (RAG) module capable of effectively mitigating model hallucinations. This module is informed by extensive medical literature, textbooks, and explanatory data. Thus, OrthoDoc not only processes complex CT images but also stores, understands, and reasons over medical knowledge and language. In extensive experiments, OrthoDoc outperforms commercial models led by GPT-4, demonstrating superior diagnostic capabilities and accuracy. Specifically, OrthoDoc significantly surpasses existing models in the diagnosis of common orthopedic conditions such as fractures, arthritis, and tumors. Additionally, OrthoDoc exhibits robust generalization and stability when handling rare and complex cases.