Source-linked AI summary
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Sheng Wang, Zihao Zhao, Xi Ouyang, Qian Wang, Dinggang Shen
TL;DR
LLMs offer medical knowledge and reasoning but struggle with medical-image interpretation, while CAD networks effectively analyze such images. ChatCAD bridges these capabilities by translating outputs from multiple CAD models into text for LLM summarization, with reported gains in diagnosis performance and potential for more understandable online healthcare.
Problem
LLMs have difficulty interpreting medical images, despite those images providing clinically important information, while CAD systems lack the LLMs’ interactive language capabilities.
Method
ChatCAD feeds images into classification, lesion segmentation, and report-generation networks, converts their outputs into natural-language text, and uses an LLM to summarize them.
Results
16.42% improvement in diagnosis performance score was reported for state-of-the-art report-generation methods, while the method achieved the highest F1 among compared methods.
Takeaways & Limitations
ChatCAD can provide interactive explanations and medical advice while combining decisions from multiple CAD models into clearer patient-oriented outputs.
Takeaways & Limitations
ChatCAD’s diagnosis accuracy improved but its BLEU score dropped, and the current system uses only three prompt types without patient chief-complaint data.
Abstract
from arXiv · showhide
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice. For example, a large dialog LLM like ChatGPT has successfully passed part of the US medical licensing exam. However, LLMs currently have difficulty processing images, making it challenging to interpret information from medical images, which are rich in information that supports clinical decisions. On the other hand, computer-aided diagnosis (CAD) networks for medical images have seen significant success in the medical field by using advanced deep-learning algorithms to support clinical decision-making. This paper presents a method for integrating LLMs into medical-image CAD networks. The proposed framework uses LLMs to enhance the output of multiple CAD networks, such as diagnosis networks, lesion segmentation networks, and report generation networks, by summarizing and reorganizing the information presented in natural language text format. The goal is to merge the strengths of LLMs' medical domain knowledge and logical reasoning with the vision understanding capability of existing medical-image CAD models to create a more user-friendly and understandable system for patients compared to conventional CAD systems. In the future, LLM's medical knowledge can be also used to improve the performance of vision-based medical-image CAD models.
1. Introduction
ChatCAD combines LLM reasoning and medical-image CAD models to address LLMs’ difficulty interpreting clinically important images. It translates outputs from multiple CAD networks into text for LLM summarization, improving reports while enabling interactive explanations and advice.
- LLMs struggle to interpret medical images, although those images provide critical information for clinical decision-making.
- Medical-image CAD networks analyze complex visual information for diagnosis, lesion segmentation, and report generation.
- ChatCAD feeds images into classification, lesion segmentation, and report generation networks, then combines their outputs with an LLM.
- ChatCAD converts vector and mask outputs into text prompts so the LLM can summarize findings from multiple CAD networks.
- 16.42% improvement in diagnosis performance score was reported for state-of-the-art report generation methods using the proposed scheme.
- Interactive explanations and medical advice can help patients understand symptoms, diagnoses, and treatment options more efficiently and reduce consultation costs.
- The scheme has potential to improve radiology-report quality and the feasibility of online healthcare services as CAD and LLM performance improves.
2. Related Works
Related work covers the growth of general and biomedical LLMs, medical image captioning and report generation, and vision-language pretraining methods that connect visual and textual information.
- Transformer advances and increased computing power enabled LLMs with billions of parameters and improved text-generation capabilities.
- Biomedical LLMs such as BioBERT, PubMedBERT, ClinicalBERT, and Med-PaLM adapt language models to biomedical or clinical data.
- Medical image captioning methods generate radiology reports using deep learning, abnormality graphs, or disease-topic knowledge graphs.
- Vision-language pretraining methods such as CLIP, Frozen, and Flamingo combine visual features with language-model representations.
3. Method
ChatCAD bridges medical-image CAD outputs and LLMs by translating tensors or masks into text prompts that the LLM can summarize. The method uses multiple CAD networks and evaluates prompt designs for producing clinically aligned reports.
- ChatCAD feeds images into classification, lesion segmentation, and report generation networks before combining their outputs for an LLM.
- 3.1. Bridge between Image and Text: The framework converts tensor or mask outputs into natural-language representations and concatenates them into an instruction prompt for the LLM.
- 3.1. Bridge between Image and Text: Prompt#1 presents five disease scores directly, but generated reports often repeat numerical values and differ from radiologist reports.
- 3.1. Bridge between Image and Text: Prompt#2 maps scores to four severity categories, while Prompt#3 reports diseases with scores above 0.5 or outputs “No Finding”.
- 3.2. Dataset and Implementation: The evaluation combines R2GenCMN with PCAM, uses MIMIC-CXR reports and CheXpert-trained classification, and tests 300 randomly selected cases.
- 3.2. Dataset and Implementation: The experiments use text-davinci-003 and the January 30, 2023 ChatGPT version, with GPT-3 output capped at 1024 tokens and temperature 0.5.
4. Report Generation
The report-generation evaluation compares ChatCAD with R2GenCMN and CvT2DistilGPT2 across five observations using precision, recall, and F1-score. ChatCAD shows stronger recall and F1 performance, while larger GPT-3 models improve diagnostic performance.
- 4.1. Quality Improvement of the Generated Report: The evaluation compares ChatCAD with R2GenCMN and CvT2DistilGPT2 on five observations using precision, recall, and F1-score.
- 4.1. Quality Improvement of the Generated Report: ChatCAD outperforms both comparison methods in recall and F1 across all five diseases, while R2GenCMN is stronger in precision on three diseases.
- 4.1. Quality Improvement of the Generated Report: 0.803 is ChatCAD’s recall for Consolidation, versus 0.239 for CvT2DistilGPT2 and 0.121 for R2GenCMN.
- 4.2. How LLMs affect Report Quality: The comparison excludes text-ada-001 because it cannot generate meaningful reports, and model-size figures are approximate estimates.
- 4.2. How LLMs affect Report Quality: text-davinci-003 shows better diagnostic performance than text-babbage-001 and text-curie-001, whose average F1-scores over five observations are low.
5. Interactive and Understandable CAD
ChatCAD uses LLMs to provide interactive explanations and medical advice alongside CAD outputs. This can help patients better understand symptoms, diagnoses, and treatment options.
- ChatCAD uses LLM medical knowledge to provide interactive explanations and advice.Examples include discussions of pleural effusion, edema, and swelling.
- The approach can clarify patients’ symptoms, diagnoses, and treatment options.The paper links this clearer understanding to more efficient and cost-effective consultations with medical experts.
6. Limitations and Discussion
The paper identifies limitations involving report naturalness, prompt design, evaluation scope, model components, and clinical rigor. It also notes that larger language models may improve diagnostic accuracy and report quality, while several directions remain unexplored.
- ChatCAD reports can sound unlike human reports and may mention the contributing networks explicitly.The authors suggest filtering generated reports or adding prompts that prohibit such references.
- ChatCAD improved diagnosis accuracy but reduced BLEU score relative to the baseline method.This reflects a trade-off between diagnostic performance and natural-language similarity.
- Only three intuitive prompt types were designed, leaving prompt design open to improvement.The authors also note that patient chief complaints were omitted because no suitable dataset was available.
- The role of vision classifiers with larger parameter counts has not yet been explored.The paper proposes further study of models such as ViT and SwinTransformer.
- Larger language models may further improve diagnostic accuracy and report quality.The experiments demonstrate a significant impact of language-model size on diagnostic accuracy.
- Prompt design was evaluated qualitatively rather than quantitatively.The authors defer deeper investigation until the ChatGPT API becomes available for use.
- The paper was not discussed with clinical professionals, limiting rigor in some areas.