[Paper Review] ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs

Zihao Zhao, Sheng Wang+7 more|arXiv (Cornell University)|May 25, 2023

Topic Modeling54 references4 citations

TL;DR

ChatCAD+ is a universal and reliable interactive CAD system that integrates domain-aware image interpretation, template-based report generation, and knowledge-grounded LLM reasoning to produce clinically consistent, up-to-date, and stylistically accurate diagnostic reports across diverse medical imaging domains. It improves reliability by retrieving evidence from authoritative medical sources and enhances report quality by leveraging exemplar reports.

ABSTRACT

The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor. Despite the promising potential of this integration, current works face at least two limitations: (1) From the perspective of a radiologist, existing studies typically have a restricted scope of applicable imaging domains, failing to meet the diagnostic needs of different patients. Also, the insufficient diagnostic capability of LLMs further undermine the quality and reliability of the generated medical reports. (2) Current LLMs lack the requisite depth in medical expertise, rendering them less effective as virtual family doctors due to the potential unreliability of the advice provided during patient consultations. To address these limitations, we introduce ChatCAD+, to be universal and reliable. Specifically, it is featured by two main modules: (1) Reliable Report Generation and (2) Reliable Interaction. The Reliable Report Generation module is capable of interpreting medical images from diverse domains and generate high-quality medical reports via our proposed hierarchical in-context learning. Concurrently, the interaction module leverages up-to-date information from reputable medical websites to provide reliable medical advice. Together, these designed modules synergize to closely align with the expertise of human medical professionals, offering enhanced consistency and reliability for interpretation and advice. The source code is available at https://github.com/zhaozh10/ChatCAD.

Motivation & Objective

• To address the limited generalizability of existing LLM-based CAD systems, which are often restricted to single image domains or modalities.
• To overcome the mismatch in writing style between LLM-generated reports and radiologist-authored reports by incorporating clinical report templates.
• To improve the reliability of medical advice by grounding LLM responses in up-to-date, reputable medical knowledge sources instead of relying solely on internal LLM knowledge.
• To enable a universal, interactive, and clinically useful CAD system capable of handling diverse medical imaging inputs and generating expert-like diagnostic reports.

Proposed method

• A domain identification module automatically classifies input medical images and selects the appropriate CAD model for interpretation, ensuring modality-agnostic processing.
• The system converts CAD network outputs into structured medical text descriptions using a unified interpretation pipeline.
• A template retrieval system extracts top-k clinically relevant report exemplars from an in-house database to guide the LLM in mimicking radiologist writing style.
• A knowledge retrieval module fetches current, evidence-based medical information from trusted sources (e.g., UpToDate) to ground LLM-generated advice and treatments.
• The LLM integrates retrieved templates and knowledge to generate final diagnostic reports that are both stylistically consistent and clinically reliable.
• The architecture is modular and scalable, allowing integration of new CAD models and knowledge sources across different imaging domains.

Experimental results

Research questions

• RQ1 Can a single LLM-based system reliably interpret medical images across diverse anatomical and imaging domains without requiring domain-specific fine-tuning?
• RQ2 How effectively can template-based retrieval improve the stylistic and clinical consistency of LLM-generated radiology reports?
• RQ3 To what extent does grounding LLM responses in external medical knowledge databases enhance the reliability and accuracy of clinical advice?
• RQ4 Can the integration of domain identification, template retrieval, and knowledge grounding significantly reduce hallucination and improve clinical trustworthiness in automated diagnostic systems?

Key findings

• ChatCAD+ demonstrates universal applicability by correctly identifying and processing medical images from multiple domains using a single, unified interface.
• The template retrieval system significantly improves the stylistic alignment of LLM-generated reports with human radiologist reports, reducing unnatural or inconsistent phrasing.
• Knowledge-grounded responses show higher clinical reliability, with evidence-based recommendations and reduced hallucination compared to baseline LLMs.
• The system achieves improved diagnostic report quality, as validated by clinical experts, particularly in complex cases involving pleural effusion and pulmonary atelectasis.
• In comparative evaluations, ChatCAD+ outperforms baseline LLMs in generating accurate, contextually appropriate, and up-to-date medical advice, especially for treatment planning and symptom interpretation.
• The integration of real-time medical knowledge retrieval ensures that recommendations remain current and aligned with clinical guidelines, even as medical evidence evolves.