mlsanityData to 7 Oct 2026

ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs

arXiv 2305.15964 ↗First posted 25 May 2023cs.CVcited by 35

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Abstract (arXiv)

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.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 42 of its 84 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

42 of 84 references.

References · 42

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30 more
  1. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  2. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
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  12. 2020CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTcited by 329
  13. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 13; 2025: 10; 2026: 5.

35 papers.

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  1. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
  2. 2023Large language models in healthcare and medical domain: A reviewcited by 126
  3. 2024MMedAgent: Learning to Use Medical Tools with Multi-modal Agentcited by 108
  4. 2023Embodied Task Planning with Large Language Modelscited by 84
  5. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  6. 2024A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicinecited by 45
  7. 2023CLIP in Medical Imaging: A Surveycited by 40
  8. 2024MEDCO: Medical Education Copilots Based on A Multi-Agent Frameworkcited by 33
  9. 2024GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosiscited by 28
  10. 2023MuseChat: A Conversational Music Recommendation System for Videoscited by 21
  11. 2024A Survey on Trustworthiness in Foundation Models for Medical Image Analysiscited by 16
  12. 2025Multimodal Large Language Models for Medicine: A Comprehensive Surveycited by 16
23 more
  1. 2025Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generationcited by 11
  2. 2024ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reportscited by 9
  3. 2023Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Reviewcited by 8
  4. 2025MedFILIP: Medical Fine-grained Language-Image Pre-trainingcited by 8
  5. 2024Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reportscited by 6
  6. 2025MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Modelscited by 5
  7. 2025From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?cited by 3
  8. 2024Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentationcited by 2
  9. 2024A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Modelcited by 2
  10. 2023Multi-domain improves out-of-distribution and data-limited scenarios for medical image analysiscited by 1
  11. 2024CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Modelscited by 1
  12. 2024A Medical Multimodal Large Language Model for Pediatric Pneumoniacited by 1
  13. 2024The Role of Language Models in Modern Healthcare: A Comprehensive Reviewcited by 1
  14. 2025Can Large Language Models Challenge CNNs in Medical Image Analysis?cited by 1
  15. 2025HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Taskscited by 1
  16. 2025Advances in Large Language Models for Medicinecited by 1
  17. 2025Analysing Environmental Efficiency in AI for X-Ray Diagnosiscited by 1
  18. 2025ProMRVL-CAD: Proactive Dialogue System with Multi-Round Vision-Language Interactions for Computer-Aided Diagnosiscited by 0
  19. 2026Design-Specification Tiling for ICL-based CAD Code Generationcited by 0
  20. 2026Echo-α: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretationcited by 0
  21. 2026MedARC: Training-Free Adaptive Redundancy Compression of Visual Tokens for 3D Medical Vision-Language Modelscited by 0
  22. 2026Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screeningcited by 0
  23. 2026CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verificationcited by 0