ChatCAD+: Toward a Universal and Reliable Interactive CAD Using LLMs

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 p...

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Vydané v:IEEE transactions on medical imaging Ročník 43; číslo 11; s. 3755 - 3766
Hlavní autori: Zhao, Zihao, Wang, Sheng, Gu, Jinchen, Zhu, Yitao, Mei, Lanzhuju, Zhuang, Zixu, Cui, Zhiming, Wang, Qian, Shen, Dinggang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2024
Predmet:
Algorithms
Brain modeling
Chatbots
computer-assisted diagnosis
Diagnosis, Computer-Assisted - methods
Humans
Image Interpretation, Computer-Assisted - methods
Large language models
Medical diagnostic imaging
medical dialogue
multi-modality system
Reliability
Reproducibility of Results
Software
Solid modeling
Visualization
ISSN:0278-0062, 1558-254X, 1558-254X
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Shrnutí: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 GitHub.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2024.3398350