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Hyungyung Lee

KAIST

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Hangyul Yoon

KAIST

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Edward Choi

KAIST

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References (23)

[1]
ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue
2026Hyunseung Chung, Jungwoo Oh et al.
[2]
OpenAI GPT-5 System Card
2025Aaditya K. Singh, A. Fry et al.
[3]
Evaluating the diagnostic accuracy of vision language models for neuroradiological image interpretation
2025Aymen Meddeb, Ida Rangus et al.
[4]
Localizing Before Answering: A Benchmark for Grounded Medical Visual Question Answering
2025Dung Nguyen, Minh Khoi Ho et al.
[5]
MedGemma Technical Report
2025Andrew Sellergren, Sahar Kazemzadeh et al.
[6]
RadFabric: Agentic AI System with Reasoning Capability for Radiology
2025Wenting Chen, Yi Dong et al.
[7]
CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays
2025HyunGyung Lee, Geon Choi et al.
[8]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[9]
ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models
2025Jeonghoon Shim, Gyuhyeon Seo et al.
[10]
MedRAX: Medical Reasoning Agent for Chest X-ray
2025Adibvafa Fallahpour, Jun Ma et al.
[11]
Pixtral 12B
2024Pravesh Agrawal, Szymon Antoniak et al.
[12]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[13]
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent
2024Binxu Li, Tian Yan et al.
[14]
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
2023Lianmin Zheng, Wei-Lin Chiang et al.
[15]
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
2023Chunyuan Li, Cliff Wong et al.
[16]
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
2023Sheng Zhang, Yanbo Xu et al.
[17]
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
2023Sheng Wang, Zihao Zhao et al.
[18]
TorchXRayVision: A library of chest X-ray datasets and models
2021Joseph Paul Cohen, J. Viviano et al.
[19]
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
2020J. Amann, A. Blasimme et al.
[20]
NeuroKit2: A Python toolbox for neurophysiological signal processing
2020D. Makowski, Tam Pham et al.

Showing 20 of 23 references

Founder's Pitch

"An AI-powered diagnostic assistant for more reliable evidence-grounded reasoning in chest X-ray interpretation."

Medical AIScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

Sources used for this analysis

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Why It Matters

Chest X-rays are critical for diagnosing thoracic conditions, and having an evidence-grounded AI assistant can significantly improve diagnostic accuracy and reliability, reducing the chance of misdiagnosis.

Product Angle

The technology can be developed into a diagnostic tool for hospitals and telemedicine platforms, providing verified interpretations of chest X-rays to assist radiologists and healthcare providers.

Disruption

The solution can disrupt traditional radiology practices by offering a more evidence-backed diagnostic process, reducing reliance on human interpretation alone and potentially lowering diagnostic errors.

Product Opportunity

The global diagnostic imaging market is substantial, driven by the increasing incidence of chronic diseases. Hospitals and telemedicine companies would pay for improved diagnostic accuracy and efficiency.

Use Case Idea

Develop a telemedicine tool that uses CXReasonAgent to allow remote verification and diagnosis of thoracic abnormalities through chest X-rays.

Science

The paper introduces CXReasonAgent, which leverages a large language model combined with diagnostic tools to interpret chest X-rays, grounding its diagnostic processes in quantitative evidence and spatial observations to provide verifiable reasoning instead of relying solely on textual output.

Method & Eval

CXReasonAgent was tested using a new benchmark, CXReasonDial, which involves 1,946 dialogues simulating multi-task diagnostic scenarios. CXReasonAgent achieved higher success rates in grounding responses in image-derived evidence compared to existing LVLMs.

Caveats

The AI's reliance on predefined tasks and specific diagnostic tools might limit its adaptability to unforeseen diagnostic challenges. Accuracy depends heavily on the quality of initial chest X-ray images and annotations.

Author Intelligence

Hyungyung Lee

KAIST

Hangyul Yoon

KAIST

Edward Choi

KAIST