Super-moon

Jong Hak Moon

Hello, I’m Jonghak Moon (pronounced Johng-hahk), a medical AI researcher and research leader currently serving as Head of Research at YejiX AI.

I earned my PhD in Artificial Intelligence from KAIST under the supervision of Professor Edward Choi. Before joining YejiX, I worked as a researcher at Samsung Medical Center for more than three years.

My research focuses on multimodal (mostly vision-language) clinical AI and clinically meaningful evaluation, with the goal of building AI systems that are both scientifically rigorous and genuinely useful in real-world healthcare settings.

I am particularly excited about advancing medical AI in hospitals and contributing to the future of digital hospitals through impactful research. I actively collaborate with clinicians and researchers from both Korea and abroad, and I am always open to new collaborations and ideas.

Email: jhak.moon@gmail.com

Education

Publications

Conferences

Extracurricular Activities

Education

Ph.D. in Artificial Intelligence, KAIST, Kim Jaechul Graduate School of AI, Daejeon, South Korea Feb. 2026
- Advisor: Edward Choi
- Research focus: medical AI, multimodal learning, and clinically grounded evaluation

M.S. in Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Seoul, South Korea Feb. 2020

Work Experience

Head of Research, YejiX (Early-stage Medical AI Startup), Seoul, South Korea / Nashville, USA Aug.2025 - Present
- Lead research in multimodal medical AI spanning chest X-rays, EHR, and clinical notes, with an emphasis on structured clinical understanding and longitudinal patient modeling.
- Translate research into product and validation strategies informed by regulatory, reimbursement, and deployment considerations across the U.S. and South Korea.
- Oversee research planning, team mentorship, and hospital collaborations for real-world clinical studies.

Medical AI Research Intern, AI CAD Team, Kakao Brain, Pangyo, South Korea Sep. 2022 - Mar. 2023
- Vision-Language representation learning model
- Large scaled CXR Image-Report multi-modal understanding and generation model

Lead Research Scientist. Medical AI Lab, Korea Artificial Intelligence Research Institute. Jan. 2020 ~ Dec.2020
- Academic lectures for artificial intelligence modeling in medical domain
- Medical AI project design and lecture; ‘Self-Attention Mechanism’, ‘Weakly-Supervised Learning’

AI Researcher, Medical AI Center, Samsung Medical Center, Seoul, South Korea Jun. 2017 - Aug. 2020

Stenosis classify and localize using coronary angiography
Myopic tilted-disc detection using fundus image
Bone metastasis cancer segmentation using Abdominal CT

Lecture Experience

Medical AI Research with Vibe Coding, Samung Medical Center, Korea Jan. 2025
Machine Learning Engineer, SK korea, Korea Jul-Aug. 2024
Mortality prediction Hands-on Session, KoSAIM2022 summer school, Korea Aug. 2022

Publications

NLP & Benchmark & Dataset & Evaluation Metric & Structuring:

7. J.H. Moon, et al., “Modeling Clinical Uncertainty in Radiology Reports: from Explicit Uncertainty Markers to Implicit Reasoning Pathways”, LREC 2026

6. J.H. Moon, et al., “Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation”, CHIL 2026

5. S Kweon, J Kim, J Kim, S Im, E Cho, S Bae, J Oh, G Lee, JH Moon, SC You, S Baek, CH Han, YB Jung, E Choi, “Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes”, ACL 2024 (Findings)

Vision & Theory & Analysis:

4. J.H. Moon, et al., “Correlation between Alignment-and-Uniformity and Performance of Dense Contrastive Representations”, BMVC 2022

Vision(dtype: Image)-Language Multimodality Foundation Model:

3. J.H. Moon, et al., “Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training”, IEEE JBHI 2022

Vision (dtype: video) & self-attention:

2. J.H. Moon, et al., “Automatic stenosis recognition from coronary angiography using convolutional neural networks”, CMPB 2021

1. B.H. Cho‡, D.Y. Lee‡, K.A. Park*, S.Y. Oh*, J.H. Moon, G.I. Lee, H. Noh, J.K. Chung, M.C. Kang and M. J. Chung, “Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography”, BMC Ophthalmology 2020

Conferences

- J.H. Moon “RNN and Hands-on RNN: ICU Mortality Prediction”, KoSAIM 2022 Summer School, Seoul, Korea (2022).

- J.H. Moon et al., “The Automated Classification and Localisation of Stenosis using Coronary Angiograms”, The Korean Society of Cardiology 2019, Seoul, Korea (2019) – English oral.

- J.H. Moon et al., “Automated classification of coronary angiograms using deep Convolutional Neural Networks”, IEEE Engineering in Medicine & Biology Society (EMBC 2019), Berlin, Germany (2019) – One paper.

- J.H. Moon et al., “Comparative Analysis of Bone Metastasis Segmentation Using Fully Convolutional Network Algorithms on Abdominal Computed Tomography”, The Korean Society of Medical Informatics, 2018, Jeonju, Korea (2018) – One paper.

Extracurricular Activities

· [Peer Review]

Journal: IEEE JBHI, CMPB Jan. 2022 ~ Current

Conference: MICCAI, ML4H, EMNLP, CHIL, ACL, CVPR, ICML, Neurips, COLM, LREC, MLHC, ICLR

· [Award] First Prize , Korean Society of Artificial Intelligence in Medicine, Korea Oct. 13. 2022

- Scientific Session (contributed talks) First Prize

· [Talks]

Invited tech talks in Google Health, San Francisco & London Oct. 2022

- Topic: Vision-Language Foundation model in Healthcare