Mingxuan Liu

According to our database, Mingxuan Liu authored at least 24 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority.

Jasmine Chiat Ling Ong, Yilin Ning, Mingxuan Liu, Yian Ma, Liang Zhao, Kuldev Singh, Robert T. Chang, Silke Vogel, John CW Lim, Iris Siu Kwan Tan, Oscar Freyer, Stephen Gilbert, Danielle S. Bitterman, Xiaoxuan Liu, Alastair K. Denniston, Nan Liu

npj Digit. Medicine, 2026

2025

Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach.

Mingxuan Liu, Yilin Ning, Haoyuan Wang, Chuan Hong, Matthew Engelhard, Danielle S. Bitterman, William G. La Cava, Nan Liu

CoRR, October, 2025

Gender Bias in Large Language Models for Healthcare: Assignment Consistency and Clinical Implications.

Mingxuan Liu, Yuhe Ke, Wentao Zhu, Mayli Mertens, Yilin Ning, Jingchi Liao, Chuan Hong, Daniel Shu Wei Ting, Yifan Peng, Danielle S. Bitterman, Marcus Eng Hock Ong, Nan Liu

CoRR, October, 2025

seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness.

Yilin Ning, Yian Ma, Mingxuan Liu, Xin Li, Nan Liu

CoRR, April, 2025

Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action.

Jasmine Chiat Ling Ong, Yilin Ning, Mingxuan Liu, Yian Ma, Zhao Liang, Kuldev Singh, Robert T. Chang, Silke Vogel, John CW Lim, Iris Siu Kwan Tan, Oscar Freyer, Stephen Gilbert, Danielle S. Bitterman, Xiaoxuan Liu, Alastair K. Denniston, Nan Liu

CoRR, February, 2025

⁠Advancing ethical AI in healthcare through interpretability.

Yilin Ning, Mingxuan Liu, Nan Liu

Patterns, 2025

A scoping review and evidence gap analysis of clinical AI fairness.

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Xiaoxuan Liu, Mayli Mertens, Yuqing Shang, Xin Li, Di Miao, Jingchi Liao, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Narrendar Ravichandran, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

npj Digit. Medicine, 2025

Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study.

Platon Lukyanenko, Joshua Mayourian, Mingxuan Liu, John K. Triedman, Sunil J. Ghelani, William G. La Cava

BioData Min., 2025

2024

FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare.

Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu

Patterns, 2024

Disparities in clinical studies of AI enabled applications from a global perspective.

Rui Yang, Sabarinath Vinod Nair, Yuhe Ke, Danny D'Agostino, Mingxuan Liu, Yilin Ning, Nan Liu

npj Digit. Medicine, 2024

Benchmarking mortality risk prediction from electrocardiograms.

Platon Lukyanenko, Joshua Mayourian, Mingxuan Liu, John K. Triedman, Sunil J. Ghelani, William G. La Cava

CoRR, 2024

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine.

Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong, Danielle S. Bitterman, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting, Nan Liu

CoRR, 2024

Towards Clinical AI Fairness: Filling Gaps in the Puzzle.

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Xiaoxuan Liu, Mayli Mertens, Yuqing Shang, Xin Li, Di Miao, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Narrendar RaviChandran, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

CoRR, 2024

Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission.

Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning, Mingxuan Liu, Feng Xie, Marcus Eng Hock Ong, Nan Liu

CoRR, 2024

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare.

Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu

CoRR, 2024

2023

FedScore: A privacy-preserving framework for federated scoring system development.

Siqi Li, Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Chuan Hong, Feng Xie, Han Yuan, Mingxuan Liu, Daniel M. Buckland, Yong Chen, Nan Liu

J. Biomed. Informatics, October, 2023

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Yuqing Shang, Victor Volovici, Bibhas Chakraborty, Nan Liu

Artif. Intell. Medicine, August, 2023

A translational perspective towards clinical AI fairness.

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Ravi Chandran Narrendar, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

npj Digit. Medicine, 2023

Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist.

Yilin Ning, Salinelat Teixayavong, Yuqing Shang, Julian Savulescu, Vaishaanth Nagaraj, Di Miao, Mayli Mertens, Daniel Shu Wei Ting, Jasmine Chiat Ling Ong, Mingxuan Liu, Jiuwen Cao, Michael Dunn, Roger Vaughan, Marcus Eng Hock Ong, Joseph Jao Yiu Sung, Eric J. Topol, Nan Liu

CoRR, 2023

Towards clinical AI fairness: A translational perspective.

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Ravi Chandran Narrendar, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

CoRR, 2023

Interpretable Machine Learning-Based Risk Scoring with Individual and Ensemble Model Selection for Clinical Decision Making.

Han Yuan, Jin Wee Lee, Mingxuan Liu, Siqi Li, Chenglin Niu, Jun Wen, Feng Xie

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

2022

Shapley variable importance cloud for machine learning models.

Yilin Ning, Mingxuan Liu, Nan Liu

CoRR, 2022

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu

CoRR, 2022

Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making.

Mingxuan Liu, Yilin Ning, Han Yuan, Marcus Eng Hock Ong, Nan Liu

CoRR, 2022