Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making. - Mingxuan Liu 005; Yilin Ning; Han Yuan; Marcus Eng Hock Ong; Nan Liu 003 (CoRR, 2022)

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. - Mingxuan Liu 005; Siqi Li 004; Han Yuan; Marcus Eng Hock Ong; Yilin Ning; Feng Xie 004; Seyed Ehsan Saffari; Victor Volovici; Bibhas Chakraborty; Nan Liu 003 (CoRR, 2022)

Shapley variable importance cloud for machine learning models. - Yilin Ning; Mingxuan Liu 005; Nan Liu 003 (CoRR, 2022)

Interpretable Machine Learning-Based Risk Scoring with Individual and Ensemble Model Selection for Clinical Decision Making. - Han Yuan; Jin Wee Lee; Mingxuan Liu 005; Siqi Li 004; Chenglin Niu; Jun Wen; Feng Xie 004 (Tiny Papers @ ICLR, 2023)

Towards clinical AI fairness: A translational perspective. - Mingxuan Liu 005; Yilin Ning; Salinelat Teixayavong; Mayli Mertens; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; Jasmine Chiat Ling Ong; Zhen Ling Teo; Ting Fang Tan; Ravi Chandran Narrendar; Fei Wang 001; et al. (CoRR, 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 005; Jiuwen Cao; Michael Dunn; et al. (CoRR, 2023)

A translational perspective towards clinical AI fairness. - Mingxuan Liu 005; Yilin Ning; Salinelat Teixayavong; Mayli Mertens; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; Jasmine Chiat Ling Ong; Zhen Ling Teo; Ting Fang Tan; Ravi Chandran Narrendar; Fei Wang 001; et al. (npj Digit. Medicine, 2023)

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. - Mingxuan Liu 005; Siqi Li 004; Han Yuan; Marcus Eng Hock Ong; Yilin Ning; Feng Xie 004; Seyed Ehsan Saffari; Yuqing Shang; Victor Volovici; Bibhas Chakraborty; Nan Liu 003 (Artif. Intell. Medicine, 2023)

FedScore: A privacy-preserving framework for federated scoring system development. - Siqi Li 004; Yilin Ning; Marcus Eng Hock Ong; Bibhas Chakraborty; Chuan Hong; Feng Xie 004; Han Yuan; Mingxuan Liu 005; Daniel M. Buckland; Yong Chen 016; Nan Liu 003 (J. Biomed. Informatics, 2023)

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare. - Mingxuan Liu 005; Yilin Ning; Yuhe Ke; Yuqing Shang; Bibhas Chakraborty; Marcus Eng Hock Ong; Roger Vaughan; Nan Liu 003 (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 005; Feng Xie 004; Marcus Eng Hock Ong; Nan Liu 003 (CoRR, 2024)

Towards Clinical AI Fairness: Filling Gaps in the Puzzle. - Mingxuan Liu 005; Yilin Ning; Salinelat Teixayavong; Xiaoxuan Liu; Mayli Mertens; Yuqing Shang; Xin Li 079; Di Miao; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; Jasmine Chiat Ling Ong; et al. (CoRR, 2024)

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine. - Rui Yang 016; Yilin Ning; Emilia Keppo; Mingxuan Liu 005; Chuan Hong; Danielle S. Bitterman; Jasmine Chiat Ling Ong; Daniel Shu Wei Ting; Nan Liu 003 (CoRR, 2024)

Benchmarking mortality risk prediction from electrocardiograms. - Platon Lukyanenko; Joshua Mayourian; Mingxuan Liu 005; John K. Triedman; Sunil J. Ghelani; William G. La Cava (CoRR, 2024)

Disparities in clinical studies of AI enabled applications from a global perspective. - Rui Yang 016; Sabarinath Vinod Nair; Yuhe Ke; Danny D'Agostino; Mingxuan Liu 005; Yilin Ning; Nan Liu 003 (npj Digit. Medicine, 2024)

FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare. - Mingxuan Liu 005; Yilin Ning; Yuhe Ke; Yuqing Shang; Bibhas Chakraborty; Marcus Eng Hock Ong; Roger Vaughan; Nan Liu 003 (Patterns, 2024)

Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study. - Platon Lukyanenko; Joshua Mayourian; Mingxuan Liu 005; John K. Triedman; Sunil J. Ghelani; William G. La Cava (BioData Min., 2025)

A scoping review and evidence gap analysis of clinical AI fairness. - Mingxuan Liu 005; Yilin Ning; Salinelat Teixayavong; Xiaoxuan Liu; Mayli Mertens; Yuqing Shang; Xin Li 079; Di Miao; Jingchi Liao; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; et al. (npj Digit. Medicine, 2025)

⁠Advancing ethical AI in healthcare through interpretability. - Yilin Ning; Mingxuan Liu 005; Nan Liu 003 (Patterns, 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 005; Yian Ma; Zhao Liang; Kuldev Singh; Robert T. Chang; Silke Vogel; John CW Lim; Iris Siu Kwan Tan; Oscar Freyer; Stephen Gilbert; et al. (CoRR, 2025)

seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness. - Yilin Ning; Yian Ma; Mingxuan Liu 005; Xin Li 079; Nan Liu 003 (CoRR, 2025)

Gender Bias in Large Language Models for Healthcare: Assignment Consistency and Clinical Implications. - Mingxuan Liu 005; Yuhe Ke; Wentao Zhu; Mayli Mertens; Yilin Ning; Jingchi Liao; Chuan Hong; Daniel Shu Wei Ting; Yifan Peng 002; Danielle S. Bitterman; Marcus Eng Hock Ong; Nan Liu 003 (CoRR, 2025)

Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach. - Mingxuan Liu 005; Yilin Ning; Haoyuan Wang; Chuan Hong; Matthew Engelhard; Danielle S. Bitterman; William G. La Cava; Nan Liu 003 (CoRR, 2025)

Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority. - Jasmine Chiat Ling Ong; Yilin Ning; Mingxuan Liu 005; Yian Ma; Liang Zhao; Kuldev Singh; Robert T. Chang; Silke Vogel; John CW Lim; Iris Siu Kwan Tan; Oscar Freyer; Stephen Gilbert; et al. (npj Digit. Medicine, 2026)