Directory

Directory

Yilin Ning

Assistant Professor, Centre for Biomedical Data Science

yilin.ning@duke-nus.edu.sg

66016836

About

Research Interests

Publications

Bio

Yilin Ning is a Senior Research Fellow at the Centre for Biomedical Data Science, Duke-NUS Medical School. Her research focuses on the responsible and clinically meaningful use of AI in healthcare, with particular emphasis on operationalising ethics, promoting equity and transparency, and developing interpretable and fairness-aware machine learning methods for clinical applications.

She currently leads an international collaboration to develop practical tools for implementing ethical principles in health AI, and contributes to the broader advancement of health AI governance and methodology. Her methodological work spans interpretable AI, fairness assessment and mitigation, and development and evaluation of clinical prediction models, with publications in Nature Medicine, The Lancet Digital Health, npj Digital Medicine, Patterns, and PLOS Digital Health.

Education

Doctor of Philosophy

National University of Singapore, Singapore

Bachelor of Science in Statistics Hons Class 2A

National University of Singapore, Singapore

Research Interests

Ethical AI for healthcare: Developing frameworks and tools to address ethical challenges and support responsible integration of AI into healthcare practice.

Trustworthy and clinically meaningful AI: Advancing methodological approaches to support ethical requirements in clinical AI, including equity, interpretability, and evaluation of clinical utility and real-world impact.

Translational AI research: Bridging the gap between AI innovation and clinical implementation by designing robust, real-world solutions to improve healthcare outcomes.

Publications

An ethics assessment tool for artificial intelligence implementation in healthcare: CARE-AI.

Nature medicine – Ning Yilin; Liu Xiaoxuan; Collins Gary S; Moons Karel G M; McCradden Melissa; Ting Daniel Shu Wei; Ong Jasmine Chiat Ling; Goldstein Benjamin Alan; Wagner Siegfried K; Keane Pearse A; Topol Eric J; Liu Nan (2024)

30 (11); 3038-3039

Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist.

The Lancet. Digital health – Ning Yilin; Teixayavong Salinelat; Shang Yuqing; Savulescu Julian; Nagaraj Vaishaanth; Miao Di; Mertens Mayli; Ting Daniel Shu Wei; Ong Jasmine Chiat Ling; Liu Mingxuan; Cao Jiuwen; Dunn Michael; Vaughan Roger; Ong Marcus Eng Hock; Sung Joseph Jao-Yiu; Topol Eric J; Liu Nan (2024)

6 (11); e848-e856

⁠Advancing ethical AI in healthcare through interpretability.

Patterns (New York, N.Y.) – Ning Yilin; Liu Mingxuan; Liu Nan (2025)

6 (6);

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

NPJ digital medicine – Liu Mingxuan; Ning Yilin; Teixayavong Salinelat; Liu Xiaoxuan; Mertens Mayli; Shang Yuqing; Li Xin; Miao Di; Liao Jingchi; Xu Jie; Ting Daniel Shu Wei; Cheng Lionel Tim-Ee; Ong Jasmine Chiat Ling; Teo Zhen Ling; Tan Ting Fang; RaviChandran Narrendar; Wang Fei; Celi Leo Anthony; Ong Marcus Eng Hock; Liu Nan (2025)

8 (1);

A translational perspective towards clinical AI fairness.

NPJ digital medicine – Liu Mingxuan; Ning Yilin; Teixayavong Salinelat; Mertens Mayli; Xu Jie; Ting Daniel Shu Wei; Cheng Lionel Tim-Ee; Ong Jasmine Chiat Ling; Teo Zhen Ling; Tan Ting Fang; RaviChandran Narrendar; Wang Fei; Celi Leo Anthony; Ong Marcus Eng Hock; Liu Nan (2023)

6 (1);

Large language models in global health

Nature Health – Jasmine Chiat Ling Ong, Yilin Ning, Rui Yang, Danielle S Bitterman, Xiaoxuan Liu, Yih Chung Tham, Gary S Collins, Michelle María Jiménez de Tavárez, Bilal A Mateen, Kwesi Nyan Amissah-Arthur, Bin Sheng, Iain Bee Huat Tan, Chuan Hong, Lionel Tim-Ee Cheng, Benjamin Alan Goldstein, Phuoc V Le, Yun Liu, Hiang Khoon Tan, Marcus Eng Hock Ong, Siegfried K Wagner, Alastair K Denniston, Pearse A Keane, Josip Car, Wendy W Chapman, Karel GM Moons, Tien Yin Wong, Eric J Topol, Nan Liu (2026)

1 (1); 35-47

How can artificial intelligence transform the training of medical students and physicians?

The Lancet. Digital health – Ning Yilin; Ong Jasmine Chiat Ling; Cheng Haoran; Wang Haibo; Ting Daniel Shu Wei; Tham Yih Chung; Wong Tien Yin; Liu Nan (2025)

7 (10);

Shapley variable importance cloud for interpretable machine learning.

Patterns (New York, N.Y.) – Ning Yilin; Ong Marcus Eng Hock; Chakraborty Bibhas; Goldstein Benjamin Alan; Ting Daniel Shu Wei; Vaughan Roger; Liu Nan (2022)

3 (4);

A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes.

STAR protocols – Xie Feng; Ning Yilin; Liu Mingxuan; Li Siqi; Saffari Seyed Ehsan; Yuan Han; Volovici Victor; Ting Daniel Shu Wei; Goldstein Benjamin Alan; Ong Marcus Eng Hock; Vaughan Roger; Chakraborty Bibhas; Liu Nan (2023)

4 (2);

Identifying clinical features and blood biomarkers associated with mild cognitive impairment in Parkinson disease using machine learning.

European journal of neurology – Deng Xiao; Ning Yilin; Saffari Seyed Ehsan; Xiao Bin; Niu Chenglin; Ng Samuel Yong Ern; Chia Nicole; Choi Xinyi; Heng Dede Liana; Tan Yi Jayne; Ng Ebonne; Xu Zheyu; Tay Kay-Yaw; Au Wing-Lok; Ng Adeline; Tan Eng-King; Liu Nan; Tan Louis C S (2023)

30 (6); 1658-1666

Variable importance analysis with interpretable machine learning for fair risk prediction.

PLOS digital health – Ning Yilin; Li Siqi; Ng Yih Yng; Chia Michael Yih Chong; Gan Han Nee; Tiah Ling; Mao Desmond Renhao; Ng Wei Ming; Leong Benjamin Sieu-Hon; Doctor Nausheen; Ong Marcus Eng Hock; Liu Nan (2024)

3 (7);

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

NPJ digital medicine – Ong Jasmine Chiat Ling; Ning Yilin; Liu Mingxuan; Ma Yian; Zhao Liang; Singh Kuldev; Chang Robert T; Vogel Silke; Lim John C W; Tan Iris Siu Kwan; Freyer Oscar; Gilbert Stephen; Bitterman Danielle S; Liu Xiaoxuan; Denniston Alastair K; Liu Nan (2026)

9 (1);

AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes.

BMC medical research methodology – Saffari Seyed Ehsan; Ning Yilin; Xie Feng; Chakraborty Bibhas; Volovici Victor; Vaughan Roger; Ong Marcus Eng Hock; Liu Nan (2022)

22 (1);

Gender disparities among adult recipients of layperson bystander cardiopulmonary resuscitation by location of cardiac arrest in Pan-Asian communities: A registry-based study.

EClinicalMedicine – Liu Nan; Ning Yilin; Ong Marcus Eng Hock; Saffari Seyed Ehsan; Ryu Hyun Ho; Kajino Kentaro; Lin Chih-Hao; Karim Sarah Abdul; Rao G V Ramana; Ho Andrew Fu Wah; Lim Shir Lynn; Siddiqui Fahad Javaid; (2022)

Estimating risk ratio from any standard epidemiological design by doubling the cases.

BMC medical research methodology – Ning Yilin; Lam Anastasia; Reilly Marie (2022)

22 (1);

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

Patterns (New York, N.Y.) – Liu Mingxuan; Ning Yilin; Ke Yuhe; Shang Yuqing; Chakraborty Bibhas; Ong Marcus Eng Hock; Vaughan Roger; Liu Nan (2024)

5 (10);

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

Journal of biomedical informatics – Li Siqi; Ning Yilin; Ong Marcus Eng Hock; Chakraborty Bibhas; Hong Chuan; Xie Feng; Yuan Han; Liu Mingxuan; Buckland Daniel M; Chen Yong; Liu Nan (2023)

146

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Journal of biomedical informatics – Xie Feng; Ning Yilin; Yuan Han; Goldstein Benjamin Alan; Ong Marcus Eng Hock; Liu Nan; Chakraborty Bibhas (2022)

125

Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.

Journal of the American Medical Informatics Association : JAMIA – Li Siqi; Liu Pinyan; Nascimento Gustavo G; Wang Xinru; Leite Fabio Renato Manzolli; Chakraborty Bibhas; Hong Chuan; Ning Yilin; Xie Feng; Teo Zhen Ling; Ting Daniel Shu Wei; Haddadi Hamed; Ong Marcus Eng Hock; Peres Marco Aurélio; Liu Nan (2023)

30 (12); 2041-2049

International partnership for governing generative artificial intelligence models in medicine.

Nature medicine – Ong Jasmine Chiat Ling; Ning Yilin; Collins Gary S; Bitterman Danielle S; Beecy Ashley N; Chang Robert T; Denniston Alastair K; Freyer Oscar; Gilbert Stephen; de Hond Anne; Leeuwenberg Artuur M; Zhao Liang; Lim John C W; Liu Mingxuan; Liu Xiaoxuan; Longhurst Christopher A; Ma Yian; Qiu Yue; Sarkar Rupa; Sheng Bin; Singh Kuldev; Tan Iris Siu Kwan; Tham Yih Chung; Thirunavukarasu Arun J; Ting Daniel Shu Wei; Vogel Silke; Zhang Rui; Zhao Jianfei; Chapman Wendy W; Shah Nigam H; Moons Karel G M; Wong Tien Yin; Liu Nan (2025)

31 (9); 2836-2839

Directory

Bio

Education

Doctor of Philosophy

Bachelor of Science in Statistics Hons Class 2A

Research Interests

Publications

An ethics assessment tool for artificial intelligence implementation in healthcare: CARE-AI.

Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist.

⁠Advancing ethical AI in healthcare through interpretability.

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

A translational perspective towards clinical AI fairness.

Large language models in global health

How can artificial intelligence transform the training of medical students and physicians?

Shapley variable importance cloud for interpretable machine learning.

A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes.

Identifying clinical features and blood biomarkers associated with mild cognitive impairment in Parkinson disease using machine learning.

Variable importance analysis with interpretable machine learning for fair risk prediction.

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

AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes.

Gender disparities among adult recipients of layperson bystander cardiopulmonary resuscitation by location of cardiac arrest in Pan-Asian communities: A registry-based study.

Estimating risk ratio from any standard epidemiological design by doubling the cases.

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

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

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.

International partnership for governing generative artificial intelligence models in medicine.

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