Lab Introduction

The Health Policy and Artificial Intelligence Lab focuses on advancing the responsible development, evaluation, and adoption of artificial intelligence within health systems. Our research seeks to bridge methodological innovation in artificial intelligence with health policy, health economics, and health systems research in order to generate evidence that supports equitable, efficient, and sustainable healthcare systems.

The lab is led by Principal Investigator John Tayu Lee, Associate Professor at National Taiwan University. Dr. Lee’s research focuses on health policy, health economics, and the application of artificial intelligence in health systems. His work has been cited more than 5,500 times (h-index 40; i10-index 75). He is also a recipient of the Alan Williams Fellowship, which recognizes promising mid-career researchers in health economics.

Before joining National Taiwan University, Dr. Lee held faculty or senior investigator positions at several universities, including Imperial College London, the National University of Singapore, the University of Melbourne, and the Australian National University. He has also been a Visiting Scientist at Harvard University and the Baker Institute for Heart and Diabetes Research. His research has been supported by more than ten competitive grants as Principal Investigator, and he has collaborated with international organizations and funding agencies, including the Bill & Melinda Gates Foundation.

The lab’s research agenda is organized around five interrelated domains: 

1. Machine Learning for Health Policy and Systems Modeling
This domain develops and applies advanced machine learning and data science methods to support health policy analysis and health system decision-making. Research focuses on building explainable and fairness-aware predictive models using large-scale administrative, clinical, and population datasets, including national health insurance databases and longitudinal survey data. These models are used to understand disease risk, healthcare utilization, and system performance, and to simulate the potential impact of policy interventions on population health and health system outcomes. Emphasis is placed on interpretable modeling, fairness evaluation, and the integration of machine learning with epidemiological and statistical approaches.

2. Health Technology Assessment and Economic Evaluation of Artificial Intelligence
This domain evaluates the value, cost-effectiveness, and policy implications of digital and AI-based health interventions. Building on established frameworks in health technology assessment (HTA), the research examines the economic, clinical, and societal impacts of AI applications in healthcare. Studies employ cost-effectiveness analysis, cost–benefit analysis, budget impact analysis, and health system modeling to inform decisions about investment, reimbursement, and large-scale adoption of AI technologies. Particular attention is given to the unique lifecycle characteristics of AI systems, including continuous learning, model updating, and infrastructure costs.

3. Responsible AI Governance, Safety, and Health Equity
This domain examines the ethical, regulatory, and governance challenges associated with the integration of artificial intelligence into healthcare systems. Research addresses issues such as algorithmic bias, transparency, accountability, model drift, patient safety, and regulatory oversight. The goal is to develop evidence-based governance frameworks that ensure AI technologies are implemented in ways that protect patient rights, maintain public trust, and reduce health inequities. This work also explores international regulatory approaches and policy tools that support responsible and safe AI adoption within healthcare systems.

4. AI and Health System Transformation
Artificial intelligence has the potential to reshape healthcare delivery, workforce roles, and system organization. This domain investigates how AI technologies influence healthcare productivity, workforce dynamics, professional roles, and service delivery models. Research examines the impact of AI on health workforce employment, skill requirements, and organizational change, as well as the broader economic and labor market implications of automation and decision-support technologies in healthcare. Through empirical and policy-oriented analyses, this domain seeks to understand how AI can support health system innovation while maintaining workforce sustainability and system resilience.

5. Training and Capacity Building for AI in Health Systems
The effective adoption of artificial intelligence in healthcare depends not only on technological advances but also on the capacity of clinicians, policymakers, regulators, and the public to understand and evaluate AI systems. This domain focuses on developing interdisciplinary education and training programs that build AI literacy and governance capacity within health systems. Research evaluates different educational interventions and training models designed to improve understanding, trust, and responsible use of AI technologies. The program also supports cross-disciplinary collaboration across medicine, public health, data science, and the social sciences, helping to cultivate the next generation of researchers and practitioners in AI-enabled health policy and health systems research.

Dr. Lee also contributes to the academic community through editorial service. He serves on the editorial teams of several international journals, including BMJ Global Health, PLOS Digital Health, Humanities and Social Sciences Communications, and npj Digital Public Health. Through these roles, he supports the peer-review and editorial processes for research in global health, digital health, health policy, and the responsible use of artificial intelligence in healthcare systems.

Selected Media Coverage:

1. Central News Agency (CNA): https://www.cna.com.tw/news/ahel/202511060395.aspx
2. Broadcasting Corporation of China (BCC) News: https://www.rti.org.tw/news?uid=3&pid=173904
3. SET News: https://health.setn.com/news/1747565
4. CTi News: https://ctinews.com/news/items/4OaZM47NW6
5. United Daily News (UDN): https://udn.com/news/story/7266/9123321
6. Commercial Times: https://www.ctee.com.tw/news/20251106701582-431401
7. Liberty Times: https://health.ltn.com.tw/amp/article/breakingnews/5236561
8. Economic Daily News: https://money.udn.com/money/amp/story/122328/9121848
9. China Daily: https://www.chinadaily.com.cn/a/202006/01/WS5ed46da0a310a8b241159db6.html
10. Harvard T.H. Chan School of Public Health News: https://hsph.harvard.edu/news/ai-chatbots-unreliable-sources-for-stroke-care-information/
11. Imperial College London News: https://www.imperial.ac.uk/news/120431/smokefree-workplaces-linked-smokefree-homes-india/
12. University of Melbourne Newsroom: https://pursuit.unimelb.edu.au/articles/are-we-getting-the-treatment-of-chronic-diseases-wrong
13. The Guardian: https://www.theguardian.com/society/2016/may/02/nhs-health-check-over-40s-marginal-benefits-study
14. BBC News: https://www.bbc.com/news/health-21067532
15. 2022 ABC Radio, News 7, New 9 〈On the out-of-pocket expenditure for medicine in Australia〉(Reference article: https://pursuit.unimelb.edu.au/articles/are-we-getting-the-treatment-of-chronic-diseases-wrong)
16. Mar 2013, Times of India, Hindustan Times, India 〈On the association between smoke-free public places and smoke-free homes in India〉

Selected Research Publications:

1. Costing Methods for Artificial Intelligence: Systematic Review and Recommended Cost Inventory for in Health Technology Assessment. Lee JT.*, Shen T., Liu V., Lu C., Chen T., Lee CC., Wu D., Huang CW., Atun R., Npj Digital Medicine, (Forthcoming, 2026)
2. Comparing Statistical and Deep Learning Models for Healthcare Utilization Prediction: Evidence from the SHARE Longitudinal Panel in Europe.  Li V, Lu Tzu-Pin, Wang C, Anindya K, Lee JT*. BMJ Health & Care Informatics, (Forthcoming, 2026)
3. Health Insurance Coverage and Quality of Care for Reproductive and Maternal Health in LMICs: a Multi-Country Propensity Score Matching Study. Marthias T., Anindya K., McPake B., Sukumar V., Hone T., La D., Zhao T., Atun R., Wang H., Pandu R., Lee JT* BMJ Global Health, (Forthcoming, 2026)
4. Wei Q., Yeh H.,… Lee JT* Comparing Private and Public Ambulance Services: A Scoping Review of Quality Outcomes. International Journal for Quality in Health Care (Forthcoming 2026)
5. Shen T., Yeh H., …Lee JT*  (2026) Artificial Intelligence Exposure and Regional Labor Market Risks in Taiwan: Scenario-Based Projections and Policy Responses. Journal of Occupational and Environmental Medicine.10.1097/DOI: 10.1097/
6. Tsai, C. H.-Y., Chang, S.-S., Bo Shen, T. K., Lin, H.-H., Cheng, S. H., & Lee, J.T.* (2026). Multimorbidity Indices for Adult Population: Systematic Review of Data Framework, Weighting Methods, and Applications. Archives of Gerontology and Geriatrics, 106216. doi: 10.1016/j.archger.2026.106216
7. Lee, JT*, Hsu, S. H., Li, V. C. S., Anindya, K., Chen, M. H., Wang, C., ... & Atun, R. Machine Learning Fairness in Predicting Underweight, Overweight and Adiposity Across Socioeconomic and Caste Group in India: Evidence from the Longitudinal Ageing Study in India. PLOS Digital. Health. 2025.
8. Lee, JT*, Li, V.CS., Wu, JJ. et al. (2025) Evaluation of performance of generative large language models for stroke care. Npj Digital Medicine. 8, 481. doi: 10.1038/s41746-025-01830-9
9. Lee JT*, Crettenden J, Tran My, Miller D., Cormack M., Cahill M., Li J.,, Sugiura T.,  & Xiang F., ,Methods for health workforce projection model: systematic review and recommended good practice reporting guideline. Human Resources for Health 2024;22(25).
10. Ali Shehzad, Alemu Feben W, Owen Jesse, Eells Tracy D, Antle Becky, Lee JT*, Wright Jesse H., .Cost-Effectiveness of Computer-Assisted Cognitive Behavioral Therapy for Depression Among Adults in Primary Care. JAMA Network Open.2024 ;7(11): e2444599.
11. Yang Lu, Lynch Chris, Lee JT*, Oldenburg Brian, Haregu Tilahun. The associations between broadband Internet, connection, well-being and psychosocial outcomes among middle-aged and older adults in China: findings from a National Longitudinal cohort Study.Journal of Medical Internet Research, JMIR 2024; e10.2196/preprints.59023.
12. Marthias Tiara, Anindya Kanya, Saputri Nurmala Selly, Putri Likke Prawidya, Atun Rifat, Lee JT*. Effective Coverage for Reproductive, Maternal, Neonatal and Newborn, Health: An Analysis of Socioeconomic and Geographical Inequalities in 39 Low-and Middle-income Countries. BMJ Global Health. 2024; e:10.1016/j.eclinm.2021.101103.
13. Lee JT*, McPake B, Anindya K, Puspandari DA, Marthias T. The effect of health insurance and socioeconomic status on women’s choice in birth attendant and place of delivery across regions in Indonesia: A multinomial logit analysis. BMJ Global Health. 2023;8: e007758.
14. DTV La, Y Zhao, P Arokiasamy, R Atun, S Mercer, T Marthias, B McPake., …, Lee JT*. Multimorbidity and out-of-pocket expenditure for medicines in China and India. BMJ Global Health. 2022;7(11): e007724.
15. Marthias T, McPake B, Carvalho N, Millett C, Anindya K, Saputri NS., …, Lee JT*. Associations between Indonesia’s national health insurance, effective coverage in maternal health and neonatal mortality: A multilevel interrupted time-series analysis 2000–2017. Journal of Epidemiology and Community Health. 2022;76(12):999-1010.
16. Zhao Y, Atun R, Anindya K, McPake B, Mathias T, Pan T, van Heusden A., …, Lee JT*. Medical costs and out-of-pocket expenditures associated with multimorbidity in China: quantile regression analysis. BMJ Global Health 2021;6: e004042.
17. Anindya K, Mathias T, Vellakkal S, Carvalho N, Atun R, Morgan A., …, Lee JT*. Socioeconomic inequalities in effective service coverage for reproductive, maternal, newborn, and child health: a comparative analysis of 39 low-income and middle-income countries. EClinicalMedicine 2021; 40:101123.
18. Huse ESG, Atun R, McPake B, Lee JT*. Use of social impact bonds in financing health systems responses to non-communicable diseases: scoping review. BMJ Global Health 2021;6(3): e004127.
19. Qin VM., Zhang Y., Chia KS., McPake B., Zhao Y., Hulse E., Legido-Quigley H., Lee JT* (senior author). Temporal trends and variation in out-of-pocket expenditures and patient cost sharing: evidence from a Chinese national survey 2011-2015. International Journal for Equity in Health. 2021. Vol 20.1-17.
20. Hulse E., Atun R., McPake B., Lee JT* (senior author). Use of social impact bonds in financing health systems responses to non-communicable diseases: scoping review. BMJ Global Health. 2021, 6 (3), e004127
21. Qian CX., Zhao Y., Anindya K, Tenneti N., Desloge A., Atun R., Qin VM., Mulcahy P., Lee JT* (Senior Author) Non-communicable disease risk factors and management among internal migrant in China: systematic review and meta-analysis. BMJ Global Health. 2021 Vol 6. e003324
22. Mulcahy P., Mahal A., McPake B., Kane S., Ghosh PK., Lee JT* (senior author). Is there an association between public spending on health and choice of healthcare providers across socioeconomic groups in India? Social Science and Medicine, 2021. 114149
23. Anindya K., Lee JT*., McPake B., Wilopo SA., Millett C., Carvalho N. Impact of Indonesia’s national health insurance scheme on inequality in access to maternal health services: a propensity score matched analysis. Journal of Global Health. 2020. 10 (1)
24. Zhao Y., Atun., Oldenburg B., McPake B., Tang SL., Mercer SW., Cowling TE., Lee JT* (senior author). Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: an analysis of population-based panel data. Lancet Global Health. 2020. 8 (6), e8540-849
25. Ishida M., Hulse E., Mahar R., Gunn J., Atun R., McPake B., Tenneti N., Lee JT* (senior author). The joint effect of physical multimorbidity and menal health conditions among adults in Australia. Preventing Chronic Disease, 2020 Dec 10; 17: E157.
26. Saputri NS., Spagnoletti B., Morgan A., Wilopo S., Singh A., McPake B., Atun R., Dewi R., Lee JT* (senior author) Progress towards reducing socioeconomic disparities in breastfeeding outcomes in Indonesia: a trend analysis from 2002 to 2017. BMC Public Health (2020) vol 20, 1-15
27. Haregu T., Lee JT., Oldenburg B., Armstrong Comorbid depression and obesity: correlates and synergistic association with non-communicable disease among Australian men. Preventing Chronic Disease. 2020. 17
28. Chang K., Vamos E., Palladino R., Majeed A., Lee JT*., Millett C, Impact of the NHS Health Check on inequalities in cardiovascular disease risk: a difference-in-differences matching analysis. Journal of Epidemiology and Community Health. 2019. 73 (1), 11-18
29. Annear PL., Lee JT*., Khim K., Ir P., Moscoe E., Jordanwood T., Bossert T. Protecting the poor? Impact of the national health equity fund on utilization of government health services in Cambodia, 2006-2013. BMJ Global Health. 2019. 4 (6), e001679
30. Sum G., Salisbury C., Koh G., Atun R., Oldenburg B., McPake B., Lee JT* (senior author). Implications of multimorbidity patterns on health care utilisation and quality of life in middle-income countries: cross-sectional analysis. Journal of Global Health. 2019. 9, 020413.
31. Qin V., Hone T., Millett C., Moreno Serra R., McPake B., Atun R., Lee JT* (senior author). The impact of user charges on health outcomes in low-and-middle income countries: a systematic review. BMJ Global Health. 2019. 3, e001087
32. Jawad M., Lee JT*., Glantz S., Millett C., Price elasticity of demand of non-cigarette tobacco products: a systematic review and meta-analysis. Tobacco Control. 2018. Doi: 10.1136/tobaccocontrol-2017-054056
33. Sum G., Hone T., Atun R., Millett C., Suhrcke M., Mahal A., Koh G., Lee JT*., Multimorbidity and out-of-pocket expenditure on medicine: a systematic review. BMJ Global Health. 2018, 3 (1), e000505
34. Alshamsan R., Lee JT*, Rana S., Areabi H., Millett C., Comparative health system performance in six middle-income countries: cross-sectional analysis using World Health Organization study of global ageing and health. Journal of the Royal Society of Medicine. 2017, 110 (9), 365-375.
35. Lee JT*., Lawson K., Wan Y., Majeed A., Morris S., Soljak M., Millett C., Are Cardiovascular Disease Risk Assessment and Management Programmes Cost Effective? A Systematic Review of the Evidence. Preventive Medicine. 2017. doi:10.1016/j.ypmed.2017.01.005
36. Hone T., Lee JT.*, Conteh L., Millett C., Does charging different user fees for primary and secondary care affect first-contacts with primary healthcare? A systematic review. Health Policy and Planning. 2017. 32 (5), 723-731.
37. Palladino R., Lee JT*., Hone T., Filippidis FT., Millett C., The great recession and increased cost sharing in european health systems. Health Affairs. 2016 Jul 01;35 (7);1204-1213. doi: 10.1377/hlthaff.2015.1170.
38. Chang KCM., Lee JT., Vamos EP., Soljak M., Johnston D., Khunti K., Majeed A., Millett C., Impact of the National Health Service Health Check on cardiovascular disease risk: a difference-in-differences matching analysis. Canadian Medical Association Journal (CMAJ). 2016 May 02. 151201. doi: 10.1503/cmaj.151201.
39. Palladino R., Lee JT.*, Ashworth M., Triassi M., Millett C., Associations between multimorbidity, healthcare utilisation and health status: evidence from 16 European countries. Age and Ageing. 2016 Mar 24; afw044. doi: 10.1093/ageing/afw044.
40. Lee JT.*, Huang Z., Basu S., Millett C., The inverse equity hypothesis: Does it apply to coverage of cancer screening in middle-income countries?. Journal of Epidemiology and Community Health. 2014 Oct 13; jech-2014-204355. doi:10.1136/jech-2014-204355.
41. Lee JT.*, Agrawal S., Basu S., Glantz SA., Millett C., Association between smoke-free workplace and second-hand smoke exposure at home in India. Tobacco Control. 2014 Jul 01; 23 (4), 308-312. doi:10.1136/tobaccocontrol-2012-050817.
42. Nazar GP., Lee JT.*, Glantz SA., Arora M., Pearce N., Millett C., Association between being employed in a smoke-free workplace and living in a smoke-free home: evidence from 15 low and middle income countries. Preventive Medicine. 2014 Feb 28; 59, 47-53. doi: 10.1016/j.ypmed.2013.11.017.
43. Millett C., Lee JT.*, Laverty AA., Glantz SA., Majeed A., Hospital admissions for childhood asthma after smoke-free legislation in England. Pediatrics. 2013 Feb 01; 131 (2), e495-e501. doi: 10.1542/peds.2012-2592.
44. Alshamsan R., Lee JT.*, Majeed A., Netuveli G., Millett C., Effect of a UK pay-for-performance program on ethnic disparities in diabetes outcomes: interrupted time series analysis. The Annals of Family Medicine. 2012 May 01; 10 (3), 228-234. doi: 10.1370/afm.1335.
45. Millett C., Lee JT.*, Gibbons DC., Glantz SA., Increasing the age for the legal purchase of tobacco in England: impacts on socio-economic disparities in youth smoking. Thorax. 2011 Apr 17; thx. 2010.154963. doi:10.1136/thx.2010.154963.