Preparing Health Systems for the Future of AI for Health: Ethical Challenges and Lesson Learned

When

28 October 2025

09:00 - 16:30

Bookings

Bookings closed

Where

IAS Seminar Room (Ground Floor)
Ground Floor, Block C, IAS Building, Universiti Malaya, Kuala Lumpur

Event Type

Workshop

Artificial Intelligence (AI) is reshaping health research and practice — from early disease detection and diagnosis to personalised care, risk prediction, and large-scale community data analysis. Yet, as these technologies accelerate, they bring complex ethical challenges: data privacy, patient safety, algorithmic bias, transparency, equitable access, and even the risk of digital colonialism.

About the Workshop

This interactive workshop brings together regional experts, policymakers, and researchers to co-develop a framework for ethical digitalisation of health information systems in Asia-Pacific economies — adaptable to diverse contexts worldwide.

Participants will explore how to:

Ensure ethical governance in AI-driven health systems
Strengthen data equity and inclusiveness across fragmented systems
Learn from global experiences to avoid repeating digital missteps
Identify best practices and safeguards for responsible AI in health research

Through expert presentations and roundtable discussions, we’ll leverage the interdisciplinary and cross-cultural strengths of the APRU network to shape a regional consensus statement on the ethical use of AI in health.

Why Join

Gain insights from global thought leaders in AI ethics and digital governance
Contribute to the development of actionable policy frameworks for the region
Network with professionals shaping the future of ethical digital health
Be part of a movement toward responsible, inclusive, and equitable AI in health systems

Featured Speakers

Dr Mellisa Withers
Program Director of Global Health Program
A Professor and Director of Pedagogical Enrichment and Training in the Department of Population and Public Health Sciences at the University of Southern California’s Keck School of Medicine, and the Director of the APRU Global Health Program. Her work focuses on global health, women’s empowerment, migrant and mental health, and community-based research. With a PhD in Community Health Sciences from UCLA, she brings rich international experience, having lived and conducted research across several countries, and continues to inspire through her global perspective and passion for cultural understanding.

Dr Peter Sy
Assistant Vice President for Digital Transformation, University of the Philippines
A leading philosopher and ethicist, Dr Sy advises the Philippine Departments of Health and Science & Technology on eHealth privacy and governance. His expertise spans AI ethics, research ethics, and open data, and he co-founded the UP Social Innovations Lab.

Dr Hannah Yee-Fen Lim
Associate Professor, Nanyang Technological University (NTU), Singapore
A world-renowned authority in AI and law, Dr Lim has advised the World Health Organisation and led multimillion-dollar research on AI governance, digital ethics, and data law. Her groundbreaking work has influenced global frameworks for AI in healthcare and smart cities.

Dr Vivek Jason Jayaraj
Deputy Director (Analytics & Informatics), Digital Health Division, Ministry of Health Malaysia
A public health physician and data scientist, Dr Jayaraj drives Malaysia’s digital health strategy. His work in predictive analytics, COVID-19 forecasting, and precision health reflects a vision of data-driven public health with empathy and impact.

Dr Cormekki Whitley
Chief Operating Officer, Data.org, USA
Bringing nearly 30 years of experience in nonprofit leadership. She oversees the organisation’s operations, finance, HR, and programme implementation, while leading financial inclusion capacity-building initiatives across the United States and Asia Pacific. Previously, she served as Interim Executive Director and COO at the Centre for Law and Social Policy (CLASP), where she secured major funding, strengthened operations, and advanced high-impact initiatives. Her expertise spans financial management, organisational development, fundraising, and change management.

Passionate about equity and inclusion, Cormekki also contributes her skills as a board and committee member for organisations such as Volunteers of America and Code for Science & Society. She holds a degree in accounting from NC State University and advanced degrees from Meredith College and Northcentral University.

Christina Schönleber
Chief Strategy Officer at APRU
She is responsible for the direction of APRU’s strategic priority of informing policy development on solutions to Asia Pacific challenges and the development of APRU’s research-related program areas. As Head of Knowledge Exchange at the Royal College of Art in London, she developed and implemented the RCA’s strategy for Knowledge Exchange and set up key industrial and third-party collaborations with major international corporations such as Tata Consultancy Services and Visa Europe. As Assistant Director of the University of Kent’s Innovation and Enterprise Department, Christina led the University’s Knowledge Exchange development in key areas related to science and social science.

Dr Sanjay Rampal Lekhraj Rampal
Executive Director of Research Development, Universiti Malaya, Malaysia
He is a Professor of Epidemiology and Public Health Physician. He also heads the Western Pacific Regional Training Centre, which is supported by the Special Programme for Research and Training in Tropical Diseases (TDR). Medically trained in India, he served in Malaysia’s Ministry of Health before earning a Master of Public Health from Harvard University and a PhD from Johns Hopkins University. He is Certified in Public Health (CPH) by the US National Board of Public Health Examiners. Dr Rampal aims to drive transformative growth in research and education within public health. He seeks to improve the nation’s health by focusing on the prevention of cardiovascular and metabolic diseases, as well as their intersection with cancer and certain communicable diseases. Dr Rampal is dedicated to advancing public health through excellence in research, education, and collaborative leadership.

Dr Eleni Dimokidis
Head of Healthcare Technology for Asia Pacific and Japan, Amazon Web Services (AWS)
She drives healthcare technology innovation and nationwide digital transformation in collaboration with Ministries of Health and governments. With multidisciplinary expertise spanning technology, law, and life sciences, she holds a PhD in Regenerative Medicine and multiple advanced degrees in Bioengineering, Nanotechnology, Law, and Computer Science. Her work covers key areas including digital health, AI, genomics, telemedicine, and bioinformatics, advancing AWS’s mission to strengthen healthcare systems and improve patient outcomes across the region.

Dr Jonathan Guillemot
Universidad San Francisco de Quito
He is the Director of the Institute of Social Medicine and Global Challenges, Universidad San Francisco de Quito. Jonathan R. Guillemot holds a PhD in Global Health, Social Medicine, and Gerontology from King’s College London (United Kingdom). Beginning his academic journey with undergraduate and graduate degrees in Political Sociology at the Université de Lille (France), he worked as an analyst in Health Economics in London (United Kingdom) for close to a decade before joining Universidad San Francisco de Quito. Currently, he serves as the Associate Dean of the School of Medicine, where he is also the Director of the Institute of Social Medicine & Global Challenges and a full-time associate professor. His teaching portfolio includes subjects such as Ageing and Later Life, Sociology of Health, and Clinical Ethics. Dr Guillemot engages in research and community outreach projects in the areas of Gerontology and Research Pedagogy, integrating initiatives such as One Health.

Domains and Discussion Points

Ethical principles from a Pacific Rim perspective

A shared set of values keeps decisions consistent across very different health systems in the region. Principles like “do no harm”, fairness, respect for persons, and accountability help us decide when AI belongs in care—and when it doesn’t. Clear values also guide tough choices when benefits and risks are finely balanced. They should work for both big urban hospitals and rural clinics with limited resources.

What values must we never compromise?
When is it acceptable to say “no” to an AI tool?
How do we balance benefit vs harm fairly for different settings?
Who decides on rare exceptions, and how?

Consent, privacy and data rights

Patients should understand what they’re agreeing to, in language they can follow, and be able to change their minds later. Privacy isn’t a nice-to-have; it is core to trust, especially when data move across organisations or borders. People should know what is collected, why, for how long, and who can see it. Simple processes must exist to view, correct, or delete data where appropriate.

How do we make consent meaningful, not just a tick box?
What data should never be shared or sold?
What happens if someone withdraws consent?
How can patients see, correct, or delete their data?

Fairness, bias and inclusion

AI must serve everyone, including small or historically overlooked groups. If the data under-represent a community, the system may perform worse for them—and that is an ethical failure. We need routine checks for uneven error rates and plans to fix them. Public reporting (where safe) helps keep us honest and builds confidence.

Does the tool (the AI) work equally well for all groups?
How will we spot and correct unfair results?
What if some groups are missing from the data?
What should we report publicly about fairness?

Safety and clinical responsibility

Patient safety comes first, always. AI can support decisions, but humans remain responsible for care and outcomes. Clear rules are needed for testing, go-live, and what to do when things go wrong—up to and including an immediate rollback. Learning from incidents should be fast, blame-aware, and focused on improvement.

Who is responsible if a patient is harmed?
What warning signs trigger a pause or rollback?
How do we test updates before patients see them?
How are incidents reported and turned into learning?

Transparency and explainability

People deserve to know when AI is used, what it does well, and where it is limited. Explanations should be honest and understandable, with more detail available for clinicians who need it. Clear labelling prevents “invisible AI” in care pathways. Basic facts—intended use, training data sources, and limitations—should not be hidden.

What must clinicians and patients be told, in plain language?
What information about the model must be public?
When is a simple explanation enough—and when isn’t it?
How do we label AI use clearly in care?

Governance and oversight

Good governance sets the rules of the road: who approves AI, who monitors it, and who can stop it. Oversight must be independent enough to challenge poor decisions, yet close enough to act quickly. Conflicts of interest must be declared and managed. Documented decisions allow audits and protect patients and staff.

Who approves, monitors, and can stop an AI system?
How do we manage conflicts of interest?
What records prove decisions were ethical?
How do we involve ethics committees early, not late?

Interoperability and security (as ethical duties)

Unsafe connections and weak security put patients at risk, even if the model is good. Minimum standards for data formats, APIs, identity, logging, and audit trails are ethical safeguards, not mere IT details. Designs should work in low-connectivity or offline settings so no one is left behind. Vendors must prove they meet these basics before deployment.

What “minimum safe” standards are required to connect systems?
How do we protect identity, logs, and audit trails?
How do we keep care safe in low-resource or offline settings?
Who checks that vendors meet these safeguards?

Evidence, validity and performance over time

An AI system proven elsewhere may not work the same in our population. Local testing before first use is essential, and regular checks are needed as patients, practice and data shift. We should track meaningful outcomes: better health, fewer harms, and fair access—not just accuracy scores. Sometimes the ethical choice is to retrain, restrict, or retire a model.

What proof is needed before first use here?
How will we detect drift and re-test on a schedule?
Which outcomes (benefit, harm, equity) must we track?
When do we retire or retrain a model?

Ethical procurement and vendor obligations

Contracts should turn ethics into enforceable duties. They must set limits on data use and intellectual property, require transparency, and tie service levels to safety and equity—not only uptime. Vendors should support updates responsibly and plan for end-of-life without trapping the health system. Penalties for broken promises protect patients and the public.

Which terms lock in safety, transparency, and support?
What limits apply to data use and intellectual property?
Which service levels link to safety and equity?
What penalties apply if promises are broken?

Public voice, trust and capacity building

AI in health works best when people are involved and staff are prepared. Communities should have a say in how tools are chosen and used. Clinicians and managers need training to use AI safely and to explain it clearly. Sharing lessons across the region helps everyone move faster and avoid repeating mistakes.

How do we involve patients and communities in decisions?
What training do clinicians and staff need now?
How do we explain benefits and risks simply?
How can countries share lessons across the region?

Programme

0900 – 1000 hrs

Registration

1000 – 1015 hrs

Welcome & Introductions (Christina Schönleber, APRU)

1015 – 1040 hrs

Plenary 1 – Peter Sy (University of the Philippines)

1040 – 1105 hrs

Plenary 2 – Hannah Yee-Fen Lim (Nanyang Technological University, Singapore)

1105 – 1130 hrs

Plenary 3 – Vivek Jason Jayaraj (Ministry of Health, Malaysia)

1130 – 1215 hrs

Cross-stakeholder Panel Discussion

Speakers: Cormekki Whitley (Data.org) & Eleni Dimokidis (AWS)

Moderator: Sanjay Rampal (Universiti Malaya, Malaysia)

1215 – 1245 hrs

Round-table Briefing and Group Assignment

1245 – 1345 hrs

Lunch

1345 – 1530 hrs

Round-table Discussions

1530 – 1615 hrs

Plenary Report back & Live Editing of Consensus Draft

1615 – 1630 hrs

Closing Remarks and Next Steps

Towards an Ethical Digital Future

Join us in shaping a regional vision where AI innovation serves equity, ethics, and human wellbeing. Together, we can build trust in digital health and ensure that technological progress benefits all communities — not just a privileged few.

Bookings

Bookings are closed for this event.