BANGKOK, Thailand — At the Global Medical AI Summit 2026, held at True Digital Park in Bangkok, international clinicians, technology pioneers, and healthcare policymakers convened to discuss the practical integration of artificial intelligence into real-world healthcare. Representing the Philippines, Dr. Venus Oliva Cloma-Rosales, physician, public health researcher, and Managing Director of 101 Health Research, delivered an insightful address during the panel session titled “Hospitals & Partners’ Perspective on Medical AI”.
Sharing the stage with Att Thongtang, CEO of Phyathai and Paolo Hospital Group; Assoc. Prof. Trongtum Tongdee, Head of Radiology at Siriraj Hospital; and Dr. Charlie Lee, APJ Lead at Amazon Web Services (AWS), with moderation by Sukrit Chunlaphan, CEO of TecTony, Dr. Cloma-Rosales moved past theoretical concepts to present concrete implementations of health data science in governance and workforce planning.
AI in Action: Two High-Impact Policy Use Cases
Under the theme “AI in Action: Two Use Cases in Health Policy & Research,” Dr. Cloma-Rosales illustrated how natural language processing (NLP) and automated data workflows convert messy, unstructured information into clear evidence for institutional decision-makers.
1. Evaluating National Health Research Investments (Research Policy)
In collaboration with the Department of Science and Technology – Philippine Council for Health Research and Development (DOST-PCHRD), 101 Health Research spearheaded the Midterm Evaluation of the Philippine National Unified Health Research Agenda (NUHRA).
Assessing national research investments across NUHRA’s 11 priority areas historically posed an immense data challenge. Project records sat fragmented across disparate silos—including separate grant registers, clinical trial protocols, ongoing study trackings, and published literature. Moreover, health research topics frequently intersect multiple disciplines; for instance, a project examining teenage pregnancy spans both child and adolescent health as well as reproductive health.
Using NLP and text classification models, Dr. Cloma-Rosales’ team systematically categorized and mapped complex, multi-thematic studies across nationwide databases. This automation eliminated what would have been an overwhelmingly tedious manual review, delivering empirical evidence to guide national health research funding and agenda-setting as the Philippines prepares for the upcoming 2028 end-term NUHRA evaluation.
2. Mapping Global Demand for Nurses (Health Workforce)
Addressing health human resource challenges, 101 Health Research partnered with Women in Global Health (WGH) Philippines, IDRC Canada, FutureWORKS Asia, and regional research partners across Sri Lanka and Bangladesh to study the international labor market for nurses.
The research team deployed automated web scraping to extract more than 14,000 nursing vacancy listings worldwide from diverse international job boards and hospital portals. Because job advertisements are inherently unstructured and vary widely by region, AI algorithms standardized the data to identify global patterns in required clinical competencies, compensation packages, benefits, and working conditions.
The study revealed an unexpected, actionable insight: among the top ten most frequently demanded competencies worldwide was advanced proficiency in basic digital productivity tools, specifically Microsoft Excel. Dr. Cloma-Rosales emphasized how this finding directly informs health sciences curricula in the Philippines, highlighting that before nurses can be expected to interact seamlessly with electronic health records (EHRs) and clinical AI, foundational digital literacy must be strengthened at the undergraduate level.
Combining Technical Automation with Human Expertise
Dr. Cloma-Rosales underscored that algorithms alone cannot guarantee public health impact. Her team’s operational pipeline pairs four technical and clinical components:
- Automated Web Scraping: Sourcing real-time, real-world data from global digital environments.
- Coding and Data Integration: Structuring fragmented datasets into unified analytical frameworks.
- Natural Language Processing & Text Classification: Categorizing complex, overlapping semantic themes across health domains.
- Expert Human Validation: Subjecting model outputs to peer-level clinical and epidemiological review to ensure contextual accuracy.
She also noted the team’s commitment to regional capacity-building. Both health data initiatives integrated student interns from the University of the Philippines (UP) and the National University of Singapore (NUS), providing upcoming ASEAN researchers with hands-on training in applied health artificial intelligence.
A Systems Framework: From Micro Interactions to the Learning Health System
In her panel intervention and closing statement, Dr. Cloma-Rosales urged leaders to evaluate medical AI through a three-dimensional health systems framework:
“First, if you think about AI and healthcare, you have to understand that it is a health system. You have to consider the various levels: How will it impact the micro level, where the provider, clinical information, and patient interface meet? How does it affect the meso level of hospital management, and the macro level of national health infrastructure and policy? You have to account for all those tiers.
Next, the beauty of AI is that it has made it so much easier for us to transform a health system into a ‘Learning Health System’—one where we learn systematically from our own data and our own outcomes. That is the ultimate aspiration: to become self-improving, data-driven, and resilient.
And third, I would like to invite all of you to consider partnering outside of Thailand. Please consider the Philippines and 101 Health Research for collaborative studies.”
Her perspective aligned with core themes echoed throughout the summit. Thai Deputy Prime Minister and Minister of Higher Education, Science, Research and Innovation (MHESI), Prof. Dr. Yodchanan Wongsawat, presented Thailand’s strategy to shift from being mere consumers of AI to becoming global deep-tech producers and clinical research leaders through initiatives like Siam Medica. Similarly, SEA HI Hub founder Dr. Supachai observed that while global superpowers dominate foundational AI development (“Game 1”), Southeast Asian nations hold a distinct opportunity to lead “Game 2″—translating technology into safe, accessible, and meaningful healthcare outcomes. Venture capital leaders at the event reinforced this sentiment, underscoring their preference for sustainable businesses addressing real healthcare pain points over AI software in search of a problem.
By showing how applied AI, rigorous human oversight, and cross-border partnerships can address health systems challenges, Dr. Cloma-Rosales demonstrated the Philippines’ readiness to contribute actively to the regional medical AI agenda.
