- AI models can support doctors in making critical decisions and expand access to care in low-resource settings
- However, gaps in infrastructure, skills and governance continue to limit safe and effective adoption
- Researchers call for an international consortium to guide regulation and responsible use of AI in healthcare
Singapore, 27 January 2026—After a cardiac arrest, families and doctors are often faced with agonising uncertainty about a patient’s chances of recovery. This uncertainty is even greater in hospitals with limited resources, where access to advanced diagnostic tools and large datasets is constrained.
In one example of how artificial intelligence (AI) can help bridge this gap, researchers from Duke-NUS Medical School and their collaborators have adapted an advanced AI model to accurately predict neurological recovery after cardiac arrest in a resource-limited setting.
Published in npj Digital Medicine, the study used transfer learning, an advanced AI approach that adapts pre-trained models built on large datasets, to new settings with limited local data. This method improves performance in new environments without requiring extensive data collection, making it particularly relevant for low-and-middle-income countries.
The team took a brain-recovery prediction model built in Japan using data from 46,918 out-of-hospital cardiac-arrest patients and adapted it for use in Vietnam, where it was tested on a smaller cohort of 243 patients.
The adapted model performed significantly better, correctly distinguishing high‑risk from low‑risk patients about 80 per cent of the time, compared with around 46 per cent when the original model was used in the Vietnam context.
Senior author Associate Professor Liu Nan, from Duke-NUS’ Centre for Biomedical Data Science and director of the Duke-NUS AI + Medical Sciences Initiative, said:
“The study shows AI models to not need to be rebuilt from scratch for every new setting. By adapting existing tools safely and effectively, transfer learning can lower costs, reduce development time and help extend the benefits of AI to healthcare systems with fewer resources.”
Expanding AI’s role in limited-resource settings
Beyond outcome prediction, AI also holds promise across a wide range of healthcare applications in low-and middle-income countries. In a separate study published in Nature Health, Duke-NUS researchers and their collaborators, including counterparts from University College London (UCL), examined how large language models (LLMs)—trained on vast volumes of text to understand and generate human language—could be applied to advance global health. In resource-constrained environments, they have the potential to improve access to care, diagnostics and clinical decision-making.
Examples highlighted include a chatbot providing pregnancy-related information to expecting mothers in South Africa and smartphone-based applications used by community health workers in Sierra Leone to detect malaria infections from blood smear samples, a more cost-efficient method than conventional microscope-based systems.
Despite these innovative advances, the researchers noted that AI development and deployment remain heavily concentrated in high-income and upper-middle-income settings.
While 63 per cent of surveyed researchers, clinicians and service providers report actively using AI tools[1], many low-and-middle-income countries continue to face barriers such as limited infrastructure, insufficient expertise and a lack of existing local knowledge on addressing these gaps.
Co-author Siegfried Wagner, from UCL Institute of Ophthalmology and Moorfields Eye Hospital NHS Foundation Trust, said:
“LLMs have the greatest opportunity to transform healthcare in settings where specialist physicians are scarcest, but the global health community needs to work together with some urgency to ensure the implementation of LLMs is supported in regions where adoption is most challenging.”
Dr Ning Yilin, Senior Research Fellow at Duke-NUS’ Centre for Biomedical Data Science and a co-first author of the study, added that empowering people should be the priority when integrating LLMs into healthcare:
“Strengthening digital literacy and building confidence in using these tools will ensure AI supports, rather than disrupts, the workforce. Tailored skills-development pathways can help under-resourced workers adapt and thrive, allowing AI to uplift and add value to clinical and administrative roles.”
Charting the path forward: governance and guardrails
While AI tools have the potential to improve healthcare delivery, appropriate governance frameworks are essential for safe and ethical implementation. Existing regulations for medical technologies often do not address AI-specific risks, including privacy concerns and model hallucinations, nor do they clearly refine accountability for safe deployment and oversight of new tools.
To address these gaps, researchers led by Duke-NUS have proposed the creation of an international consortium—the Partnership for Oversight, Leadership, and Accountability in Regulating Intelligent Systems-Generative Models in Medicine (POLARIS-GM).
The consortium aims to establish actionable best-practice guidance for regulating new tools, monitoring their impact, establishing safety guardrails and adapting them for resource-limited settings. Bringing together healthcare leaders, regulators, ethicists and patient groups worldwide, POLARIS-GM will adopt a phased approach, beginning with a review of existing research before working towards global consensus on AI governance in healthcare.
Dr Jasmine Ong from Duke-NUS AI + Medical Sciences Initiative and a Principal Clinical Pharmacist at Singapore General Hospital, first author of the correspondence published in Nature Medicine, said:
“With clear oversight and clearly defined guidelines, healthcare systems can confidently leverage AI’s many strengths to improve health outcomes while steering clear of potential pitfalls. From policymakers to patient groups, all stakeholders have a crucial role to play in making this goal a reality.”
Duke-NUS continues to catalyse and advance biomedical research at the intersection of medicine, data science and health systems, with a focus on improving care delivery and outcomes globally.
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