According to the Bank’s website, the proposed TA will enhance the understanding and accelerate the deployment of artificial intelligence (AI) applications in health among ADB developing member countries (DMCs). It will improve the readiness of health systems to adopt AI applications towards more equitable access and efficient delivery of quality health services. The TA will be implemented initially in Bangladesh, Indonesia, and Pakistan, to help survey the possible use cases of AI in primary care, hospital care, health professional education, and health insurance; and strengthen these countries AI governance frameworks, procurement systems, research and development, technology transfer and commercialization. The TA will deliver knowledge products to support project processing and implementation including: (i) PAK: Punjab Nursing and Health Workforce Reform Program (indicatively $150m for 2025 approval); (ii) BAN: health digitalization support project (indicatively $250m for 2026 approval), (iii) INO: Supporting Essential Health Actions and Transformation ($350m, ongoing) and (iv) INO: Teaching Hospitals Expansion Project (indicatively $650m for 2027 approval).

The application of AI has the potential to substantially improve efficiency, quality, and equitable access to healthcare services. AI applications leveraging big data analytics, deep learning, natural language processing, and robotics are improving diagnostic and treatment capabilities. These technologies are rapidly unlocking new possibilities in drug discovery and personalized medicine. On the other hand, the adoption of AI can also support the health system transition to a new paradigm of proactive health maintenance and management instead of reactive disease treatment, enabling accelerated progress towards universal health coverage.

Three trends are important underlying the AI applications developed for the health sector. First, advances in image processing and pattern recognition allow medical equipment to become "smart", boosted by AI's ability to generate diagnoses at the point of care. These have been applied in many clinical fields including cervical and other types of cancer screening, radiological imaging, ophthalmology, dermatology, and high-resolution microscopy. Such clinical decision support would allow a rural primary care clinic lacking pathologists or other experienced specialists to perform the essential screening or initial diagnosis accurately. In more advanced clinical settings, AI can greatly enhance the accuracy of certain treatments including robotic surgery, endoscopy, and radiotherapy.

The application of AI has the potential to substantially improve efficiency, quality, and equitable access to healthcare services. AI applications leveraging big data analytics, deep learning, natural language processing, and robotics are improving diagnostic and treatment capabilities. These technologies are rapidly unlocking new possibilities in drug discovery and personalized medicine. On the other hand, the adoption of AI can also support the health system transition to a new paradigm of proactive health maintenance and management instead of reactive disease treatment, enabling accelerated progress towards universal health coverage.

Three trends are important underlying the AI applications developed for the health sector. First, advances in image processing and pattern recognition allow medical equipment to become "smart", boosted by AI's ability to generate diagnoses at the point of care. These have been applied in many clinical fields including cervical and other types of cancer screening, radiological imaging, ophthalmology, dermatology, and high-resolution microscopy. Such clinical decision support would allow a rural primary care clinic lacking pathologists or other experienced specialists to perform the essential screening or initial diagnosis accurately. In more advanced clinical settings, AI can greatly enhance the accuracy of certain treatments including robotic surgery, endoscopy, and radiotherapy.

Second, the text, image, and language processing capacities of generative AI can help automate a substantial part of the administrative tasks for health professionals, saving time for patient care and improving the efficiency and quality of doctor-patient interaction, and preventing burnout that is common among health professionals in both primary care and hospitals. Similarly, significant efficiency gains can be expected by applying AI to health insurance management which results in reduced fraud and improved claim efficiency.

Third, AI can analyze longitudinal multi-modal multi-dimensional data to generate accurate projections, which can be applied to health management and disease prevention. Data from individuals' wearable health devices and medical history can be mined to provide valuable information about propensity and timing of certain diseases, facilitating preventive measures. Such AI applications can support health insurance in better projecting health risks of beneficiaries, improve pricing efficiency, and facilitate targeted health promotion.

AI is becoming increasingly mainstreamed while the application costs are continuously dropping. AI applications built for low-resource settings are already available and applied, with the People's Republic of China (PRC) as a leading country in this realm. Building on prior investment in digital health, other ADB DMCs could take advantage of AI to enhance service excellence in healthcare. This TA will provide timely support for AI adoption in the health sector by surveying and assessing AI applications and best-use cases from PRC and other countries, strengthening the understanding and readiness of DMCs to utilize AI tools in healthcare through knowledge sharing, capacity building, and even piloting.

To address a key barrier for AI adoption - namely, the absence of robust governance mechanisms and regulatory frameworks to mitigate AI-related risks such as data privacy and cybersecurity - the TA will provide targeted technical support. This support aims to strengthen institutional capacity and build confidence in managing AI within the broader context of public digital infrastructure.

Women and girls risk being left behind in benefiting from health AI due to gender-biased use-case design, limited digital access and lower digital literacy, and underrepresentation in AI leadership roles. Addressing these issues is essential to ensure that AI adoption in health systems is inclusive, equitable, and evidence-based.

The TA is aligned with ADB's mid-term review of Strategy 2030 and the Strategy 2030 Health Sector Directional Guide. It will support achieving indicators 2, 3, 4 and 11 of the 2025-2030 Corporate Results Framework.

The TA financing amount is $950,000, which will be financed on a grant basis by People's Republic of China Poverty Reduction and Regional Cooperation Fund.

No contacts available at the time of disclosure.

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