Access and Reimbursement for Artificial Intelligence in Radiology in Mongolia

INTRODUCTION TO RADIOLOGY SERVICES IN MONGOLIA

Mongolia is a vast landlocked country in Central Asia bordered by Russia and China, with a total area of over 1.5 million square kilometers and a population of approximately 3.5 million. Radiology was first introduced in 1934 with X-ray equipment from the Union of Soviet Socialist Republics (USSR). Until the 1960s, Mongolian radiologists and radiographers were primarily trained in the Soviet Union, with Soviet consultants supporting clinical services. Following Mongolia’s transition to democracy in 1992, the Mongolian National University of Medical Sciences (MNUMS) established the country’s first and only academic radiology department, and in 1995, the Mongolian Society of Radiology was founded to promote education and research in this field [1].

Despite Mongolia’s unique challenges stemming from its vast but sparsely populated territories and unequal distribution of infrastructure, the rapid expansion of radiological services and the subsequent workload increase have resulted in a shortage of qualified radiologists and radiographers in Mongolia. As of 2024, Mongolia’s healthcare sector comprises 69,700 workers providing medical services, including 1.7 radiologists per 10,000 people nationwide [2]. Despite annual increases in radiological facilities and radiology service demand, 1.7 radiologists per 10,000 population nationwide is insufficient compared to other countries and the recommended ratio per modality [3]. Under the Ministry of Health’s initiative, the Center for Health Promotion and MNUMS have expanded the training capacity, raising radiology residency admissions to 80 in 2024 and radiologic technology enrollments from 30 in 2018 to 90 by 2025.

There were 756 registered radiologists in 2024 for a ratio of 1.25 radiologists per 10,000 people nationwide, with differences between provinces (0.38 radiologists per 10,000) and city centers (0.87 radiologists per 10,000), revealing disparities in radiology service coverage in urban and rural areas [2,4].

INTEGRATION OF IMPORTED AI SOLUTIONS INTO RADIOLOGY PRACTICES IN MONGOLIA

Although Mongolia is not yet a developer of artificial intelligence (AI) technologies for healthcare, it has actively adopted imported AI solutions to improve service efficiency and accessibility. Recognizing the potential of AI to strengthen healthcare delivery, particularly in radiology, the country began piloting AI tools in 2017, starting with AI-supported laboratory diagnostics for sexually transmitted diseases and cervical cancer detection using Pap smear tests. The first wave of AI adoption in radiology aimed to address workforce shortages by improving workflow efficiency, image analysis, and interpretation. The MNUMS Mongolia–Japan Teaching Hospital and Intermed Hospital were among the first hospitals to apply AI to chest radiography and CT evaluations [5]. During the COVID-19 pandemic, the Mongolian Society of Radiology launched a national AI-based tuberculosis screening and pneumonia and COVID-19 diagnosis project. Following early success, more commercial AI tools were introduced, including Lunit AI (2022) and Deep C (2023), to support radiologists in detecting anomalies and reducing workloads. Private hospitals led adoption on a larger scale, with the Nura Early Detection Center using Fuji AI for CT lung and heart screening, and Brilliant Hospital and the JCI-accredited Intermed Hospital implementing SWIFT MRI in 2024. Despite progress, AI in healthcare remains unregulated. The Medicine and Medical Devices Regulatory Authority oversees pharmaceutical policies, but no formal policies address the development, application, ethics, or data management of AI in the healthcare sector.

NATIONAL STRATEGY FOR AI INTEGRATION INTO RADIOLOGICAL SERVICES

In 2020, the Parliament of Mongolia adopted Resolution No. 52, endorsing the country’s long-term development policy “Vision-2050.” This policy prioritizes the integration of cutting-edge technologies, modern evidence-based diagnostic tools, and treatment techniques, with expansion of e-health services and the creation of a national health database. To advance this vision, the Ministry of Digital Development, Innovation, and Communications, in collaboration with the United Nations Development Program (UNDP), conducted a national assessment of AI readiness, and developed a National AI Strategy. This strategy was informed by contributions from both public and private sectors, scientists from the Mongolian Academy of Sciences, university experts, and young Mongolian professionals working in AI abroad. In 2024, the Ministry of Health took further steps to address key healthcare challenges, including rising costs, system inefficiencies, and the need for higher-quality care, by establishing a National AI Committee to support teleradiology services for regional and provincial hospitals. The Ministry also launched the first State Teleradiology Consultation Center at the Health Promotion Agency, marking a significant milestone in the national integration of AI into healthcare delivery [6].

HEALTH FINANCING AND REIMBURSEMENT FOR DIGITAL TECHNOLOGY USE

The Mongolian health sector is financed through the state budget, health insurance funds, out-of-pocket payments, international aid, and loans. The Health Insurance General Office (HIGO) acts as the single purchaser and manages a unified fund that reimburses both public and private healthcare providers. Primary care is reimbursed through capitation, whereas hospitals are paid using case-based diagnostic-related group payments, promoting efficiency and reducing unnecessary hospitalization. Reimbursements also extend to telemedicine services, which cover a wide range of conditions, including cardiovascular, respiratory, and metabolic diseases, under designated ICD codes. The introduction of 50,000 MNT (approximately 15 USD) in telemedicine services marked the first government-supported reimbursement for digital health technologies. However, AI in healthcare remains unregulated, with implementation costs fully borne by healthcare organizations rather than covered by public financing or insurance schemes.

CONCLUSION

Mongolian radiology is a rapidly growing professional community, where emerging AI experiments and the development and improvement of infrastructure have enabled efficient adoption of new technologies. While the national strategy emphasizes the use of advanced technologies and “big data” to enhance healthcare quality, Mongolian radiology has shown strong interest, and significant potential to become an AI developer. Integration of AI technologies offers promise to mitigate the shortage of radiologists, enhance diagnostic accuracy, and expand access to quality imaging across urban and rural regions. However, sustainable progress will depend on the establishment of clear regulations, robust data governance, and effective reimbursement frameworks for digital health tools.