As China ramps up efforts to apply artificial intelligence across a broad range of industries, healthcare is emerging as one of the most promising frontiers, with experts saying that the technology could help make medical services smarter, more efficient and more accessible while narrowing gaps in medical resources.

In the healthcare sector, they said, AI is creating new opportunities to improve efficiency, standardize diagnosis and treatment and strengthen service capacity at the primary level, where shortages in diagnostic capability have for long posed a key challenge.

This year's Government Work Report called for deeper implementation of the AI Plus initiative, faster rollout of next-generation intelligent terminals and AI agents, and greater efforts to promote the large-scale commercial application of AI in key sectors and fields.

The momentum was evident at the recent 2026 China International Medical Equipment Fair, where Wandong Medical, a Chinese medical imaging equipment maker, unveiled the industry's first full-process AI agent for digital radiography.

Ye Hongnan, product manager for DR at Wandong Medical, said the intelligent agent integrates AI across the entire examination process, from imaging to diagnosis, and is able to accurately detect more than 20 common chest conditions, including tuberculosis, pneumothorax and fractures.

Using 3D cameras and AI-based algorithms, the agent helps technicians improve positioning accuracy and choose personalized exposure settings for different patients, Ye said, producing images that are "more in line with diagnostic needs".

Beyond image acquisition, the agent uses a large model developed by Midea's Corporate Research Center and trained on more than 4 million DR chest images collected from diverse devices and regions, enabling it to identify common chest diseases and automatically generate standardized, structured reports, Ye said.

Guo Hongyu, director of the intelligent imaging institute at Midea Medical Devices Academy, said such intelligent agents represent a shift from fragmented smart tools designed for only individual stages of the workflow to integrated systems that can connect the entire process and continuously evolve through self-learning.

Guo said the agent's self-learning capability stems from its ability to support real-time interaction with doctors, allowing them to question, verify and refine AI-generated judgments throughout the diagnostic process.

If a doctor challenges the AI's reading, for instance, the agent can explain why it identified a lesion as a nodule rather than inflammation, enabling the doctor to review the evidence and reach a final diagnosis.

Such advances are seen as especially relevant for primary-level healthcare, where diagnostic capacity has been relatively limited, experts said. Marked disparities also remain among medical institutions across regions and tiers in terms of service capability.

Huang Jiaxiang, general manager of WanLiCloud Healthcare IT Co, an AI imaging innovation platform under Wandong Medical, said the company is working to extend such solutions to primary-level medical institutions through cloud-based imaging services.

"The DR AI-Agent is designed to address a key pain point for primary-level medical institutions — limited diagnostic capacity — by enabling grassroots doctors to produce high-quality diagnostic reports with AI support," Huang said.

Policy support has also gathered pace. A guideline issued in November by the National Health Commission and four other government bodies made primary care one of the key priorities of China's AI Plus Healthcare drive, calling for wider use of AI in diagnosis, imaging and patient services.

By 2030, the country aims to make AI-assisted diagnosis widely available in primary care and AI applications routine in medical imaging analysis and clinical decision support at secondary and higher-level hospitals, according to the guideline.

Looking ahead, Wang Zhenchang, an academician of Chinese Academy of Engineering who works at Beijing Friendship Hospital, said efforts should focus on moving beyond single-disease AI products and accelerating the development of smart imaging systems that cover a full range of conditions and are embedded in clinical workflows.