AI Empowers Healthcare in China: Large-Scale Application Transforms Clinical Services

At the 91st China International Medical Equipment Expo, staff demonstrated a fully flexible micro-endoscopic surgical robot, showcasing the latest achievements of AI integration in medical equipment. AI technology is evolving rapidly and has been widely applied in medical imaging, auxiliary diagnosis and personalized treatment recommendation, marking that the application of AI in modern medicine has entered a stage of large-scale implementation.

In the intensive care unit of a Shenzhen hospital, doctors use an AI large model to complete the retrospective integration of the entire diagnosis and treatment process in 5 seconds and generate a structured medical record in 1 minute, accelerating clinical treatment. In a Xi’an hospital, AI has reduced the time for aortic dissection imaging diagnosis from 15-20 minutes to 3 minutes, gaining valuable time for life-saving. In lung nodule screening, AI helps radiologists reduce their workload by 30% to 50%, increasing the overall efficiency of imaging diagnosis by 30% and shortening the average waiting time for patients by 42%.

AI Serves as a Reliable Assistant for Medical Staff

In medical scenarios, AI operates not independently but as an "assistant" to medical staff, forming a new human-machine relay model. After patients complete imaging examinations, AI-assisted imaging diagnosis systems can quickly analyze and automatically complete preliminary screening. For example, in the detection of early breast cancer and occult rib fractures, the system marks suspicious lesions within seconds, provides quantitative indicators such as size, shape and density, and generates structured initial reports. Doctors review AI results combined with clinical experience to rule out false positive lesions and make final diagnoses.

In clinical decision-making, AI large models integrated into doctor workstations can quickly extract key abnormal indicators from test report screenshots, generate prompts including "result overview + possible diagnosis + supplementary examinations" based on clinical guidelines, and propose multiple treatment options such as drug selection and precautions. Doctors can make personalized adjustments based on these suggestions to form the final plan.

AI also optimizes surgical planning. Before surgery, surgeons import patients’ CT data into AI surgical planning systems to generate 3D models of the lungs, clearly showing the exact location of tumors, their infiltration range and spatial relationship with surrounding tissues. During surgery, the 3D model serves as real-time navigation to help doctors monitor the surgical progress, enabling "sub-lobar resection" that maximizes the preservation of healthy lung tissue by avoiding important organs and blood vessels.

In addition, AI can identify millimeter-level micro-nodules that are difficult for the human eye to detect, improving screening sensitivity and enabling some patients to receive diagnosis and treatment locally without traveling to large cities. AI surgical robots have also been introduced to assist complex spinal surgeries, achieving precise positioning and minimally invasive operations, bringing better treatment effects to patients, including elderly and fracture patients.

AI has also made new progress in traditional Chinese medicine (TCM). A TCM AI large model, jointly developed by a research institute and a technology enterprise, integrates more than 1,500 classic works, over 100,000 clinical cases and more than 100 guidelines, covering application scenarios such as TCM clinical diagnosis and treatment, health maintenance, TCM education and traditional Chinese medicine R&D. This model ranks first in the MedBench TCM large model list and can realize online consultation, intelligent guidance and chronic disease management.

It should be noted that for rare diseases or cases with multiple overlapping diseases, the matching degree of AI suggestions is still not high. At this stage, the main value of AI lies more in "speeding up diagnosis and reducing missed diagnoses" rather than surpassing human judgment in all scenarios.

Expanding Full-Chain Application Scenarios

Globally, AI healthcare is in a growth period of intensive technology research and scenario differentiation, with China’s AI medical application entering a large-scale landing phase. From a clinical perspective, the maturity of AI medical applications presents a pattern of "leading diagnosis and treatment, advancing prevention, and initial rehabilitation".

Policy support has laid a solid foundation for the standardized development of AI healthcare. In November 2024, three national departments jointly issued the "Reference Guidelines for Artificial Intelligence Application Scenarios in the Health Industry", listing 84 typical application scenarios and clarifying the auxiliary status of AI, filling the gap in industry application standards. In November 2025, five departments jointly issued an implementation opinion, proposing that by 2030, intelligent auxiliary applications for primary diagnosis and treatment will basically achieve full coverage, secondary and above hospitals will generally carry out AI applications such as medical imaging auxiliary diagnosis and clinical decision support, and a sound standard system for "AI + healthcare" applications will be established.

China’s AI healthcare is in a policy-driven large-scale landing period. Technically, it is transitioning from "technology pilot" to "large-scale implementation". Data shows that with the support of policies and county-level medical community construction, AI tools have quickly covered primary medical care and public health scenarios, with 80% of counties (cities, districts) initially establishing resource sharing centers for medical imaging, electrocardiography and laboratory tests. In 2025, the volume of county-level remote medical imaging diagnosis services exceeded 68 million person-times, and AI has become an important support for primary medical services.

In terms of scenario application, AI healthcare is extending from "single scenario" to "full chain". Previously focused on single links such as imaging initial screening, its application has gradually expanded to the entire chain of "prevention - diagnosis and treatment - rehabilitation - health management", forming a full-process service closed loop. At the same time, multi-modal AI technology is developing rapidly, integrating multi-source data such as images, texts and genomes, promoting the transformation of AI from an "auxiliary tool" to an "intelligent partner" .

Driving Comprehensive Industry Upgrade

The acceptance of AI tools among front-line medical staff is steadily improving. A two-year survey by the American Medical Association from 2023 to 2024 shows that the proportion of doctors using AI has increased significantly, and their trust in AI has gradually improved, with core needs focusing on clinical decision support and administrative work burden reduction, reflecting a positive shift in global medical staff’s acceptance of AI.

AI has always been a tool to serve medical practitioners rather than a substitute. It can optimize resource allocation and play a role in promoting medical research, accelerating drug development and exploring disease mechanisms. However, in the TCM field, due to the difficulty of AI in understanding TCM philosophical concepts, some TCM practitioners still hold reservations about AI applications, requiring further training and promotion to improve medical staff’s trust and ability in using new technologies.

Regarding concerns that AI will replace some jobs, AI will replace some repetitive and standardized positions and functions in the medical field, but not the core functions of doctors such as clinical decision-making, practical operations and humanistic care. On the contrary, it will reshape the medical post structure and spawn a large number of new technology-enhanced and human-machine collaborative medical positions.

In the long run, AI will promote the optimization of medical positions, the innovation of medical models and the comprehensive upgrading of public health life. With continuous technological iteration, AI healthcare will evolve from a simple auxiliary tool to a core enabling engine of the industry, deriving diverse medical models and formats and promoting the transformation of medical services from single-point support to full-process and multi-dimensional empowerment.