Hanwang Technology's Liu Qiutong: In the era of AI-driven transformation, the opportunities for health management are unprecedented

Beijing News, Shell Finance (Reporter Chen Weicheng) - On March 26, at the 2026 Zhongguancun Forum Annual Conference “Medical AI Innovation and Development Conference,” Liu Qiutong, Vice President of Hanwang Technology and General Manager of Hanwang Health, mentioned that we are in an era of transformation driven by AI, and health management, especially the global challenge of hypertension, has also welcomed unprecedented opportunities.

Hanwang Technology started with handwriting recognition, OCR, and other technologies, and now applies AI technology in the medical health field. What considerations are behind the innovative development of the AI Korotkoff Sound Intelligent Blood Pressure Monitor?

Liu Qiutong told reporters that Hanwang deeply cultivates pattern recognition and AI algorithms. Whether it is handwriting recognition, OCR, or now Korotkoff sound recognition, they are essentially “signal acquisition + feature recognition + intelligent judgment.” The core capabilities are interconnected, only implemented in different application scenarios. In the medical field, our advantage lies in processing weak signals and pursuing accuracy through our core AI algorithm engine.

According to the Minamata Convention, the production of mercury-containing blood pressure monitors will be completely banned starting January 1, 2026. How do you view the industry’s transformation with traditional mercury sphygmomanometers exiting the historical stage?

Liu Qiutong stated that this is an inevitable industry transformation and also an important opportunity for upgrading. After traditional mercury blood pressure monitors exit the market, there is a need for a reliable alternative. The Hanwang Korotkoff Sound Blood Pressure Monitor plays the role of a “precise substitute.”

How do mainstream oscillometric electronic blood pressure monitors perform? In Liu Qiutong’s view, their core principle is based on ‘estimation’ rather than ‘measurement,’ inferring blood pressure through oscillation waves, which determines their inherent shortcomings. The International Organization of Legal Metrology clearly defines it as an indirect measurement method, and research shows that compared to mercury blood pressure monitors, the proportion of data with an error greater than 5mmHg is as high as 69%. In addition, different manufacturers have varying algorithms, making results difficult to communicate, and their adaptability to special populations such as those with arrhythmias is poor. These constitute the precision bottleneck of oscillometric methods.

Liu Qiutong believes that blood pressure health management in the AI era has three core elements: first, precise measurement; second, big data interconnectivity, with all data uploaded to the cloud and AI assistants making health management smarter; finally, doctor-patient collaboration, connecting families and hospitals throughout the entire process, enabling seamless integration of home measurements and professional diagnoses.

“AI algorithms need hardware support.” Liu Qiutong explained that the company’s Korotkoff Sound Blood Pressure Monitor has achieved dozens or even thousands of times improvement in core hardware such as processors and memory compared to traditional oscillometric blood pressure monitors, providing a solid computational power foundation for real-time inference of AI models on the device.

Liu Qiutong stated that they will develop dedicated AI chips to reduce costs, allowing precise measurements to benefit more people, and plan to promote the gradual replacement of traditional oscillometric products with AI Korotkoff Sound Blood Pressure Monitors within 3-5 years. At the same time, they will open standardized interfaces to build a smart health ecosystem and promote the Korotkoff Sound AI method to become the new industry standard, reducing the ISO standard deviation of blood pressure measurements from 5mmHg to below 3mmHg.

Editor Chen Li Proofreader Liu Baoqing