The number of users entitled to Ping An Healthcare’s family doctor services exceeded 35 million in the first half of 2025, while home-based senior care beneficiaries increased 83%, indicating growth in the company’s service coverage.
Ping An Healthcare and Technology Company Limited reported interim results for the six months ending June 30, 2025, on Aug. 19. Following its return to profitability in 2024, the company maintained growth and improved profitability during the reporting period, supported by core business performance and developments in medical AI applications.
Revenue for the period reached RMB2.5 billion, up 19.5% from the same period last year. Net profit attributable to shareholders increased to RMB134 million, representing a 136.8% year-on-year gain. Adjusted net profit totaled RMB165 million, an 83.6% increase. The company said AI deployment and optimization of its business mix contributed to a gross profit margin of 33.6%.
Revenue from the integrated finance business, F-end, and corporate health management services for B-end clients rose 30.2% year-on-year. Family doctor services and senior care concierge services improved service efficiency and standards, contributing to the increase in members.
Last July, Ping An Insurance (Group) Company of China Ltd named Ray Wang as chief technology officer and general manager of Ping An Technology. The company said Wang’s appointment is intended to strengthen its capabilities in artificial intelligence and accelerate the integration of proprietary large language models and open-source big data platforms.
The company also reported developments in AI-driven healthcare services. Using its “data + models + scenario” approach, Ping An Healthcare enhanced its large multi-modal model, Ping An Medical Master, leveraging six major medical databases and 1.44 billion online consultation records. The company optimized five vertical models for major medical scenarios and introduced the “7+N+1” AI medical product matrix during the reporting period.
AI-assisted inquiry and consultation achieved an accuracy rate of approximately 98%, while multidisciplinary team complex disease treatment accuracy reached nearly 80%. AI handled up to 4.0 million consultation requests per day and reduced average service costs per family doctor by about 52% year-on-year. Process reengineering and optimization supported by AI improved middle-office operational efficiency by roughly 50% compared with the prior year.
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