This article was automatically translated by AI.
I. Overview
In our CEC Capital China Healthcare Industry White Paper 2024, released last year, we estimated that as China fully emerged from the COVID-19 pandemic—while accounting for domestic volume-based procurement (VBP) policies and ongoing healthcare anti-corruption efforts—the country’s healthcare industry reached an estimated market size of approximately RMB13.3 trillion (around USD 1.8 trillion) in 2023. This included roughly RMB3 trillion in pharmaceutical-related markets, RMB1.2 trillion across medical devices and diagnostics, and approximately RMB9.1 trillion encompassing healthcare services, digital health, and wellness sectors. After adjusting for overlapping segments, we projected the total market size of China’s healthcare industry in 2023 to be between RMB10.5 trillion and RMB11.0 trillion (USD 1.4–1.5 trillion).
In 2024, with China’s economy generally stabilizing and the healthcare industry continuing to expand in scope, we estimate the market size of China’s healthcare sector as follows: the pharmaceutical-related market remains steady at approximately RMB 3 trillion, similar to 2023; the medical devices and diagnostics segment is projected to grow modestly, reaching around RMB 1.4 trillion. Driven by China’s rapidly aging population and the widening industry boundaries enabled by emerging technologies—such as artificial intelligence, robotics, and biomanufacturing—we forecast the combined market size of healthcare services, digital health, and broader health-related sectors to reach RMB 10 trillion. After adjusting for overlapping components, we estimate the total size of China’s healthcare industry in 2024 to range between RMB 11.5 trillion and RMB 12.0 trillion (USD 1.5–1.6 trillion), reflecting a year-on-year growth rate of 9% to 10%.
Rapid population aging is a global phenomenon, and China is experiencing one of the most rapid and large-scale demographic shifts in history — a trend expected to accelerate significantly over the next two decades. This demographic transition presents both systemic challenges to China’s socioeconomic fabric and transformative opportunities for industrial growth. The aging population is placing unprecedented strain on China’s healthcare infrastructure, while simultaneously driving the rise of a substantial elderly care economy with significant growth potential. According to projections from the China Research Center on Aging, the elderly care economy is expected to reach RMB 30 trillion by 2035, accounting for roughly 10% of GDP, with health-related technological innovation and evolving consumer demand emerging as key engines of economic growth. We anticipate that technologies such as artificial intelligence, embodied robotics, and brain-computer interfaces (BCI) — once confined to the realm of science fiction — will rapidly evolve into practical solutions for eldercare, offering innovative approaches for Chinese society to meet the challenges of an aging population.
The reform and opening-up initiative launched in the late 1970s transformed China into a globally competitive manufacturing powerhouse over the past four decades. Today, China is evolving beyond its role as a manufacturing leader to become a global hub for technological innovation, product R&D, and the export of intellectual property (IP). Chinese innovation is gaining international prominence across sectors — from new energy vehicles represented by BYD, and semiconductor manufacturing led by Huawei, to artificial intelligence exemplified by DeepSeek, and cultural IP showcased by Black Myth: Wukong and Nezha.
China’s growing innovation capabilities, R&D strength, and expanding IP exports are playing an increasingly important role in advancing global healthcare. China's healthcare innovation is being propelled by increasing demand and rapid acceleration of technological development. This dynamic not only helps address global disparities in medical resource allocation but also accelerates China’s transformation from the “world’s factory” into a “global laboratory” and an “innovation epicenter.”
In 1994, Professor William Kissick of Yale University introduced the Iron Triangle of Healthcare, a renowned framework that describes the inherent tension within traditional healthcare systems: high quality, low cost, and broad accessibility are universally desired, yet only two of these objectives can typically be achieved simultaneously. Throughout human history, quality healthcare has never been an equal-access resource — it remains a scarce asset, and at times, a privilege that wealth alone cannot fully secure.
One of the most pressing challenges of China’s rapidly aging society is precisely this scarcity of medical resources. In cities with dense elderly populations, healthcare demand is surging, while medical expertise and resources remain disproportionately concentrated in first-tier urban centers, resulting in severe distribution imbalances.
Today, the accelerating progress of artificial intelligence (AI) and robotics — particularly embodied robotics — offers a pathway to partially alleviate the constraints of the Iron Triangle. These technologies hold vast potential across numerous healthcare applications, including AI-assisted diagnosis, medical imaging interpretation, surgical planning and execution, chronic disease management, health monitoring, new drug development, postoperative rehabilitation, and elderly care.
While AI and robotics will likely never fully replace the expertise of skilled physicians and specialists, technological advancements are steadily propelling Chinese society toward the long-term goal of healthcare equity. The road ahead remains challenging — but the first light of that future is already visible.
Bio-manufacturing and synthetic biology, much like artificial intelligence, exhibits foundational infrastructural characteristics and is emerging as a core driver in the transformation of traditional industries. Over the next 5 to 10 years, it is expected to fundamentally reshape global industrial ecosystems across biopharmaceuticals, food production, and agriculture.
Projections indicate that the sector’s market size will expand from RMB 417.6 billion in 2023 to RMB 476.2 billion in 2024, representing year-on-year growth of over 15%. By 2030, China’s bio-manufacturing market is projected to reach approximately RMB 1.8 trillion, while the global market is forecast to surpass USD 1.1 trillion (around RMB 8 trillion), with China accounting for nearly 23% of global capacity.
Our analysis suggests that China’s growing technological leadership and strong industrial base are enabling its transition from participant and contributor to catalyst and global leader in bio-manufacturing. The country is well-positioned to become a central hub in global bio-production, extending its long-standing industrial manufacturing strengths into this strategically critical sector.
In 2024, China’s healthcare industry recorded approximately 148 M&A transactions totaling RMB 75 billion, marking a clear rebound from the downturn in 2023. The total deal value approached the 2021 peak, making 2024 the second-highest transaction year since 2019. The normalization of M&A activity in China’s healthcare sector represents an important milestone in both industry development and capital market maturation, ushering in a new era of consolidation.
A-share listed companies remain the dominant force in China’s M&A market, leveraging capital strength and industrial synergies to lead in deal-making. The strategic rationale behind M&A has evolved from a focus on market capitalization management toward deeper vertical integration. The ongoing capital investment winter has forced more rational valuations, bringing high-quality assets to market at more attractive price points. The adoption of innovative deal structures has further enhanced transaction viability, significantly accelerating deal momentum on both the buy and sell sides.
We believe that in this environment, a clearly articulated M&A strategy, strong execution capabilities, and disciplined post-merger integration are critical competencies that will enable leading players to navigate market cycles and reshape the competitive landscape.
II. The Most Significant Investment Themes in China's Healthcare Industry Today
1. Population aging is a global trend, and is particularly acute in China. The country is undergoing the world’s largest and fastest aging process — a demographic shift that will intensify markedly over the next two decades. This transformation presents both significant challenges and new opportunities for societal evolution.
1) China’s aging population is expanding at an exceptionally rapid rate both in scale and pace, placing sustained pressure on social security and elderly care systems.
China’s elderly population has reached an unprecedented scale. As of 2024, there are 310 million people aged 60 and above (22% of the total population), including 220 million aged 65 and older (15.6%), signifying the country’s transition into moderate aging. Over the next decade (2025–2035), the 60+ population is projected to grow by more than 10 million annually, surpassing 400 million by 2035 and accounting for over 30% of the total population — moving society into an era of severe aging. By around 2050, China’s elderly population is expected to peak at 487 million, representing one-quarter of the world’s elderly population, with an aging rate of 34% — twice the global average.
The pace of this demographic shift is unprecedented. China’s aging rate is projected to rise from 10% in 2000 to over 20% by 2025 and surpass 30% by 2035 — completing the transition from mild to severe aging in just 35 years, far faster than in Western economies. At the same time, the working-age population (ages 20–64) is projected to shrink by 47 million by 2035 and by 124 million by 2050, exacerbating structural imbalances between labor supply and pension demand.
Severe aging brings multiple systemic challenges: the elderly dependency ratio continues to rise, pension funding gaps are widening, and demand for healthcare and long-term care is surging. For example, the potential workforce required for disabled elderly care could reach 74 million by 2050 — representing 11% of the working-age population. Stark urban-rural disparities persist: rural regions are aging faster than urban areas but lack adequate elderly care infrastructure.
At the same time, declining birth rates and demographic imbalances remain unresolved. In 2024, China recorded 9.54 million births — an increase of 520,000 from 9.02 million in 2023 — ending a seven-year consecutive decline since 2016. However, the birth rate remains low at just 6.77‰, and overall population growth remains negative. The dual challenges of aging and low fertility rates continue to strain family support structures and intensify intergenerational pressures.
2) Population aging is placing unprecedented pressure on China’s healthcare and medical services industry, while simultaneously creating significant new opportunities. Both supply-side players and payer stakeholders are rapidly adapting to this evolving landscape. The elderly care economy is driving substantial market expansion and has become a key focus of broader socioeconomic development.
l Aging populations present the clearest growth opportunities in specialized healthcare segments — particularly in medical nutrition, chronic disease management, rehabilitation, and long-term care for disabled patients.
Demand for food for special medical purposes (FSMP) is growing exponentially, fueled by demographic shifts and changing disease patterns. With accelerated aging, China is facing a worsening prevalence of chronic diseases. Data shows that among residents aged 40 and above, the combined morbidity rate for cardiovascular diseases, cancer, and diabetes has reached 34.3%. Moreover, 80% of patients with chronic diseases suffer from nutritional imbalances. Among hospitalized elderly patients, the incidence of malnutrition reaches 65%, with 42% experiencing more than 5% body weight loss during hospitalization — significantly increasing infection risk and prolonging recovery times.
FSMP provides targeted nutritional interventions to address these challenges. Clinical evidence shows that elderly patients receiving standardized FSMP protocols experience 35% slower muscle loss, a 28% reduction in infection rates, and an average hospital stay shortened by 12 days. In diabetes-specific FSMP, formulations incorporating resistant dextrin and dietary fiber reduce postprandial glucose fluctuations by 40%, with this segment projected to exceed RMB 7.5 billion in market size in 2024. In oncology FSMP, formulations enriched with ω-3 fatty acids and arginine have raised weight maintenance rates to 78% — a 26-percentage-point improvement over conventional nutritional support.
China’s FSMP (Food for Special Medical Purposes) market is expanding rapidly, with a compound annual growth rate (CAGR) of 30%. The market is projected to reach RMB 23.42 billion in 2024 and exceed RMB 80 billion by 2030, establishing FSMP as a trillion-yuan growth pillar within the elderly economy. The sector is evolving from a medical adjunct into a core component of routine health management.
Regulatory authorities continue to introduce favorable policies to support industry development. The revised Administrative Measures for Registration of Foods for Special Medical Purposes (2023) shortened the standard approval cycle to 18 months, with priority review products now eligible for accelerated approval within just 30 days. In 2024, 196 domestically produced FSMP products were approved across three key categories: complete nutrition, disease-specific complete nutrition, and non-complete nutrition. Simultaneously, several cities — led by Wuxi — have begun incorporating FSMP into medical insurance coverage, driving faster adoption and industry penetration.
This sector is also attracting increasing attention from investors, with several significant transactions recently emerging. Driven by strong supply and demand dynamics, we recommend focusing on enterprises pursuing breakthrough innovations in three key areas:
l Overcoming technical barriers to demonstrate R&D strength and enable domestic substitution of specialized imported FSMP products.
l Leveraging synthetic biology applications to achieve transformative cost advantages.
l Integrating AI technologies to develop personalized, AI-driven nutrition solutions and establish differentiated market positioning.
At the same time, China’s chronic disease management industry is undergoing a paradigm shift — moving from passive treatment toward proactive care. China currently has over 460 million chronic disease patients, with a prevalence rate of 62.3% among those aged 65 and older. Chronic conditions such as cardiovascular diseases and diabetes account for over 70% of total healthcare expenditures, fueling the growth of a health management market now valued at several hundred billion yuan.
China’s chronic disease health management industry has steadily evolved, with its ecosystem now spanning five key areas: pharmaceutical companies, hospitals, pharmacies, home care, and insurance. Market players are pursuing diverse entry strategies tailored to their respective capabilities and resource advantages. For example, e-commerce platforms such as Ali Health and JD Health leverage their strong distribution channels to expand into healthcare services. Insurance-backed players like Ping An Good Doctor draw on their large policyholder bases, while companies such as Ark Health rely on physician networks to enter the telemedicine and medication procurement space. These differentiated approaches aim to deliver continuous, integrated care — both within and beyond hospital settings — to China’s vast chronic disease patient population. Building a large patient base and creating closed-loop business models across service touchpoints remain key competitive advantages.
We believe the rapid advancement of AI foundation models will further energize this sector, creating new opportunities in internet-based health management and enabling more proactive chronic disease interventions. For example:
l AI-powered dynamic risk prediction systems can analyze large-scale patient datasets to forecast acute complication risks up to six months in advance, helping reduce the incidence of chronic disease complications.
l Personalized behavior modification engines, utilizing reinforcement learning algorithms, can continuously adjust intervention strategies. By analyzing patient dietary and exercise data, these systems generate tailored plans to promote long-term lifestyle optimization for chronic disease patients.
Medical AI foundation models are propelling the chronic disease management industry into a new era of predictive, preventive, personalized, and participatory (4P) medicine. Companies that combine strengths in data, algorithms, and ecosystem integration will be best positioned to lead this transformation.
The rehabilitation healthcare sector has also emerged as a key growth area within China’s precision healthcare transformation, with its inelastic demand continuing to rise. The rehabilitation market has surpassed RMB 700 billion, with geriatric rehabilitation accounting for 58% of this total. Statistics show that 60% of individuals aged 60 and above require rehabilitation services (e.g., post-stroke, post-fracture surgery, cognitive impairment), with potential service volume exceeding 145 million patients in 2024.
Key clinical scenarios include:
l Neurological Rehabilitation: Stroke patients have a critical treatment window within 3–6 months post-onset, yet only 38% received systematic rehabilitation in 2024.
l Post-Surgical Rehabilitation: Demand among knee replacement patients is highly concentrated within the first three months post-operation, while public hospital rehabilitation beds have an average waiting period of 4.2 weeks.
l Geriatric Syndrome Management: 72% of functionally impaired seniors require combined physical therapy and cognitive training, yet current service coverage remains below 20%.
China’s rehabilitation medical institutions are predominantly privately operated, yet the sector faces severe supply shortages. As of 2024, there were only 823 rehabilitation hospitals nationwide (152 public and 671 private), translating to just 6.1 facilities per 10 million people — well below the OECD average of 15.3. Rehabilitation beds account for only 1.8% of total hospital beds (327,000 beds in 2024), compared to 12% in the United States.
Government policy explicitly encourages the development of chain-based private rehabilitation hospitals. The 14th Five-Year Plan for Digital Economy Development calls for "supporting non-governmental entities in establishing chain-based and group-based rehabilitation medical institutions, and promoting the extension of rehabilitation services to communities and households." In parallel, the Opinions on Promoting the Sustainable, Healthy and Standardized Development of Non-Public Medical Institutions allows private rehabilitation hospitals to exceed regional bed quotas, with chain operators eligible for a "one-license, multiple-location" registration system. A range of construction subsidies, tax incentives, and other supportive measures are also in place to encourage private sector participation.
Against this backdrop, we maintain a positive outlook for the development of private rehabilitation medical institutions over the next 3–5 years, with particular attention to operators demonstrating experienced operational and management capabilities. At the same time, demand for intelligent rehabilitation hardware and assistive devices continues to expand. The integration of emerging technologies — including VR/AR systems, exoskeleton robotic platforms, and 3D printing — with AI-driven algorithms is propelling technological advancements in the rehabilitation equipment sector.
Disability care services have become a critical frontier for professionalization and standardization. China currently has 45 million elderly individuals with disabilities, of whom 32% are severely disabled. The average care duration exceeds 7.3 years, with annual nursing costs surpassing RMB 50,000 — fueling explosive demand as the population continues to age rapidly. However, the disability care sector faces a pronounced supply-demand gap. International benchmarks recommend a nursing bed ratio of 1:3 (one bed per three disabled elderly), which implies that China should have 8–10 million professional nursing beds when accounting for additional demand from dementia, post-operative rehabilitation, and related conditions. Yet, as of 2024, China’s elderly care institutions provided only about 3.2 million nursing beds — leaving a shortfall of over 6 million beds.
Against this backdrop, the government is actively promoting private sector investment in integrated eldercare and medical services. The National Health Commission has adopted a multi-pronged approach to enhance administrative efficiency, including streamlining approval processes through record-filing management and consolidating establishment approvals with operational registrations. In terms of facility development, the Commission has removed regional quota restrictions for privately operated eldercare medical institutions and accelerated the inclusion of qualified medical facilities within retirement communities into the designated medical insurance network. Local governments are also encouraged to establish industrial investment funds to further support sector growth.
The accelerating aging trend is transforming long-term care insurance and disability care into a trillion-yuan market. Structural inefficiencies in the public system — spanning service quality, technological advancement, and payment flexibility — are creating significant opportunities for private operators. Leading enterprises with strong chain operation capabilities, intelligent service platforms, and integrated value chain competencies are projected to capture more than 70% of market share by 2030.
l China’s social health insurance, the primary payer, is under mounting pressure amid demographic aging, spurring the rise of diversified alternative payment mechanisms such as private pension insurance and commercial pension insurance to strengthen the overall payment system.
China’s healthcare insurance fund is facing growing challenges in maintaining fiscal sustainability and has now entered a critical inflection period marked by “declining contributions and rising expenditures.” While the national employee medical insurance fund remains in surplus (RMB 416.4 billion as of 2024), the resident medical insurance system is under increasing strain. For example, in 2024 Tianjin’s urban-rural resident medical insurance fund posted contributions of RMB 5.88 billion versus expenditures of RMB 7.24 billion, resulting in a deficit of RMB 1.36 billion. Similarly, Beijing’s medical insurance program recorded an annual funding gap of RMB 526 million.
The underlying cause of this imbalance is demographic in nature: individuals aged 65 and older now account for 13.5% of the population, with per capita medical expenditures 3-5 times higher than those of younger age groups. Under China’s pay-as-you-go system, a shrinking contributor base combined with a growing pool of elderly beneficiaries is creating severe fiscal challenges for the health insurance system.
The national rollout of personal pension insurance will be completed by December 2024, marking the formal establishment of a third pillar in China’s pension system to complement basic medical insurance and enterprise annuities. However, current resident contribution patterns indicate limited participation enthusiasm. By the end of 2024, approximately 70 million personal pension accounts had been opened, representing 42% of urban employee basic pension insurance participants, with actual contributors accounting for less than 30% of account holders. Two primary factors have contributed to this phenomenon: first, current tax incentives exclude individuals with annual incomes below RMB 96,000, thereby dampening participation among middle- to low-income groups; second, as closed-end investment accounts, these products lack competitive yield advantages compared to alternative wealth management instruments, fostering wait-and-see attitudes among potential contributors. Potential policy relaxations regarding tax incentives and early withdrawal mechanisms are anticipated to enhance the product’s overall appeal.
Concurrently, the commercial pension insurance market continues to expand, supported by active government promotion and multiple policy incentives. Regions such as Shanghai and Shandong have successively implemented "medical insurance + commercial insurance" one-stop settlement systems, covering over 1,000 public and private hospitals while reducing claims processing cycles to under two hours. The long-term care insurance pilot program is accelerating, with mandatory coverage targets set for 50% of prefecture-level cities nationwide by the end of 2025. This initiative establishes a tripartite funding mechanism involving government, enterprises, and individuals, while enabling insurers to develop supplemental products covering non-policy nursing durations and premium care institutions. At the national healthcare security administration level, efforts are underway to expedite the opening of medical insurance data to commercial insurers. Commercial insurance, as a supplement to public healthcare, is progressively transitioning from singular financial supplementation to comprehensive lifecycle health management.
By 2035, China’s elderly care economy is projected to reach RMB 30 trillion, accounting for approximately 10% of GDP, with health-related consumption poised to become a significant market growth driver.
The period from 2025 to 2035 represents a critical transition window, as China’s aging population moves from moderate to severe levels. In the coming decade, retirees will primarily comprise younger seniors aged 60–74, who generally enjoy better health and possess substantial accumulated wealth. This demographic will serve as the core driver of the "elder economy benefit." Projections indicate that China’s elderly care economy will expand from approximately RMB 9–10 trillion today to RMB 30 trillion by 2035, representing a compound annual growth rate of 12%–15%. This growth will be fueled by policy tailwinds, technological innovation, and increasing consumption— though it will require careful management of regional disparities and varying payment capacities across population segments.
The rise of the elderly care economy has positioned health-related consumption at the center of market attention. In the functional foods segment, specialized categories are experiencing strong demand growth, including:
l products designed for age-related swallowing and chewing difficulties;
l low-sodium and low-GI formulations for chronic disease management;
l cognitive support supplements featuring PS128 and Omega-3;
l probiotic solutions aimed at gut microbiome regulation.
Technology innovation in senior-oriented products continues to accelerate. For example, high-pressure microfluidics now achieves 80% fragmentation of meat fibers, enabling the production of "soft-textured yet nutrient-dense" foods. Nano-encapsulation technologies have tripled the bioavailability of lutein compared to conventional products. The industry has evolved from providing basic nutritional coverage to offering functional enhancements targeting cardiovascular, skeletal, digestive, and cognitive health.
China’s senior population is also driving growth in the wellness tourism sector. Travelers aged 60 and above now account for 28% of total tourism spending, with an average annual travel frequency of 3.2 trips — well above the overall average of 1.8 trips. The domestic wellness tourism market is projected to reach RMB 90 billion in 2024, maintaining a compound annual growth rate exceeding 20%, with forecasts suggesting the market will surpass RMB 160 billion by 2029.
Integrated healthcare and elderly care is emerging as a new investment hotspot. The Boao Lecheng International Medical Tourism Pilot Zone hosted over 260,000 medical tourists between January and August 2024, introducing more than 440 licensed medical devices and pioneering innovative healthcare–eldercare models. A diverse range of players — including tourism operators, listed pharmaceutical companies, and real estate developers — are actively entering this space. Examples include: Takeda Pharmaceuticals’ planned development of traditional Chinese medicine wellness tourism parks; PanLong Pharma’s initiative to build integrated medical-rehabilitation-wellness complexes; and OCT Group’s rollout of comprehensive wellness tourism resorts.
Overall, the consumption wave driven by the elderly care economy remains in a strong growth phase. We maintain a positive outlook on companies that demonstrate robust capabilities in healthcare resource integration, age-friendly service design, and innovative product development, anticipating that such enterprises will achieve superior market performance in the years ahead.
3) A new wave of revolutionary technological advancements—spanning artificial intelligence (AI), brain-computer interfaces (BCI), and embodied robotics—is opening novel pathways for Chinese society and families to address the challenges of an aging population.
Groundbreaking progress in AI is injecting new momentum into elderly care services. A continuous stream of innovative applications is emerging, significantly improving quality of life, strengthening health protection systems, and reshaping care delivery models. These developments are becoming a key driving force behind the development of intelligent elderly care ecosystems.
Through the deep integration of intelligent sensing, big data analytics, and human-machine collaboration, AI now enables comprehensive coverage across the spectrum of elderly care—from basic life support to personalized health management. Moreover, AI is restructuring the traditional value chain of elderly care services through intelligent service networks, delivering an innovative approach that balances efficiency with human-centered care to tackle the challenges of an aging society.
Intelligent hardware devices enable real-time, in-home health monitoring for elderly individuals, supporting multidimensional personalized interventions. By integrating physiological data, behavioral patterns, and genomic information, these systems create precise individual profiles for deeper, data-driven health management.
For example:
l U.S.-based startup VitalConnect has developed wearable smart sensors capable of real-time monitoring of multiple vital signs and biometric parameters, positioning the company as a leader in dynamic cardiac monitoring. By 2025, VitalConnect has attracted over $100 million in funding.
l London-based Zoe analyzes blood and stool samples to assess lipid profiles, blood glucose levels, and gut microbiome health, providing personalized nutritional plans. The company is now valued at over $300 million.
Smart wearable manufacturers are further integrating AI learning models to predict chronic disease progression, optimize dietary plans, and enable proactive interventions—representing one of the most promising applications in the sector.
By combining digital AI technologies—such as video consultations and smart sensing—a remote healthcare system has been established to support home-based elderly care. Leveraging wearable devices and smart home sensors, seniors can transmit real-time vital sign data to cloud-based diagnostic platforms, enabling remote diagnosis and e-prescription issuance by tertiary hospital specialists. This creates a closed-loop service model of “data collection – intelligent analysis – cloud consultation.” Concurrently, AI-powered emergency response systems leverage multimodal sensors to detect abnormal physiological signals (e.g., sudden spikes in heart rate or fall posture changes), achieving second-level critical condition identification via medical knowledge graphs. These systems then activate emergency services and community networks through a tiered alert mechanism, reducing critical rescue times by over 40%.
AI-powered companion robots are also providing emotional support and cognitive interaction training for elderly users. Enabled by large language models such as ChatGPT and DeepSeek, these AI-driven interactive terminals have rapidly advanced. They now offer natural conversational exchanges, information services, cognitive training games, and personalized entertainment modules—supporting 24/7 interaction with seniors. Integrated psychological state recognition algorithms enable these systems to deliver targeted emotional interventions that help mitigate isolation-related loneliness and cognitive decline.
Globally, Intuition Robotics’ elderly companion robot ElliQ provides daily companionship, combats loneliness, and supports independent living. The company has secured over $100 million in cumulative funding, with continued investment from Japanese firms including Toyota AI Ventures.
AI technologies are further enabling intelligent transformations in home environments to reduce risks for elderly individuals living alone. IoT-based smart home systems are being widely implemented. For example:
l Smart mattresses monitor sleep patterns and automatically adjust firmness to improve sleep quality, while mechanized functions assist bedridden seniors with repositioning and personal care, helping prevent pressure ulcers.
l Intelligent lighting control systems provide responsive illumination to help prevent nighttime falls.
Through these AI-driven advancements, comprehensive age-friendly residential environments are being established—creating livable, adaptive homes for China’s growing elderly population.
l Brain-computer interfaces (BCIs) are widely regarded as having significant potential in the treatment of age-related neurodegenerative diseases, particularly in restoring motor function and enhancing neurocognitive capabilities—areas where early conceptual breakthroughs have already been demonstrated. With continued technological advancement and progressing clinical trials, broader clinical adoption is anticipated within the next 3–5 years.
The global BCI industry is experiencing exponential growth, with market size projected to expand from $2.44 billion in 2024 to $6.52 billion by 2030, representing a compound annual growth rate (CAGR) of 18.1%. Non-invasive BCI currently dominates the market, accounting for an 85.9% share in 2024, largely driven by EEG headset devices that have found widespread adoption in consumer applications. In contrast, invasive BCI remains limited by surgical risks and high costs, with current usage largely confined to medical rehabilitation.
In the context of Alzheimer’s disease, early diagnosis and intervention can be supported through monitoring brainwave signals to detect characteristic EEG abnormalities (such as variations in theta and alpha wave rhythms), aiding the identification of early cognitive impairment. AI-driven algorithms can further enhance this process by tracking the evolution of disease-related biomarkers, facilitating earlier intervention by clinicians. For example, Neurable has developed BCI-enabled headphones currently used to monitor attention in Alzheimer’s patients, with plans to expand into early dementia screening. As the disease progresses, customized cognitive training programs can be developed to activate targeted brain regions through tailored exercises. Invasive BCIs may additionally provide precise stimulation of the hippocampus or prefrontal cortex, with the goal of repairing damaged neural circuits, slowing cognitive decline, and delaying disease progression. MIT researchers, for instance, have developed a BCI-based neural prosthesis that improves short-term memory in mice via hippocampal stimulation, offering a promising new therapeutic direction for Alzheimer’s disease. While late-stage interventions may offer more limited potential, BCI technologies that convert brain signals into speech outputs can still assist patients in maintaining verbal communication and functional control.
BCI applications for restoring motor function in cases of paralysis caused by spinal cord injuries, stroke, or amyotrophic lateral sclerosis (ALS) also represent a critical focus area for the industry. By bypassing damaged neural pathways and establishing direct communication between the brain and external devices, BCI technology enables patients to regain varying degrees of motor or environmental control.
Governments worldwide are actively funding BCI research to accelerate clinical progress. In the U.S., the BRAIN Initiative continues to support projects in neuroregulation and stimulation, with its research emphasis shifting in 2024 from proof-of-concept toward clinical validation. The National Institute of Health (NIH) launched the Blueprint Medtech program in 2024 to facilitate the commercialization of BCI devices. In Europe, Horizon Europe added 18 new BCI projects in 2024, spanning neuroprosthetics development and biomarker screening.
Amid this favorable global backdrop, overseas BCI technologies have already achieved breakthrough clinical milestones. Leading companies initiated multiple trials in 2024: Neuralink completed three human cases with its invasive BCI device, targeting 20–30 additional cases in 2025 and over 22,000 implants by 2030. Synchron’s BCI for paralysis treatment has also advanced to large-scale human trials, with 10 successful implants completed to date.
l The caregiving demands of an aging society are creating substantial incremental market opportunities for embodied robots.
Since Alan Turing first introduced the concept of embodied intelligence in the 1950s, embodied robots have undergone more than half a century of technological evolution, and are now reshaping the technology landscape at an exponential pace.
l Phase I (1950s–2000s) marked the theoretical exploration era, dominated by bionic robotic arms and early AI algorithms. While Honda’s ASIMO achieved basic motion control, limited computational power and restricted sensory perception confined functionality to pre-programmed tasks.
l Phase II (2010s–2019) represented the sensory awakening epoch, driven by deep learning breakthroughs. Boston Dynamics’ Atlas demonstrated complex parkour maneuvers through reinforced learning, signaling a shift from mechanical execution to dynamic environmental interaction.
l Phase III (2020–present) is witnessing the convergence of large language models and multimodal technologies, triggering an mobility/movement intelligence explosion. Commercial deployments such as Tesla’s Optimus and Ubtech’s Walker in industrial and household service applications are driving projections of a trillion-dollar global market for embodied robots by 2030.
From an economic perspective, industrial applications are expected to mature earlier than consumer applications. As production scales, robotic costs will decline while performance improves. Over the long term, demographic-driven labor shortages—particularly in aging societies—will make embodied robots a necessary and viable supplement to the workforce.
In elderly care, embodied robots offer the potential to augment or partially replace human caregivers, thereby improving nursing efficiency and addressing acute caregiver shortages. For example, at the 2024 IEEE International Conference on Robotics and Automation (ICRA), the GARMI nursing robot—developed by the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich—demonstrated its ability to autonomously perform 20 high-frequency care tasks, including repositioning, feeding, and hygiene assistance, by interpreting instructions via ChatGPT. This technology frees caregivers from repetitive physical tasks and allows them to focus instead on providing emotional support and managing emergency interventions, thereby improving overall care quality. Japanese firms are also developing transfer-assist robots that enable safe patient repositioning, reducing the incidence of pressure ulcers and minimizing risks and injuries associated with manual handling.
Technological breakthroughs and supportive policy measures are working in tandem to accelerate adoption. Since 2023, major cities including Shanghai, Beijing, and Guangzhou have launched pilot programs to include devices such as exoskeleton robots and smart monitoring mattresses under long-term care insurance coverage, with reimbursement rates ranging from 30% to 50%. For instance, Shanghai’s 2023 Implementation Opinions on Further Improving the Long-Term Care Insurance System explicitly endorsed the inclusion of eligible smart care devices (e.g., exoskeleton robots, non-invasive monitors) in insurance coverage. In 2024, the Smart Elderly Care Application Demonstration Zone Construction Plan issued by Shanghai’s Pudong New Area specifically incorporated "rental services for care robots" into the long-term care insurance payment pilot, setting a monthly reimbursement cap of RMB 800 per device.
Despite challenges in consumer applications—where high development and manufacturing costs currently constrain widespread commercial deployment—policy-driven pilot programs represent a powerful catalyst. We anticipate that innovative leasing models combined with insurance reimbursement will drive adoption of embodied robots in elderly care. This evolution will enable broader implementation, address workforce shortages in the senior care sector, and optimize cost structures for care institutions. Ultimately, embodied robotics will become a critical component of the technology toolkit to manage the challenges of an aging population.
2.Generative AI and robotics are reshaping the global healthcare landscape by enhancing diagnostic precision, accelerating drug development, and improving medical accessibility. While China possesses distinct competitive advantages in large-scale datasets and application-driven innovation, it also faces foundational bottlenecks, including data silos, algorithmic dependency, and regulatory lag. Future breakthroughs will require advancements in intelligent medical devices, compliant data circulation, and differentiated innovation — all within a synergistic technology-governance-humanities framework to enable the distributed transformation of healthcare resources.
As a key technology driver, AI’s most significant advantage lies in rapidly enhancing precision and efficiency across a wide range of medical applications. By replacing manual processes and facilitating the integration of personalized solutions, AI substantially improves healthcare accessibility and drives systemic industry transformation. In clinical practice, AI is enabling breakthroughs in drug discovery and medical imaging diagnostics, while surgical robotics and brain-computer interfaces are advancing precision in surgical and rehabilitation therapies. Robotics is evolving from simple automation toward intelligence-driven applications, unlocking new possibilities such as customized physical examinations and personalized health management. In parallel, AI’s inclusive capabilities are helping bridge healthcare resource disparities by strengthening primary care through diagnostic assistance tools.
However, significant implementation challenges remain. Healthcare’s highly regulated environment requires prolonged validation cycles for medical AI applications, while human expertise remains irreplaceable in key clinical decision-making. At present, AI technologies primarily serve as efficiency enhancers within medical workflows rather than as autonomous decision-makers.
China holds notable advantages in the scale of its data resources (derived from a population of 1.4 billion) and in application scenario innovation (such as AI-driven medical imaging and chronic disease management), particularly relative to the U.S. Yet key foundational gaps persist: stringent regulatory constraints; reliance on foreign databases for clinical research; dependence on imported algorithmic frameworks and medical technologies; fragmented data silos across healthcare institutions; and an underdeveloped innovation ecosystem for integrating pharmaceuticals with medical devices.
To build a healthcare technology development pathway that reflects China’s unique strengths, it will be essential to strengthen computational infrastructure, promote compliant data circulation, refine regulatory frameworks, and drive breakthroughs in specialized areas such as intelligent medical devices and AI-assisted drug development. A concerted focus on these priorities will help China establish a globally distinctive, innovation-led healthcare ecosystem.
1) AI Drives Transformative Changes in the Healthcare Industry
Generative AI and robotics are rapidly converging with the healthcare sector, catalyzing a broad transformation through the alignment of technological innovation, policy support, and clinical demand. Intelligent medical devices are making breakthroughs in replacing foreign products with domestic substitutes, generative AI is reshaping drug R&D paradigms, distributed healthcare systems are optimizing resource allocation, and automated diagnostics are extending into primary care. Rehabilitation robotics are leveraging reforms in medical insurance reimbursement to expand into home-based health management, forming an integrated ecosystem of devices, data, and services. While China is at the forefront of application-driven innovation, it still faces key challenges—including limited penetration of smart technologies at the grassroots level and lagging payment systems. Moving forward, the sector must forge a development path grounded in technological iteration, institutional adaptation, and ecological synergy.
l Technology-Policy-Clinical Demand Resonance Triggers Industry-Wide Transformation
Breakthroughs in AI and robotics technology are not isolated developments but the product of deep integration across policy guidance, aging society shifts, and foundational technology innovation. At the policy level, the government has elevated AI + healthcare to a strategic priority, establishing application boundaries through documents such as the Healthcare Industry AI Application Scenarios Reference Guide, while also encouraging the development of large-scale medical AI models. Clinically, shortages in primary healthcare resources and the complexity of disease management are accelerating the adoption of intelligent solutions. In county-level hospitals, AI-assisted diagnostic systems have evolved from basic tools to core decision-support systems. On the technological front, multimodal data integration—spanning imaging, pathology, and genomic data—is moving the sector beyond single-point optimization toward a closed-loop system that supports prevention, diagnosis, treatment, and rehabilitation. These forces are shifting the healthcare industry from equipment-centric models toward data-driven transformation—though challenges such as data silos and fragmented computing infrastructure remain critical bottlenecks.
l The Critical Threshold of Medical Device Intelligence: From Substitution to Surpassment
The wave of replacing foreign products with domestic substitutes has advanced from low-value consumables to high-end equipment, underpinned by intelligent technologies and precision manufacturing capabilities. AI navigation systems are pushing the limits of robotic arm precision through dynamic compensation algorithms, enabling domestically produced endoscopic robots to perform sub-millimeter procedures while reducing the surgical learning curve for grassroots physicians by up to 70%. Flexible sensing technologies have cut the cost of electromyography monitoring to one-fifth that of imported systems, accelerating the transition of rehabilitation exoskeletons from laboratories to communities. Intelligent upgrades are redefining product value curves through a standardized hardware and customized software model—transforming devices from standalone tools into data-enabled platforms that support value-added ecosystems spanning hardware, data services, and outcome-based payment models. However, with equipment idle rates exceeding 30% in county-level hospitals, intelligent adoption must be aligned with clinical pathway optimization to realize its full potential.
l The efficiency revolution in pharmaceutical R & D: Generative AI breaks through the traditional challenges of high cost, high risk, and long cycles.
Generative AI is fundamentally transforming the drug discovery paradigm. The LucaProt model enhances RNA virus detection efficiency by 30x through multimodal feature extraction, while target prediction models can compress preclinical cycles from 5 years to 14 months, replacing traditional trial-and-error approaches. This paradigm shift is driven by three pillars: cross-disciplinary data integration (e.g., closed-loop systems combining protein structure prediction with virtual screening); intensive computing infrastructure deployment (e.g., regional medical supercomputing hubs); and innovation in incentive mechanisms (e.g., data property rights trading frameworks).
l Healthcare Service Model Transformation: From Centralized to Distributed Healthcare, Enabling the Decentralization of High-Quality Medical Resources
The decentralized healthcare system is undergoing structural transformation. Telesurgical robots are achieving over 95% success rates in remote operations with distances across 5,000 kilometers. AI-powered health management platforms enable real-time monitoring of 10+ physiological parameters via wearables, increasing chronic disease complication prediction accuracy to 89%. WeDoctor’s Tianjin Digital Health Consortium has validated a closed-loop model of primary screening - AI diagnosis - efficacy tracking, tripling efficiency in grassroots diabetes management. This shift redefines the value of medical resources, moving from equipment procurement costs to health management outcomes. Yet, widespread adoption remains constrained by outdated payment models—only 12% of AI services are reimbursed under DRG—and continued dependence on primary care subsidies.
l AI Diagnostics & Robotic Automation: Transforming Medical Testing – From Efficiency Gains to Grassroots Accessibility
The convergence of AI and robotics is transforming diagnostics from isolated optimizations to full-chain automation with intelligence. Leading domestic companies are rapidly advancing large-scale clinical deployment of AI-assisted diagnostic tools, backed by Class III medical device certifications. These solutions translate physician expertise into reusable algorithmic models, improving diagnostic accuracy and consistency.
Major IVD manufacturers have adopted fully automated laboratory workflows, enabling unmanned, closed-loop systems for sample handling, testing, and analysis—powered by integrated robotic arms, smart transfer mechanisms, and AI-driven quality control.
However, primary care adoption remains limited, with coverage below 20%, hindered by fragmented sample volumes, limited operator training, and high maintenance costs. To address this, the industry is pursuing two approaches: 1) Establish regional testing hubs using a "hub lab + satellite clinic" model to centralize demand, and 2) Developing lightweight, cost-effective POCT + AI systems to empower tertiary hospitals and strengthen primary care services.
l Domestic Surgical Robot Substituting Foreign Imports Accelerates, Achieving Breakthroughs in Accessibility and Procedure Decentralization
Domestic surgical robots are entering a rapid acceleration phase of import substitution, driven by parallel advances in technology and reimbursement frameworks. Domestically produced endoscopic and orthopedic robots are now matching imported models in precision and haptic feedback performance. Expanded reimbursement policies—such as those in Beijing and Zhejiang that include robotic surgical consumables—are lowering adoption barriers and enabling the adoption of robotic systems from tertiary hospitals to county-level institutions. This expansion relies on the integrated advancement of technology, payment systems, and training infrastructure. Modular equipment designs help reduce maintenance costs, making solutions more accessible to grassroots institutions. At the same time, manufacturers are partnering with regional centers to establish training hubs, cutting learning curves from months to weeks. Despite this progress, peripheral hospitals often lack stable technical support teams, leading to reliance on OEM engineers and inconsistent usage. In response, the industry is pursuing two paths: 1) Robotic surgery subscription models to eliminate heavy upfront capital investment, and 2) Remote expert networks powered by 5G + AR, enabling hybrid-reality surgeries that integrate specialist expertise with local execution.
l Rehabilitation Robotics Secures Insurance Coverage, Home Care Becomes Key Commercialization Battleground
The commercialization of rehabilitation robotics is shifting from hospital-centered care to home-based adoption. Breakthroughs in medical insurance policies—such as rental coverage pilots in Shanghai and Nanjing—combined with lightweight device designs have enabled exoskeletons and upper-limb rehabilitation robots to enter households. This shift redefines value: from episodic therapeutic intervention to continuous health management through high-frequency, low-intensity home training that enhances quality of life.
However, the industry faces two key challenges: 1) Meeting the higher adaptability demands of home environments—ensuring device safety, ease of operation, and spatial compatibility, and 2) Overcoming reliance on limited regional insurance pilots, in the absence of a scalable medical-commercial-personal cost-sharing model. Emerging solutions are beginning to address these gaps. Leading companies are developing bundled models that combine hardware, services, and insurance—offering tele-rehabilitation guidance with each robot purchase and collaborating with insurers to create coverage for device failure or suboptimal outcomes. Looking ahead, competition will shift from hardware specifications to ecosystem integration—focusing on building seamless device-data-service platforms that enable home-based health management and realize the full potential of the hospital-to-home rehabilitation transition.
2) Constraints and Future Prospects of AI + Robotics Development in China
China’s AI + healthcare sector shows strong competitive advantages in pharmaceutical R&D across the value chain, yet still faces significant foundational challenges—including fragmented data silos, dependence on foreign algorithms, and a lack of target diversity. The gap with the U.S. remains wide in key ecosystem capabilities such as open data infrastructure and computing power subsidies. While breakthroughs are emerging in frontier areas like AI-integrated organ-on-chip models and virtual clinical trials, three key barriers persist—data governance, grassroots clinical adoption, and ethical certification.
Advancing future development will require strengthening foundational innovation and maturing the ecosystem—through mechanisms such as data property rights trading, collaborative R&D between industry and academia, and tiered accountability frameworks.
l AI empowers entire pharmaceutical R&D value chain, with policies continuously evolving to align with industry realities.
AI is rapidly transforming drug discovery and development. In target identification, DeepMind’s AlphaFold has been widely adopted by pharmaceutical companies, having predicted over 200 million protein structures. Generative AI is reshaping drug design paradigms—Insilico Medicine’s PandaOmics platform, for example, identified the TNIK target and advanced it from discovery to preclinical candidate nomination in just 18 months—at one-third the cost of traditional approaches—making it one of the fastest AI-developed drug programs globally. AI is also revolutionizing clinical trials: Flatiron Health’s cancer data analytics uncovered novel biomarkers for predicting drug response, reducing patient recruitment timelines from months to days. TechMed’s "Internet + AI" recruitment platform now supports over 1,200 clinical trials with 45% efficiency gains, while its RPA-based automation has cut labor needs for adverse drug reaction reporting by 37%.
Although no AI-developed drugs have yet received market approval, the global pipeline now exceeds 300 programs. With continued progress in data sharing, human-AI collaboration, and ethical governance, the industry is well positioned to overcome the traditional "valley of death" in drug development. Notably, the U.S. FDA has recently announced new policy initiatives to reduce, refine, or replace animal testing requirements for monoclonal antibody therapies and other drugs—signaling growing regulatory openness to technological innovation.
l China’s medical AI sector continues to face significant foundational infrastructure deficiencies, limiting its ability to scale deep industrial applications—particularly when compared to the U.S. in several key domains.
China benefits from unique advantages, including the vast scale of its healthcare data from 1.4 billion people and a strong track record of application-driven innovation, such as AI-powered medical imaging and chronic disease management. However, major foundational challenges persist: strict regulatory constraints, dependence on foreign research databases and imported algorithmic frameworks, reliance on overseas medical equipment, fragmented data silos across institutions, and an immature innovation ecosystem for integrating AI with drugs and medical devices.
In contrast, the U.S. demonstrates robust infrastructure strengths. Public data access is high—NIH alone has released over 1 million clinical imaging cases. Cloud computing leaders like Google and Nvidia offer pharmaceutical companies up to 50% subsidies on computing power. Additionally, target innovation in the U.S. has reached 43%, compared to 67% target homogenization in China, where 92% of AI algorithms still rely on foreign open-source frameworks.
l The AI + robotics industry has reached a breakthrough inflection point, yet must overcome three critical challenges: data governance frameworks, clinical adoption barriers, and robust ethical accountability protocols.
Clear opportunities are emerging in pharmaceutical R&D, healthcare accessibility, and ethical frameworks:
l Pharmaceutical R&D: AI-organ-on-a-chip technology is accelerating complex disease modeling, and virtual clinical trials projected to replace 30% of conventional trials.
l Healthcare accessibility: AI-assisted diagnostic systems aim to achieve 80% primary-care coverage by 2027, narrowing urban-rural treatment disparities.
l Ethical governance: GDPR-style cross-border medical data regulations and AI misdiagnosis liability frameworks are anticipated to be formalized by 2026.
While the industry foundation for growth is solid, several critical barriers remain:
Data Governance
l Compliance rate for medical data anonymization remains below 45%.
l Questionable authenticity of historical data limits model generalization capabilities.
Clinical Adoption
l 90% of grassroots AI healthcare projects remain dependent on government subsidies.