# Background

## Introduction

Today's global healthcare industry stands at a historic turning point, where the deep integration of cutting-edge technologies such as Artificial Intelligence (AI), blockchain, Decentralized Physical Infrastructure Networks (DePIN), and Real World Asset (RWA) tokenization is redefining the boundaries and possibilities of medical services. This technological revolution is not only changing the operational models of medical institutions, but also fundamentally reshaping patient healthcare experiences and the value realization methods of medical data.

In this wave of transformation, the digital transformation of healthcare infrastructure appears particularly crucial. From traditional paper medical records to electronic health records, from manual diagnosis to AI-assisted medical decision-making, from centralized data storage to distributed blockchain networks, each technological leap has brought unprecedented efficiency improvements and service innovations to the healthcare industry. The proactive response of policymakers, the accelerated iteration of technological development, and society's growing demand for intelligent medical services are jointly driving this historic transformation process.

However, traditional healthcare systems have also exposed many deep-seated structural problems in embracing digital transformation. The phenomenon of data silos between medical institutions remains widespread, leading to fragmented patient information and uneven allocation of medical resources; the scarcity and high cost of high-performance computing resources constrain the widespread application of AI technology in grassroots medical institutions; the non-standardized characteristics and insufficient liquidity of medical assets hinder effective capital allocation and rapid implementation of innovative projects. Although these challenges are complex, they precisely provide broad application space and value creation opportunities for emerging decentralized technologies.

## Industry and Policy Environment

### Macro Trends of Digital Transformation

The global healthcare industry is experiencing an unprecedented wave of digital transformation. **The global healthcare IT market size has rapidly grown from $663 billion in 2023 to $760.2 billion in 2024, and is expected to reach $1.834.3 trillion by 2030, with a compound annual growth rate of 15.8% from 2024-2030**. This strong growth reflects the urgent need of medical institutions for digital solutions and also demonstrates the core role of technological innovation in improving medical services.

The driving forces of digital transformation come from multiple levels: first is the improvement of patient expectations, as consumers increasingly hope to obtain convenient and personalized medical service experiences; second is the pressure of medical cost control, as digital tools can significantly improve operational efficiency and reduce management costs; finally is the optimization of the regulatory environment, as governments around the world have introduced supportive policies, providing a good development environment for medical digital innovation.

### Artificial Intelligence: The Core Driver of Medical Innovation

The application of artificial intelligence technology in the medical field is showing explosive growth. **The global AI healthcare market is expected to grow from $26.57 billion in 2024 to $187.69 billion in 2030, with a compound annual growth rate of up to 38.62%**, a growth rate far exceeding traditional medical technology fields. More noteworthy is that the return on investment of AI technology has been validated in practice: currently 79% of medical organizations are already using AI technology, with an average return on investment of 1:3.2 and an investment payback period of only 14 months.

AI application scenarios in the medical field are becoming increasingly rich: in the diagnostic stage, AI image recognition technology can identify early cancer lesions with accuracy exceeding that of senior physicians; in the treatment stage, AI-assisted surgical robots are improving surgical precision and reducing postoperative complications; in drug development, AI algorithms have significantly shortened the cycles of new drug discovery and clinical trials; in health management, AI-driven wearable devices are achieving early disease warning and personalized health interventions.

### Blockchain: Reshaping the Trust Foundation of Medical Data

The value of blockchain technology in the medical field is gaining widespread recognition. **The global medical blockchain market is expected to grow from $7.04 billion in 2023 to $214.86 billion in 2030, with a compound annual growth rate of 63.3%**. This remarkable growth rate is mainly due to the increasingly severe medical data security issues and the urgent need to establish trusted data sharing mechanisms.

Frequent medical data breach incidents have exposed the vulnerability of traditional centralized systems. The distributed architecture and encryption technology characteristics of blockchain provide technical guarantees for secure storage, privacy protection, and trusted sharing of medical data. More importantly, blockchain technology can achieve data interoperability between medical institutions while protecting patient privacy, providing richer and more reliable data foundations for precision medicine and medical research.

### Decentralized Infrastructure: New Opportunities for Computing Power Democratization

**The rise of DePIN (Decentralized Physical Infrastructure Networks) technology has opened up new pathways for the democratized deployment of medical computing power**. With the explosive growth in demand for computing power from medical AI applications, traditional centralized cloud computing models face challenges such as high costs, data privacy concerns, and unstable service availability. DePIN provides medical institutions with more economical, secure, and flexible computing power solutions by integrating globally distributed computing resources.

Industry forecasts show that by 2028, 25 billion smart devices will be connected to the network globally. These devices are not only data collection and processing terminals, but will also become important nodes in decentralized computing networks, creating Universal Basic Data Income (UBDI) for device owners. This new economic model not only lowers the threshold for medical AI applications, but also provides new incentive mechanisms for individuals and small institutions to participate in medical innovation.

### Human Resource Shortage: A Catalyst for Technological Innovation

The global healthcare system is facing unprecedented human resource shortage challenges. **It is predicted that by 2030, the global medical workforce shortage will reach 10 million people**, a figure that is particularly severe in developing countries and underdeveloped regions. The scarcity of human resources not only affects the accessibility of medical services, but also drives up medical costs and exacerbates the imbalance in medical resource allocation.

However, workforce shortages have also become an important catalyst for medical technology innovation. AI-assisted diagnostic systems can help primary care physicians improve diagnostic accuracy; telemedicine platforms enable specialist doctors to serve broader patient populations; smart medical devices and automated treatment solutions are reducing the workload of medical staff. These technological innovations not only alleviate workforce shortage pressures, but also provide new possibilities for improving the quality of medical services.

### Policy Environment Support

Governments around the world are increasing their policy support for medical digital transformation, creating a favorable environment for industry innovation and development. India's "Prime Minister's Digital Health Mission" launched in 2021 aims to establish a unified national digital health ecosystem, achieving secure interoperability of medical data and equitable access to medical services. The EU invested $49.3 million in 2021 to support 38 AI medical innovation projects, focusing on AI applications in rare disease diagnosis, drug development, and precision medicine.

The United States encourages medical data interoperability and clinical application of innovative technologies through the "21st Century Cures Act" and related policies; China's "Healthy China 2030" strategy and "14th Five-Year Plan" for the digital economy both position medical digital transformation as a key development direction. These policies not only provide development opportunities for medical technology companies, but also create a favorable regulatory environment for the application of emerging technologies in the medical field.

### Historic Convergence of Development Opportunities

Multiple factors including technological progress, market demand, policy support, and capital investment are forming a historic convergence of development opportunities. The popularization of 5G networks provides infrastructure support for telemedicine and real-time data transmission; the maturity of cloud computing and edge computing technologies has lowered the deployment threshold for medical AI applications; the widespread application of IoT devices has made real-time monitoring and analysis of health data possible.

The synergistic effect of these factors is driving the healthcare industry toward more intelligent, personalized, and inclusive development, providing unprecedented historic opportunities for the construction and application of decentralized medical infrastructure. At this critical moment, innovative solutions that can effectively integrate cutting-edge technologies such as AI, blockchain, DePIN, and RWA will have the potential to occupy important positions in future medical ecosystems.

## Key Challenges and Opportunities

Despite the enormous potential of AI and blockchain technologies in the medical field, there are still numerous challenges in achieving true medical digital transformation:

### Major Challenges

* **Computing Resources and Cost Issues**: The demand for computing resources from medical AI applications has reached unprecedented heights. **Medical AI model training requires tens of thousands of hours of GPU computation, and a single medical imaging file can reach several GB**, with a typical CT scan dataset potentially containing hundreds to thousands of high-resolution images. The cost structure of traditional centralized cloud services poses significant barriers to small and medium-sized medical institutions: on one hand, the rental costs of high-performance GPU resources are expensive, with annual costs often reaching hundreds of thousands of dollars; on the other hand, data transmission and storage costs are also extremely high, especially in scenarios requiring real-time processing of large amounts of medical data. Additionally, centralized architectures have inherent flaws in data privacy protection, as medical data must leave the local environment for processing on third-party servers, which conflicts with strict medical data protection regulations.
* **Data Security and Privacy Protection**: The sensitivity of medical data makes it a primary target for cyberattacks. **In 2022 alone, India's healthcare industry suffered 1.9 million cyberattacks, while medical data breach incidents in the United States increased by 35% year-over-year in 2023**, affecting over 70 million patient records. According to IBM reports, the average cost of medical data breaches has reached $10.3 million per incident, far higher than the average for other industries. Furthermore, while strict data protection regulations such as GDPR and HIPAA implemented globally are necessary, they also create complex compliance barriers for data sharing. The phenomenon of data silos between medical institutions is severe, and due to the lack of unified data standards, secure sharing protocols, and interoperability frameworks, patient data cannot flow effectively between different systems, greatly hindering cross-institutional clinical collaboration, medical research, and the development of medical AI.
* **Insufficient Liquidity of Medical Assets**: Medical industry asset management and traceability issues affect global supply chain security. **Global counterfeit drug sales exceeded $75 billion in 2021, a 90% increase from the previous five years, with WHO reports showing that 1 in 10 drugs circulating in developing countries have quality problems**. Meanwhile, insufficient liquidity of medical equipment is a serious problem: the global second-hand medical equipment market exceeds $24 billion, but due to the lack of reliable quality assessment and certification systems, large amounts of equipment are idle or prematurely scrapped. Financing channels for medical infrastructure are similarly limited, especially for emerging markets and small and medium institutions, with over 50% of small and medium medical institutions globally reporting insufficient funds to meet required medical equipment updates. The non-standardized characteristics of medical assets, fragmented supply chain data, and lack of transparency make evaluation and transaction processes complex, significantly raising participation thresholds for small and medium institutions and individual investors.
* **Human Resource Shortage**: Medical workforce shortage has become a global crisis. **According to joint reports from the World Health Organization and International Labour Organization, global medical workforce shortage will increase from 15 million in 2022 to 10 million in 2030**, with Africa and the Middle East bearing the heaviest burden, and some countries having medical service coverage rates below 40%. The growing aging population further exacerbates this shortage: by 2050, the global population aged 65 and above will grow to 1.7 billion, and the elderly care industry in the United States will need to add 36% more jobs. In terms of technology adoption, surveys show that 70% of medical institutions face professional talent shortages in implementing emerging technologies such as artificial intelligence, and nearly 55% of institutions report lacking digital skills training resources. The medical industry faces multiple challenges including technical understanding, regulatory compliance, change management, and talent development when adopting emerging technologies.

### Development Opportunities

However, these challenges also breed enormous market opportunities:

* **Decentralized Computing Networks**: **DePIN networks utilize the expected 25 billion smart devices globally to create new revenue models**, providing new pathways to solve medical AI computing power shortages and high costs;
* **Blockchain Data Interoperability**: **The blockchain technology market is expected to develop rapidly with a compound annual growth rate of 63.3%**, and its immutable characteristics provide a technical foundation for secure sharing and interoperability of medical data;
* **Real World Asset (RWA) Tokenization**: **The global asset tokenization market potential reaches $10 trillion**. Medical asset tokenization can release the liquidity of medical equipment and infrastructure, lowering investment thresholds;
* **AI-Driven Efficiency Improvements**: **The average return on AI investment is 1:3.2, with an investment payback period of only 14 months**, providing more inclusive and efficient diagnostic and treatment solutions for medical services.

**The convergence of these technological innovations provides entirely new possibilities for building next-generation medical infrastructure, and is expected to spawn a trillion-dollar decentralized medical ecosystem, releasing the true value of medical data and assets while solving the pain points of traditional medical systems.**

## References

\[1] Healthcare IT Market Size, Share & Trends Analysis Report 2024-2030, Grand View Research, [https://www.grandviewresearch.com/industry-analysis/healthcare-it-market](https://www.grandviewresearch.com/industry-analysis/healthcare-it-market)

\[2] AI In Healthcare Market Size, Share & Trends Analysis Report 2025-2030, Grand View Research, [https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-market](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-market)

\[3] Blockchain Technology In Healthcare Market Report 2024-2030, Grand View Research, [https://www.grandviewresearch.com/industry-analysis/blockchain-technology-healthcare-market](https://www.grandviewresearch.com/industry-analysis/blockchain-technology-healthcare-market)

\[4] Global Strategy on Human Resources for Health: Workforce 2030, World Health Organization, [https://www.who.int/publications/i/item/9789241511131](https://www.who.int/publications/i/item/9789241511131)

\[5] Microsoft makes the promise of AI in healthcare real through new collaborations, Microsoft Blog, [https://blogs.microsoft.com/blog/2024/03/11/microsoft-makes-the-promise-of-ai-in-healthcare-real-through-new-collaborations-with-healthcare-organizations-and-partners/](https://blogs.microsoft.com/blog/2024/03/11/microsoft-makes-the-promise-of-ai-in-healthcare-real-through-new-collaborations-with-healthcare-organizations-and-partners/)

\[6] BlackRock's $10 Trillion Tokenization Vision: The Future Of Real World Assets, Forbes, [https://www.forbes.com/sites/nataliakarayaneva/2024/03/21/blackrocks-10-trillion-tokenization-vision-the-future-of-real-world-assets/](https://www.forbes.com/sites/nataliakarayaneva/2024/03/21/blackrocks-10-trillion-tokenization-vision-the-future-of-real-world-assets/)

\[7] DePIN Explained: What Are Decentralized Physical Infrastructure Networks?, HackerNoon, [https://hackernoon.com/depin-explained-what-are-decentralized-physical-infrastructure-networks](https://hackernoon.com/depin-explained-what-are-decentralized-physical-infrastructure-networks)

\[8] Indian healthcare faced enormous cyber-attacks in 2022: Report, CyberPeace Foundation, [https://www.expresshealthcare.in/news/indian-healthcare-faced-enormous-cyber-attacks-in-2022-report/437127/](https://www.expresshealthcare.in/news/indian-healthcare-faced-enormous-cyber-attacks-in-2022-report/437127/)

\[9] Decentralized physical infrastructure network (DePIN), explained, Cointelegraph, [https://cointelegraph.com/explained/decentralized-physical-infrastructure-network-depin-explained](https://cointelegraph.com/explained/decentralized-physical-infrastructure-network-depin-explained)

\[10] AI in Chinese healthcare: From medical imaging to AI hospitals, Daxue Consulting, https://daxueconsulting.com/ai-healthcare-china/

\[11] Decentralized Physical Infrastructure Networks: Challenges and Opportunities, IEEE Xplore, https://ieeexplore.ieee.org/document/10737386
