Guotai Junan Securities: The Chinese medical and health industry welcomes the "Deepseek Moment" with huge potential in the AI medical and AI pharmaceutical markets

According to the Zhitong Finance APP, Guosen Securities released a research report stating that through sorting out the "AI + healthcare" application scenarios, it was found that in the pharmaceutical sector, AI has made significant progress in the field of preclinical drug discovery, and has also been applied to patient screening and management in clinical trials. In the medical sector, the standardization process of domestic data is advancing (such as electronic medical record rating), and there is also certain application potential in areas such as diagnostic services, medical devices, hospital management, and precision diagnosis. Overall, the AI healthcare and AI pharmaceutical markets have enormous potential and are among the most important application fields of AI technology. Considering the extremely high valuations of some overseas AI pharmaceutical/healthcare benchmark companies, the domestic AI pharmaceutical/healthcare sector has broad prospects and imaginative space.

The main viewpoints of Guosen Securities are as follows:

Event: The healthcare industry is undergoing a transformation period of digital transformation and intelligent upgrading. Since the beginning of 2025, the attention on several overseas AI healthcare companies has significantly increased. The recent report "Big Ideas 2025" released by ARK Invest mentioned that using artificial intelligence to "operate" data will disrupt diagnosis, drug discovery, and treatment, and the Chinese healthcare industry is welcoming its own "Deepseek moment."

The global artificial intelligence solutions market is rapidly developing, with the healthcare sector being one of the largest application fields for artificial intelligence

Driven by technological advancements, favorable government policies, and increasing demand across various industries, the global artificial intelligence solutions market is rapidly developing. The global artificial intelligence solutions market size increased from USD 43.3 billion in 2018 to USD 139.5 billion in 2022, with a CAGR of 34.0%, and is expected to further grow from USD 187 billion in 2023 to USD 1,414.2 billion by 2030, with a CAGR of 33.5%. The China artificial intelligence solutions market size increased from USD 3.5 billion in 2018 to USD 13.9 billion in 2022, with a CAGR of 40.8%, and is expected to further grow from USD 18.5 billion in 2023 to USD 168.3 billion by 2030, with a CAGR of 37.0%.

The global market size for artificial intelligence solutions in the healthcare sector is expected to grow from USD 13.7 billion in 2022 to USD 155.3 billion by 2030, with a CAGR of 35.5%; the global market size for artificial intelligence solutions in the agriculture sector is expected to grow from USD 5.4 billion in 2022 to USD 56 billion by 2030, with a CAGR of 34.0%; the global market size for artificial intelligence solutions in the beauty and cosmetics sector is expected to grow from USD 2.7 billion in 2022 to USD 28.1 billion by 2030, with a CAGR of 34.0%.

The global market size for artificial intelligence solutions in the petrochemical sector is expected to grow from USD 1.4 billion in 2022 to USD 20.6 billion by 2030, with a CAGR of 39.8%; the global market size for artificial intelligence solutions in the battery sector is expected to grow from USD 3.8 billion in 2022 to USD 39.5 billion by 2030, with a CAGR of 33.8%; The global market size for AI solutions in the display sector is expected to grow from USD 100 million in 2022 to USD 1.3 billion by 2030, with a CAGR of 39.1%.

AI + Pharmaceutical R&D

The drug development process is time-consuming, typically taking at least ten years to commercialize a drug asset. Before a drug asset is launched and commercialized, the entire drug development process usually involves: (1) a four to six-year drug discovery process, including approximately 25 months from target identification to lead compound stage, about 25 months from lead compound to candidate compound stage, and around ten months for lead compound optimization; (2) a one to two-year preclinical candidate compound stage; (3) a six to seven-year clinical trial stage; (4) a six-month to two-year regulatory approval stage. However, the use of artificial intelligence technology and quantum physics-based computing in the drug discovery process can reduce the time and costs required, improving the efficiency of the drug discovery process.

Compared to traditional manual methods, AI approaches significantly enhance efficiency in drug discovery. AI-based methods demonstrate significant advantages in time, cost, and success rates during the drug discovery process, greatly improving the efficiency and effectiveness of pharmaceutical R&D.

(1) Time Efficiency: The traditional drug discovery process typically takes 5 to 7 years; AI methods can quickly analyze large amounts of data through machine learning and deep learning algorithms, significantly shortening the drug discovery time.

(2) Cost Efficiency: Traditional drug R&D is expensive, especially in terms of experimental materials, labor costs, and equipment usage, with high failure rates further increasing costs; AI methods can reduce the number of actual experiments through virtual screening and simulation, lowering material and labor costs.

(3) Success Rate: Traditional methods rely on extensive experimentation and trial and error; AI methods can increase the overall success probability of drug R&D through big data analysis and pattern recognition.

(4) Data Utilization: Traditional methods depend on manual processing and analysis, which can easily overlook important information; AI methods can efficiently process and analyze vast amounts of data, providing more comprehensive insights.

(5) Innovation: Traditional methods rely on researchers' experience and intuition for innovation; AI methods can explore a broader chemical space, proposing novel compound designs and optimization schemes, driving innovation in drug discovery.

AI + Medical Devices

Breakthroughs in AI large model technology and IT IoT technology have opened up imaginative possibilities for medical devices to understand clinical scenarios and build intelligent ecosystems. Many medical devices have begun deep integration with AI, such as monitoring instruments that continuously monitor and analyze patient conditions, medical imaging devices that significantly improve diagnostic efficiency, automated analysis and testing devices that require no human intervention, and surgical robots that assist doctors in precise operations, all of which have achieved preliminary concepts of intelligent transformation in medical devices to some extent.

AI + Diagnostic Services

AI technology is demonstrating enormous potential in various stages of medical testing, from sample processing to report interpretation. The medical testing industry will continue to benefit from AI empowerment, with multimodal, full-scenario AI agents in the medical testing industry expected to comprehensively enhance the intelligent process of medical testing services, achieving overall quality and efficiency improvements in external services and internal operations AI + Precision Diagnosis

In the field of genomics, the reduction in sequencing costs due to autonomous and controllable tools is accelerating the arrival of the era of personal genomics. AI can be applied to massive genomic data, aiding in the efficient and precise extraction of necessary genetic information to understand disease progression, which is beneficial for medical institutions and individuals in conducting precise health management and accelerating the development of precision medicine.

AI + Smart Hospitals

AI will assist in the construction of smart hospitals, achieving optimized allocation of medical resources through in-depth analysis of massive data, helping hospitals enhance patient safety management and the efficiency of medical resource allocation. AI can help hospitals provide more personalized medical services, customizing treatment plans based on patients' specific conditions, improving treatment outcomes and patient satisfaction; it can also monitor patients' health status in real-time, providing timely interventions and treatments to prevent the occurrence and progression of diseases.

Recommended Focus Targets:

1. AI + Pharmaceutical R&D: Chengdu XianDao (688222.SH), WuXi AppTec (603259.SH, 02359); 2) AI + Medical Devices: Xiangsheng Medical (688358.SH), Mindray Medical (300760.SZ), United Imaging Healthcare (688271.SH); 3) AI + Diagnostic Services: KingMed Diagnostics (603882.SH), Dian Diagnostics (300244.SZ); 4) AI + Precision Medicine: Shengxiang Biology (688289.SH); 5) AI + Smart Hospitals.

Risk Warning:

The development or commercialization of AI-related products and services may fall short of expectations, industry policy risks, and intensified market competition risks