At the 17th Annual Meeting of the New Champions (Summer Davos 2026), organized by the World Economic Forum in Dalian on June 23, Gong Yingying, Founder and Chairwoman of Yidu Tech, joined a panel titled “Faster Drugs, Better Access?” to discuss the evolving landscape of pharmaceutical innovation. Her remarks focused on the transformative role of artificial intelligence and healthcare data infrastructure in accelerating drug discovery and improving clinical outcomes worldwide.
The discussion, moderated by Li Xin, Managing Editor of Caixin Global, also included Giovanni Caforio, Chairman of Novartis, Ren Minghui, Professor at Peking University’s School of Public Health, and Eric Tse S. Y., Chief Executive Officer of SBP Group.
Building the Foundation for AI-Driven Healthcare
China has emerged as the second-largest market for innovative drug development, with clinical research activity growing rapidly. Despite this momentum, the industry continues to face obstacles including fragmented data systems, lengthy research cycles, recruitment challenges for clinical trials, and difficulties in generating reliable evidence that supports broader patient access to new treatments.
Gong emphasized that these issues extend beyond operational inefficiencies and are rooted in the fragmented nature of healthcare data itself. She explained that data often exists in isolated systems, lacks consistency, and remains disconnected across institutions, limiting the ability of AI technologies to deliver meaningful impact.
To address this challenge, Yidu Tech has focused on developing healthcare data infrastructure as a core strategic priority.
According to Gong, an effective medical AI ecosystem consists of four key layers: computing power, foundation models, healthcare-specific foundation models, and a robust high-quality data layer. She described this final layer as an “evidence foundation,” where raw healthcare data is transformed through standardization, governance, and validation into structured, traceable, and clinically reliable information.
With this evidence-based framework in place, healthcare providers can deploy AI systems that are safe, transparent, and compliant with regulatory requirements. Gong noted that such infrastructure has become essential for improving both the speed and quality of pharmaceutical research.
A central component of this strategy is Yidu Tech’s proprietary AI platform, YiduCore. The system converts fragmented healthcare information into structured medical knowledge and research intelligence. By September 30, 2025, YiduCore had processed nearly seven billion authorized medical records, connected over 10,000 healthcare institutions, and established a disease knowledge graph encompassing virtually every known disease.
Enhancing Drug Development Through Data Infrastructure
Drawing on partnerships with leading hospitals and research organizations throughout China, Yidu Tech has expanded its data infrastructure capabilities beyond clinical settings to support the entire drug development lifecycle.
During the early stages of research, the company’s disease knowledge graph assists scientists in identifying promising therapeutic areas, evaluating study feasibility, and designing more effective clinical trial protocols. These capabilities help reduce costly missteps, improve resource allocation, and increase the likelihood of successful outcomes.
As clinical trials move into execution, the platform enables intelligent patient screening and matching across multiple locations and research sites. This approach improves recruitment efficiency, shortens enrollment timelines, and helps address common issues such as low matching accuracy and limited patient diversity.
The platform also supports AI-powered quality assurance, continuous data verification, and proactive monitoring of adverse events throughout the trial process. These features strengthen compliance, improve data integrity, and reduce operational risks.
Following drug development, Yidu Tech’s standardized evidence framework facilitates the organization of real-world data, generation of clinical evidence, and analysis of patient populations. These capabilities support post-market research, expansion into additional treatment indications, reimbursement evaluations, and broader clinical adoption, creating a continuous value chain from innovation to patient access.
Gong highlighted that while clinical trial optimization traditionally depended heavily on human expertise, the combination of standardized healthcare data and AI technologies has now become a fundamental requirement for conducting high-quality research and accelerating pharmaceutical innovation.
Expanding Medical AI Internationally While Respecting Data Sovereignty
Discussing international cooperation and healthcare data governance, Gong stressed the importance of prioritizing security, privacy, and regulatory compliance.
She noted that healthcare information is typically stored within sovereign cloud environments governed by national regulations. Independent organizations oversee data protection, access control, auditing, and compliance to ensure information remains secure and properly managed.
Gong also pointed out that healthcare differs significantly from industries that can deploy standardized global products. Medical systems are shaped by local regulations, healthcare policies, and population needs, making localization a critical factor for successful international expansion.
As an example, she cited Yidu Tech’s work in Brunei. Through a joint venture established with the Bruneian government and supported by a shared technical team, the company co-operates the country’s national digital health platform, BruHealth. The collaboration allows Yidu Tech to introduce proven AI healthcare capabilities while fully adhering to local requirements regarding data sovereignty and privacy.
Concluding the discussion, Gong reaffirmed that Yidu Tech’s continued investment in healthcare AI infrastructure is guided by a clear objective: ensuring that technological innovation ultimately delivers meaningful benefits to patients. She emphasized that technology itself is not the end goal; rather, it serves as a means to create healthcare systems that are more efficient, reliable, and accessible for everyone.
The discussion, moderated by Li Xin, Managing Editor of Caixin Global, also included Giovanni Caforio, Chairman of Novartis, Ren Minghui, Professor at Peking University’s School of Public Health, and Eric Tse S. Y., Chief Executive Officer of SBP Group.
Building the Foundation for AI-Driven Healthcare
China has emerged as the second-largest market for innovative drug development, with clinical research activity growing rapidly. Despite this momentum, the industry continues to face obstacles including fragmented data systems, lengthy research cycles, recruitment challenges for clinical trials, and difficulties in generating reliable evidence that supports broader patient access to new treatments.
Gong emphasized that these issues extend beyond operational inefficiencies and are rooted in the fragmented nature of healthcare data itself. She explained that data often exists in isolated systems, lacks consistency, and remains disconnected across institutions, limiting the ability of AI technologies to deliver meaningful impact.
To address this challenge, Yidu Tech has focused on developing healthcare data infrastructure as a core strategic priority.
According to Gong, an effective medical AI ecosystem consists of four key layers: computing power, foundation models, healthcare-specific foundation models, and a robust high-quality data layer. She described this final layer as an “evidence foundation,” where raw healthcare data is transformed through standardization, governance, and validation into structured, traceable, and clinically reliable information.
With this evidence-based framework in place, healthcare providers can deploy AI systems that are safe, transparent, and compliant with regulatory requirements. Gong noted that such infrastructure has become essential for improving both the speed and quality of pharmaceutical research.
A central component of this strategy is Yidu Tech’s proprietary AI platform, YiduCore. The system converts fragmented healthcare information into structured medical knowledge and research intelligence. By September 30, 2025, YiduCore had processed nearly seven billion authorized medical records, connected over 10,000 healthcare institutions, and established a disease knowledge graph encompassing virtually every known disease.
Enhancing Drug Development Through Data Infrastructure
Drawing on partnerships with leading hospitals and research organizations throughout China, Yidu Tech has expanded its data infrastructure capabilities beyond clinical settings to support the entire drug development lifecycle.
During the early stages of research, the company’s disease knowledge graph assists scientists in identifying promising therapeutic areas, evaluating study feasibility, and designing more effective clinical trial protocols. These capabilities help reduce costly missteps, improve resource allocation, and increase the likelihood of successful outcomes.
As clinical trials move into execution, the platform enables intelligent patient screening and matching across multiple locations and research sites. This approach improves recruitment efficiency, shortens enrollment timelines, and helps address common issues such as low matching accuracy and limited patient diversity.
The platform also supports AI-powered quality assurance, continuous data verification, and proactive monitoring of adverse events throughout the trial process. These features strengthen compliance, improve data integrity, and reduce operational risks.
Following drug development, Yidu Tech’s standardized evidence framework facilitates the organization of real-world data, generation of clinical evidence, and analysis of patient populations. These capabilities support post-market research, expansion into additional treatment indications, reimbursement evaluations, and broader clinical adoption, creating a continuous value chain from innovation to patient access.
Gong highlighted that while clinical trial optimization traditionally depended heavily on human expertise, the combination of standardized healthcare data and AI technologies has now become a fundamental requirement for conducting high-quality research and accelerating pharmaceutical innovation.
Expanding Medical AI Internationally While Respecting Data Sovereignty
Discussing international cooperation and healthcare data governance, Gong stressed the importance of prioritizing security, privacy, and regulatory compliance.
She noted that healthcare information is typically stored within sovereign cloud environments governed by national regulations. Independent organizations oversee data protection, access control, auditing, and compliance to ensure information remains secure and properly managed.
Gong also pointed out that healthcare differs significantly from industries that can deploy standardized global products. Medical systems are shaped by local regulations, healthcare policies, and population needs, making localization a critical factor for successful international expansion.
As an example, she cited Yidu Tech’s work in Brunei. Through a joint venture established with the Bruneian government and supported by a shared technical team, the company co-operates the country’s national digital health platform, BruHealth. The collaboration allows Yidu Tech to introduce proven AI healthcare capabilities while fully adhering to local requirements regarding data sovereignty and privacy.
Concluding the discussion, Gong reaffirmed that Yidu Tech’s continued investment in healthcare AI infrastructure is guided by a clear objective: ensuring that technological innovation ultimately delivers meaningful benefits to patients. She emphasized that technology itself is not the end goal; rather, it serves as a means to create healthcare systems that are more efficient, reliable, and accessible for everyone.
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