Yidu Tech's Gong Yingying at Summer Davos: Pioneering AI in Innovative Medicine Access
On June 23, 2026, during the 17th Annual Meeting of the New Champions, commonly referred to as Summer Davos, held in Dalian, Gong Yingying, the Founder and Chairlady of Yidu Tech, engaged in a thought-provoking panel discussion titled "Faster Drugs, Better Access?" This dialogue, moderated by Li Xin, the Managing Editor of Caixin Global, also featured prominent industry leaders including Giovanni Caforio, Chair of the Board at Novartis, Ren Minghui from Peking University, and Eric Tse, CEO of SBP Group.
Gong addressed a crucial issue in the pharmaceutical industry: the ongoing structural transformation of global pharmaceutical research and development (R&D) amidst the rapid expansion of clinical trial activities in China, now recognized as the world’s second-largest hub for innovative drug development. She highlighted specific challenges that continue to hinder the accessibility of innovative therapies, including fragmented data, extended timelines for development, complexities in patient recruitment, and inefficient generation of evidence necessary for drug approvals.
"These obstacles go beyond mere inefficiencies; they arise from a deeper fragmentation of healthcare data," Gong remarked. She described how scattered, inconsistent, and disconnected data pose significant barriers that prevent artificial intelligence (AI) from fully realizing its potential in the healthcare sector. As a solution, Yidu Tech has committed to constructing a robust healthcare data infrastructure from the ground up.
During her address, Gong laid out a comprehensive four-layer architecture essential for advancing medical AI. This structure includes a computing layer, foundational models, healthcare-specific foundational models, and the pivotal high-quality data pool which she referred to as the "evidence layer." She emphasized that raw data alone is insufficient for clinical decision-making. Data must undergo standardization, governance, and transformation to evolve into a reliable clinical evidence system that can be trusted by healthcare providers.
This evidence foundation enables healthcare organizations to build responsible, controllable, and compliant AI agents that are instrumental in enhancing the quality and efficiency of drug development processes. An embodiment of this vision is Yidu Tech's proprietary tool, the "AI Medical Brain," known as YiduCore. By converting fragmented healthcare data into structured knowledge pertaining to diseases, YiduCore supports an extensive range of healthcare applications. By the end of September 2025, YiduCore had processed nearly 7 billion authorized medical records, linked over 10,000 healthcare institutions, and established a disease knowledge graph encompassing virtually all known diseases.
Yidu Tech’s advancements in data infrastructure are proving to be transformative in improving the efficiency of drug development. By leveraging an extensive network of top hospitals and clinical research partners throughout China, Yidu Tech has effectively expanded its healthcare data infrastructure beyond traditional hospital settings, thereby supporting the entire clinical research and drug development value chain. This systematic application enhances both real-world studies and clinical trials.
At the initial stages of drug development, Yidu Tech’s disease knowledge graph assists researchers in pinpointing promising research trajectories, crafting clinical trial protocols, and executing feasibility assessments, thereby mitigating the risks associated with poorly designed studies, implementation complexities, and resource inefficiencies. During the trial execution phase, the platform facilitates intelligent patient identification and matching across various regions and research centers, greatly boosting recruitment efficiency and decreasing enrollment timelines. It tackles chronic issues such as slow patient recruitment, low matching accuracy, and a lack of diverse patient populations.
Furthermore, AI-driven quality control, along with real-time data validation and intelligent monitoring of adverse events, provides dynamic oversight throughout the lifecycle of clinical trials. These capabilities not only enhance data quality and compliance but also diminish operational risks.
In the post-development phase, the standardized evidence layer enables efficient real-world data curation, evidence generation, and patient cohort analysis. This functionality is vital for conducting post-marketing studies, expanding indications, making reimbursement decisions, and achieving clinical adoption based on solid evidence. Gong noted that while clinical trial optimization historically leaned heavily on human expertise, the necessity for standardized data infrastructure combined with AI has now become a fundamental requirement for conducting high-quality clinical research and accelerating innovative drug development. What used to be regarded as an enhancement has now transformed into a critical component of industry innovation.
Gong also addressed the importance of global collaboration and data governance, underscoring that the management of healthcare data must prioritize security and compliance. With regards to global regulations, healthcare data is typically housed within sovereign government-controlled cloud systems. Independent entities oversee the protection of this data, including access permissions, usage audits, and compliance enforcement.
She pointed out the unique nature of healthcare, which differs from industries that can implement standardized global products. Healthcare delivery is significantly influenced by local regulations, public health needs, and policies, necessitating deep localization for any strategy involving international expansion. For instance, Yidu Tech’s joint venture in Brunei, working alongside the government on the BruHealth digital health platform, exemplifies the importance of maintaining local data sovereignty and privacy while integrating AI capabilities to enhance digital health services for the local community.
In closing, Gong reiterated that Yidu Tech's dedication to healthcare AI infrastructure is motivated by a simple yet profound goal: ensuring that innovations in healthcare translate into tangible benefits for patients. She emphasized that while technology serves as a powerful tool, the ultimate aim remains to enhance the efficiency, reliability, and accessibility of healthcare systems across the globe.
Gong addressed a crucial issue in the pharmaceutical industry: the ongoing structural transformation of global pharmaceutical research and development (R&D) amidst the rapid expansion of clinical trial activities in China, now recognized as the world’s second-largest hub for innovative drug development. She highlighted specific challenges that continue to hinder the accessibility of innovative therapies, including fragmented data, extended timelines for development, complexities in patient recruitment, and inefficient generation of evidence necessary for drug approvals.
"These obstacles go beyond mere inefficiencies; they arise from a deeper fragmentation of healthcare data," Gong remarked. She described how scattered, inconsistent, and disconnected data pose significant barriers that prevent artificial intelligence (AI) from fully realizing its potential in the healthcare sector. As a solution, Yidu Tech has committed to constructing a robust healthcare data infrastructure from the ground up.
During her address, Gong laid out a comprehensive four-layer architecture essential for advancing medical AI. This structure includes a computing layer, foundational models, healthcare-specific foundational models, and the pivotal high-quality data pool which she referred to as the "evidence layer." She emphasized that raw data alone is insufficient for clinical decision-making. Data must undergo standardization, governance, and transformation to evolve into a reliable clinical evidence system that can be trusted by healthcare providers.
This evidence foundation enables healthcare organizations to build responsible, controllable, and compliant AI agents that are instrumental in enhancing the quality and efficiency of drug development processes. An embodiment of this vision is Yidu Tech's proprietary tool, the "AI Medical Brain," known as YiduCore. By converting fragmented healthcare data into structured knowledge pertaining to diseases, YiduCore supports an extensive range of healthcare applications. By the end of September 2025, YiduCore had processed nearly 7 billion authorized medical records, linked over 10,000 healthcare institutions, and established a disease knowledge graph encompassing virtually all known diseases.
Yidu Tech’s advancements in data infrastructure are proving to be transformative in improving the efficiency of drug development. By leveraging an extensive network of top hospitals and clinical research partners throughout China, Yidu Tech has effectively expanded its healthcare data infrastructure beyond traditional hospital settings, thereby supporting the entire clinical research and drug development value chain. This systematic application enhances both real-world studies and clinical trials.
At the initial stages of drug development, Yidu Tech’s disease knowledge graph assists researchers in pinpointing promising research trajectories, crafting clinical trial protocols, and executing feasibility assessments, thereby mitigating the risks associated with poorly designed studies, implementation complexities, and resource inefficiencies. During the trial execution phase, the platform facilitates intelligent patient identification and matching across various regions and research centers, greatly boosting recruitment efficiency and decreasing enrollment timelines. It tackles chronic issues such as slow patient recruitment, low matching accuracy, and a lack of diverse patient populations.
Furthermore, AI-driven quality control, along with real-time data validation and intelligent monitoring of adverse events, provides dynamic oversight throughout the lifecycle of clinical trials. These capabilities not only enhance data quality and compliance but also diminish operational risks.
In the post-development phase, the standardized evidence layer enables efficient real-world data curation, evidence generation, and patient cohort analysis. This functionality is vital for conducting post-marketing studies, expanding indications, making reimbursement decisions, and achieving clinical adoption based on solid evidence. Gong noted that while clinical trial optimization historically leaned heavily on human expertise, the necessity for standardized data infrastructure combined with AI has now become a fundamental requirement for conducting high-quality clinical research and accelerating innovative drug development. What used to be regarded as an enhancement has now transformed into a critical component of industry innovation.
Gong also addressed the importance of global collaboration and data governance, underscoring that the management of healthcare data must prioritize security and compliance. With regards to global regulations, healthcare data is typically housed within sovereign government-controlled cloud systems. Independent entities oversee the protection of this data, including access permissions, usage audits, and compliance enforcement.
She pointed out the unique nature of healthcare, which differs from industries that can implement standardized global products. Healthcare delivery is significantly influenced by local regulations, public health needs, and policies, necessitating deep localization for any strategy involving international expansion. For instance, Yidu Tech’s joint venture in Brunei, working alongside the government on the BruHealth digital health platform, exemplifies the importance of maintaining local data sovereignty and privacy while integrating AI capabilities to enhance digital health services for the local community.
In closing, Gong reiterated that Yidu Tech's dedication to healthcare AI infrastructure is motivated by a simple yet profound goal: ensuring that innovations in healthcare translate into tangible benefits for patients. She emphasized that while technology serves as a powerful tool, the ultimate aim remains to enhance the efficiency, reliability, and accessibility of healthcare systems across the globe.
Topics Health）
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