AI in Healthcare's Next Era: Trust, Real-World Fit, and Access for All
At the 2025 Fortune Innovation Forum in Kuala Lumpur, leaders from across healthcare sat down to talk about what actually works with AI. On the "AI vs. MD" roundtable, Gong Yingying, Founder and Chairwoman of Yidu Tech, laid out a practical roadmap for how AI should be developed and deployed. She was joined by KPJ Healthcare Berhad President & Managing Director Keat Chyuan Chin and NUHS Clinical Advisory Director Dr. Zubin Daruwalla, with Fortune AI Editor Jeremy Kahn moderating.
Her message was clear: the next phase isn't about hype. It's about evidence, safety, and real outcomes in clinics and communities.
1) No Universal AI - Only Workflow-Aligned Precision Solutions
There's no one AI to solve healthcare. Effective systems depend on two non-negotiables: high-quality medical data and tight integration into clinical workflows.
Built on its YiduCore engine, Yidu Tech has developed deep disease knowledge structures and a doctor-facing AI Copilot composed of specialized agents. These agents support documentation, diagnostic assistance, and research data management, working in the background where clinicians already work. The goal: reduce cognitive load, improve consistency, and respect the complexity of real-world medicine.
2) Inclusiveness as the Mission - Technology Must Expand Access
AI's real value is measured by how many people it helps. Yidu Tech has worked with multiple Chinese cities and insurers to support inclusive health insurance-low-premium plans informed by data-driven insights and AI-based actuarial modeling.
In parallel, the team is co-developing digital doctor twins with medical experts to extend professional consultation and everyday health management. The outcome is broader access to reliable, affordable care for residents who often face higher barriers.
3) Globalization Requires Local Co-Creation, Not Copy-Paste
Technology may travel, but clinical settings and regulations are local. Yidu Tech's international approach centers on co-building with governments, hospitals, and clinicians.
In Southeast Asia, the company has helped develop national digital health platforms that enable personal health records, AI-guided consultation, and personalized health management. In Singapore, it supports the Ministry of Health's home-based care initiatives with tailored digital tools for structured remote monitoring and continuity of care after discharge.
What This Means for Healthcare Leaders
- Treat data as infrastructure: invest in quality, governance, and consent; wire AI into the EHR and existing clinical pathways.
- Deploy modular AI agents that map to specific tasks (documentation, triage, coding, research) and track hard outcomes like time saved, error rates, and readmissions.
- Design for equity: multilingual support, varied health literacy, and inclusive risk modeling; bring payers to the table early.
- Co-create locally with clinicians and regulators; run small pilots, iterate, and scale only with validated results.
- Keep human oversight and safety front and center; use prospective evaluations, bias monitoring, and clear escalation paths.
The Bigger Picture
The direction is set: AI should strengthen clinical expertise, live inside real workflows, and extend benefits to broader populations. That's the consensus emerging from leaders across systems and regions.
Yidu Tech's work across clinical settings, public programs, and cross-border collaborations shows how responsible deployment can move from slide decks to bedside and home care. The next chapter will be written by clinicians, technologists, and policymakers working together-not by algorithms alone.
Further Reading
Upskilling for Clinical AI
If you're building clinical AI capability in your team, see focused options by role here: AI courses by job.
Your membership also unlocks:
