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- Building trust in healthcare AI
Key takeaways
- Artificial intelligence is no longer a future prospect—it is actively redefining clinical and operational care delivery across the APAC region
- Implementing AI requires navigating a highly fragmented legal and regulatory landscape, demanding robust, localized validation frameworks
- Building systemic trust in AI depends on moving beyond theoretical accuracy to proving workflow safety and measurable value
APAC leadership perspectives: An expert panel on building trust in healthcare AI
APAC leadership perspectives: An expert panel on building trust in healthcare AI
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Artificial intelligence has emerged as a critical lever for healthcare systems facing escalating challenges, from workforce shortages and rising costs to disparities in care access. As AI transitions from theoretical research into active clinical settings, successful deployment requires navigating a complex hurdle: Establishing trust among clinicians, patients, and regulators.
In a recent Healthcare Transformers expert panel, leading voices from across the Asia-Pacific region came together to discuss what this means and how to establish trust in AI within this landscape. Dr. Julian Sham, APAC Clinical Digital Transformation Chapter Lead, Roche Information Solutions, facilitated the discussion between Prof. Robin Mann, Chief Digital and Information Officer at Monash Health, Australia, Prof. So Hyun Kang, Medical Doctor / Surgeon, Seoul National University Bundang Hospital, South Korea, Prof. Adam Chee, Director, AI Development Office (AIDO), SingHealth, Singapore, and Dr. Ernest Lo, Senior Medical Director, Roche Information Solutions, US. The panel drew on their own experience at leading hospitals and health systems across the region to provide real-world examples of AI use and the challenges they face with implementation. The discussion bypassed theoretical hype, focusing entirely on practical, on-the-ground perspectives from executives and clinicians actively managing clinical validation, workflow integration, and regulatory rigor.
Addressing systemic challenges with AI
The APAC region presents a highly fragmented healthcare landscape, meaning AI solutions cannot simply be uniformly deployed. Each panelist highlighted specific, urgent systemic pressures driving their AI adoption:
- Managing exponential demand: At Monash Health in Australia, Professor Robin Mann, Chief Digital and Information Officer, is utilizing AI to meet a projected doubling of patient demand over the next decade.
- Overcoming workforce constraints: For Professor Adam Chee, Director of the AI Development Office at SingHealth, Singapore, technology adoption is not just about innovation—it is a mandatory response to severe manpower constraints where traditional labor scaling is not an option.
- Reducing cognitive overload: Dr. Ernest Lo, a US-based oncologist and Senior Medical Director at Roche Information Solutions, noted that physicians often spend 50% of their time parsing fragmented Electronic Health Records (EHR) just to find the 1% of data necessary for clinical decision-making.
The "trust equation" and clinical validation
While enthusiasm for AI’s potential is high, clinical adoption stalls without proven, calibrated trust. Professor So Hyun Kang, a surgeon at Seoul National University Bundang Hospital, emphasized that physicians demand robust clinical evidence before altering treatment protocols. She highlighted the necessity of "explainable AI" (XAI)—tools that do not just deliver a conclusion, but transparently show the clinician exactly how that conclusion was reached.
However, AI models trained on Western populations may not directly translate to Asian demographics. Professor Chee approaches external AI solutions with rigorous scrutiny, asking not just "Does it work?" but "Does it work here, on our population?".
Bridging the regulatory gap
The pace of AI innovation is drastically outstripping the speed of global regulatory bodies. Into this vacuum, healthcare organizations must establish their own rigorous governance.
The panel explored how they are securing patient data and monitoring systems for unexpected risks. To combat the lack of universal standards, Dr. Lo highlighted how industry leaders are now partnering with specific global academic centers of excellence to establish new clinical benchmarks for medical AI.
Discover the "silver bullet" use cases
AI use cases range from complex genomic analysis to routine administrative support. But what is the single most effective application of AI in a hospital today? During the panel, Dr. Julian Sham challenged each expert to reveal the one "silver bullet" AI use case they believe delivers the most immediate, tangible value to clinicians.
Their answers—ranging from an unexpected operational bottleneck to a critical patient-facing intervention—might reshape where you invest your digital health resources.
Watch the on-demand webinar: APAC leadership perspectives: An expert panel on building trust in healthcare AI to hear their answers and learn how to deploy AI safely and confidently in your organization.
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