Asia-Pacific is becoming the proving ground for AI-native medtech solutions. Because the region is not burdened with the outdated legacy healthcare infrastructure common in the West, health systems across the region are scaling AI at unprecedented speed. While the U.S. remains the largest global medtech market by revenue share, APAC is quickly catching up, driven by the AI-powered efficiency gains unfolding across the region.
Against this backdrop, AlphaSense’s Daren Yoong, APAC Head of Enterprise Sales, hosted an expert panel at LSI Asia 2026 featuring:
- Virginia Giddings, Vice President, Exploration, Edwards Lifesciences
- Ruey Peh, Head of Innovation, Genesis Medtech
These experts gathered to discuss how AI is reshaping patient access, surgical care, and commercial strategy across APAC in real time. The discussion also covered the barriers still standing in the way of broader AI deployment.
This article explores the key trends pinpointed by the expert panel. Learn where AI is delivering the strongest returns early on and the strategic and cultural shifts required for APAC medtech companies to compete in 2026, powered by AlphaSense.
Improving Patient Access to Care
The expert panel agreed that AI’s near-term value in medtech lies upstream, in areas such as patient identification, triage, and referral. What this looks like in practice: AI screens routine diagnostic data, automates the triage decision, and routes the patient to a specialist who might otherwise never have seen them.
Modalities like cardiovascular care are considered especially ripe for this kind of upstream AI-driven transformation, with experts highlighting structural heart conditions like aortic stenosis, mitral disease, and tricuspid disease as low-hanging fruit. For example, transcatheter aortic valve replacement (TAVR) has strong clinical evidence and proven outcomes, yet only a small fraction of eligible patients globally receive this treatment. The failure point is not the therapy itself or a lack of evidence, but rather that patients go undiagnosed or don’t navigate the healthcare system well enough to reach a specialist. AI models tackle that failure point by flagging at-risk patients, such as those identified through automated EKG scans across a hospital system, and referring them directly to interventionalists.
Catching these patients early on improves patient outcomes and carries a massive economic benefit. This is because delayed treatment for conditions that worsen over time, such as aortic stenosis, turns manageable cases into costly emergencies and can lead to complications that add even more concern and cost.
For TAVR, it's a front-end issue. It's not a solution issue, and that's where I think there's so much potential for AI. …[I]f AI can facilitate the patient's access to the technology by early detection, by facilitating the referral through the system, by identifying the right time points for intervention, and for the patients to receive the TAVR, I think that's really the opportunity there.
Emphasizing APAC-Specific Product Design
APAC markets demand bespoke AI solutions that are tailored specifically to the region. Yet experts caution that building local versus building global should not be a binary choice. The more effective path, they argue, is to start by solving a specific clinical problem that current Western tools can’t address before building that solution on a platform architecture that’s flexible enough to apply to other markets.
Genesis Medtech’s work on intraoperative visualization is a case in point. Relative to Western markets, clinicians in Asia see a higher proportion of small, ground-glass, and indolent tumors that are considered strong candidates for less-invasive lung-preserving procedures like wedge resection or segmentectomy.
The challenge is that the lung deflates and deforms during minimally invasive surgery, making tumor margins hard to see with traditional methods and forcing surgeons to improvise. Genesis’s response: a Lung AI Console that marries pre-op 3D imaging with real-time thoracic scope video so that the system can track the tumor’s position even as the lung deforms.
“Instead of [asking] when we start off, ‘Is it gonna be a local product or a global product?’ I think it really still boils down to, number one, who is the customer that you really wanna serve…that you think can benefit from AI? And number two: [Are] there any problems that his current tools in his armamentarium don’t address well? …[T]hen we think about what sort of customization we need for the offering, which in this case is the AI.
And I think when we do that, it then naturally becomes that the solution is localized, but hopefully — and that's a part that we in Genesis are trying to build — still a technology platform for a global market.
Prioritizing Differentiated, Higher-Margin Products
The expert panelists confirmed that APAC health systems are moving away from simply pursuing the most affordable devices toward high-value products geared toward APAC patients specifically.
However, capturing that opportunity requires a new go-to-market approach. Rather than selling expensive finished products directly on the market, more firms are co-developing them directly with hospital systems. The upside goes beyond better patient outcomes: Working through implementation together helps build trust that compounds and breeds long-term success, even if that trust-building process takes time. Experts argue that trust is especially important in APAC markets.
Why we see the partnerships increasingly important for operating in Asia is…that we're in this region where trust is important, and trust is actually a moat — and it's a moat that doesn't erode like price. Actually, it's a moat that actually scales and compounds, as compared to [firms] just always [saying], 'Oh, because we are a company primarily in Asia, hence, we are only offering products that are…lower cost.'
Tackling Remaining Barriers to AI Adoption
Even if the technology works, AI adoption still runs into two main obstacles, the panelists agreed. One is data governance and policy. At a time when access to AI alone is no longer a rarity, high-quality clinical data governance has emerged as a key differentiator. Firms that can secure anonymized and ethnically representative data for model training are considered best positioned in this environment. However, doing so requires maintaining a constant dialogue with hospital ethics committees and regulators to figure out proper implementation.
The bigger challenge is winning clinician trust. Historically, clinicians have extracted and verified data manually. Ramping AI adoption requires clinicians to cede control of these manual processes to AI. This is primarily a behavioral barrier, not a technical one, and overcoming the barrier requires framing AI as augmenting clinicians rather than replacing them. For example, in radiology, where imaging volume outpaces clinician supply due to chronic care shortages, AI-assisted triage is an absolute necessity to provide the required bandwidth.
I think the barrier is really the adoption [itself]. And when you think about that, it's…much more of a behavioral kind of barrier than a technical barrier. But behavioral changes are the hardest things for human beings. …What is it gonna take for those behavioral changes [to happen]? It's that physicians really have to trust AI, and they have to get to a point where they're just okay with it being a black box.
Stay on Top of Market-Moving Trends
As AI moves from answering questions to influencing healthcare decisions, the standard for trust, context, and source-backed evidence rises. But even as AI deployment has advanced across the industry, most firms are not utilizing AI to its full advantage.
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