SingHealth's AI Chief Makes the Case for Platform-Based Agentic AI in Clinical Workflows

Most clinical AI tools today are narrow by design. A diagnostic algorithm reads chest X-rays. Another flags diabetic retinopathy. Each one is bolted to a specific device, a specific task, a specific silo. That's been fine for proving AI works in medicine. It's not fine for scaling it.

Dr. Daniel Ting, director of the AI Office at SingHealth (Singapore's largest healthcare group), is making the case that the next phase of clinical AI needs a different architecture entirely. Speaking ahead of HIMSS26 APAC, Ting laid out why he thinks the field needs to move from narrow, single-purpose algorithms toward platform-based agentic AI, systems built to handle repetitive tasks autonomously and safely, at scale, across a health system rather than within one device or one workflow.

It's a familiar pattern to anyone who's watched enterprise software evolve. You start with point solutions. Eventually the maintenance burden and the integration mess force a shift toward platforms that can orchestrate many tasks through a common layer. Healthcare AI is hitting that inflection point now, and Ting's framing gives it a name: agentic workflows.

From Diagnostic Tools to Ambient Agents

The most concrete example Ting points to is ambient AI, systems that listen in on clinical encounters and handle documentation automatically, rather than requiring a clinician to type notes while talking to a patient.

Here's the practical problem ambient AI solves: clinicians spend an enormous chunk of patient encounters staring at a screen, typing notes, rather than making eye contact and actually listening. That's not a new complaint. Electronic health records have been blamed for eroding clinician-patient rapport since they became standard. But Ting frames ambient AI as a genuine fix rather than another layer of software to fight with.

The shift matters architecturally, too:

• Narrow AI operates task-by-task, tied to specific devices or specific diagnostic pipelines. A radiology model doesn't talk to a documentation model. Each tool needs its own validation, its own integration work, its own maintenance cycle.
• Agentic AI, in Ting's framing, operates at the platform level. Instead of one algorithm per task, you get an orchestration layer capable of running multiple repetitive tasks (documentation, history-taking support, workflow triage) continuously and safely across a system.
• The payoff isn't just efficiency. It's clinician attention. Freeing up cognitive and administrative load during a patient encounter lets doctors actually do the "history-taking" and rapport-building parts of the job that got squeezed out by screen time.

This isn't a purely theoretical shift for Singapore's healthcare sector. Ting's comments track with broader agentic AI momentum happening across the region right now. A major Singaporean airport group, for instance, has recently been offering healthcare organizations lessons on running agentic AI at scale, an unusual cross-industry signal that the orchestration challenges (safety, monitoring, continuous operation) look similar whether you're managing passenger flow or patient flow. HIMSS itself is also updating its Digital Health Indicator, the framework organizations use to benchmark their digital maturity, specifically to account for the AI era. That's a tell: the standards bodies see this transition as significant enough to require new measurement tools, not just new products.

There's also a safety dimension that's easy to underweight if you're not the one accountable for it. "Safely at scale" is doing a lot of work in Ting's framing. Agentic systems that operate continuously and autonomously carry different risk profiles than a narrow diagnostic tool that flags a finding for a human to review. An orchestration layer making decisions across many repetitive tasks, hour after hour, needs guardrails that a single-purpose classifier never had to worry about. Healthcare AI governance conversations are increasingly centered on exactly this: not whether the model is accurate, but whether the system running continuously in the background is auditable, interruptible, and bounded.

What to Watch

Ting's comments are part of the broader lead-up to HIMSS26 APAC, where agentic AI in clinical settings is shaping up to be a dominant theme. It's worth watching a few threads here.

First, whether "platform-based agentic AI" ends up meaning something architecturally distinct from just running several narrow models under one dashboard. There's a real difference between orchestration (agents that plan, delegate, and execute across tasks) and mere aggregation (several tools sharing a UI). Health systems evaluating vendors should push on which one they're actually buying.

Second, keep an eye on how ambient AI adoption plays out beyond pilot programs. It's one of the more mature agentic use cases in clinical settings precisely because the task (listening and documenting) is well-bounded and the failure modes are relatively easy to catch. That makes it a useful bellwether for whether more ambitious agentic workflows, like autonomous triage or care coordination, can follow.

Third, watch what HIMSS bakes into its revised Digital Health Indicator. If a major industry benchmarking framework starts scoring organizations on agentic AI readiness rather than just "AI adoption" broadly, that's a strong signal about where health system investment is expected to flow over the next few years.

None of this means narrow AI is going away. Point-solution diagnostic tools still do useful, validated work, and they're not about to be ripped out. But the direction of travel, at least according to the people running AI offices inside major health systems, is toward orchestration layers that can take on the repetitive grunt work safely and continuously, so clinicians can get back to doing the part of the job that actually requires being human.