AI in Healthcare: What 2025 Revealed — and What to Watch in 2026

Over the past few years, AI in healthcare has moved rapidly from experimentation into real operational environments. By 2025, many organisations were no longer asking whether AI could work, but whether it could hold up under real world conditions — at scale, across systems, and over time.

At Jonda Health, we spent the year working across diagnostics, clinical data pipelines, research platforms, and digital health systems. What emerged was not a single breakthrough moment, but a set of recurring patterns that shaped where AI delivered value — and where underlying constraints became harder to ignore.

This blog article shares a selection of those reflections. It is not a prediction or a framework, but a perspective on what became clearer in 2025, and where attention is likely to shift as healthcare AI moves into 2026.

This perspective reflects on patterns observed as AI systems moved from pilots into operational use across healthcare in 2025. It focuses on data readiness, reliability, sustainability, trust, and system design — and how these factors are shaping where healthcare AI is likely to evolve in 2026.

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Data availability is not the same as data readiness

In many healthcare environments, the primary constraint on AI performance was not access to data, but whether that data was usable, aligned, and ready for computation at scale.

One assumption steadily lost momentum in 2025: that healthcare’s primary data challenge is access.

In practice, data was often available, but not consistently usable. Information remained fragmented across systems and vendors, trapped in PDFs or scanned documents, expressed in inconsistent formats, units, reference ranges, and languages — and collected primarily for documentation or billing rather than computation.

As AI systems were introduced, limitations surfaced early, not at the model layer, but in the ambiguity and semantic misalignment of inputs. Teams found that preparing and maintaining data was not a one time task, but an ongoing operational capability.

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Reliability and sustainability matter more than sophistication

In operational healthcare settings, dependable and sustainable systems consistently outperformed technically impressive but fragile AI solutions.

As AI initiatives moved beyond pilots, operational realities came into focus. Systems that were dependable — predictable in timing, stable under load, and consistent in behaviour — often proved more valuable than those that were technically impressive but fragile.

At the same time, sustainability became harder to ignore. Costs associated with compute, human review, maintenance, and continuous data preparation accumulated over time. Some initiatives slowed not because the technology failed, but because the operating model did not hold under sustained use.

In real healthcare environments, reliability and sustainability increasingly determined which systems endured.

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Human effort didn’t disappear — it shifted

As automation increased, human effort moved toward exception handling, review, and judgment rather than disappearing altogether.

Automation reduced certain forms of manual work, particularly repetitive extraction and reconciliation tasks. But it did not eliminate human effort.

Instead, effort shifted toward reviewing edge cases, handling exceptions, monitoring system behaviour, and intervening when outputs fell outside expected bounds. The practical question became not whether to automate, but where human judgment should remain deliberately embedded.

Systems that made this balance explicit tended to integrate more smoothly into everyday workflows.

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LLMs changed the pace, not the fundamentals

Large Language Models accelerated specific workflows in healthcare, but did not remove the need for structure, validation, and human oversight.

Large Language Models (LLMs) played a visible role in 2025, particularly in working with unstructured and multilingual clinical data. They accelerated tasks that previously required significant manual effort, lowering barriers to working with complex documents and free text.

However, deployments that relied on LLMs alone often encountered familiar constraints. Validation, consistency, clinical context, and edge cases still required structure, rules, and human oversight.

Where LLMs were treated as components — paired with clinical vocabularies, deterministic checks, and review processes — systems tended to be more stable and easier to trust.

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Trust and traceability became operational constraints

As AI outputs moved closer to clinical and operational decisions, the ability to trace, explain, and contextualise those outputs became critical for sustained use.

Teams increasingly asked whether outputs could be traced back to source data, whether changes were visible over time, and when automation should — or should not — be relied upon. Systems that made these boundaries clear progressed further than those that did not.

Trust emerged not from confidence alone, but from transparency and traceability built into system design.

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Looking ahead to 2026

Rather than a clean break, 2026 is shaping up as a continuation — with attention shifting away from individual tools and toward infrastructure, traceability, and design choices that support steady, responsible use.

Multilingual and cross border data handling is becoming a baseline expectation. Invisible AI embedded in workflows is proving more durable than highly visible tools. And responsibility is increasingly being designed upstream, rather than addressed after deployment.

These shifts are less about novelty, and more about building systems that can hold under real world conditions.

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Want the full perspective paper?

This article is an excerpt from a longer perspective paper on the operational realities of AI in healthcare, reflecting on 2025 and where attention is likely to shift in 2026.

If you’d like to read the full paper, or explore any of these themes in more depth, you’re welcome to contact us at partnerships@jonda.health.