From context to continuity: Five ways AI will transform healthcare in 2026 and beyond

As 2026 begins, the conversation around AI in healthcare feels markedly different from this time last year. Not because the technology itself has changed, but because expectations have. Across health systems, life sciences and R&D departments, there’s been a profound shift away from experimentation for its own sake towards accountable, transparent and reliable AI that demonstrates ROI within real care pathways, under real-world conditions.

After years of pilots and sometimes bold claims, the most meaningful progress in 2025 came from the realisation that AI has to genuinely fit into care delivery if it is to have a tangible impact. Technology that focuses on workflow integration, clinical relevance and longitudinal visibility can make a greater difference than those built purely for technical performance.

In 2026 the role of technology in helping health providers meet their key performance indicators (KPIs) will become even more important. AI will be judged not on what it can predict, but on how effectively it improves visibility across multimodal data, connects pathways, closes care gaps and supports better decision-making in day-to-day clinical workflows. 

Context will matter more than perfect data

As AI increasingly enters real-world pathways, one lesson has become clear: clinicians aren’t short on data; they’re short on context at the moment decisions need to be made. Signals that matter to a person’s health are often scattered across free text, conversations and fragmented records, or buried at the point of care. When such context is missed, so too are patients and outcomes.

In 2026, providers will stop chasing “perfect” data and instead make fuller use of the information they already have at their fingertips. Through responsible, secure multimodal AI that can reason across structured records, free text and history, systems will surface the right information at the right time leading to better decisions for individual patients and improved continuity across the entire journey.

Diagnostics will shift from identifying disease to identifying missing patients

Across health systems globally, people continue to be undetected or underserved. Not because data, guidelines or clinical expertise are lacking, but because existing systems are reactive by design. Built for narrow clinical encounters, they struggle to see beyond their immediate limits and this means follow-ups fall through the cracks, patients enter pathways too late, or quietly drift out of them altogether. Such failings are rarely dramatic, but they’re persistent, compounding and shape outcomes for millions at scale.

Advances in multimodal AI mean diagnostics are increasingly moving beyond identifying disease to identifying patients and care gaps across the full clinical journey. Acting as connective tissue across pathways, such systems in 2026 will help care teams identify and intervene proactively, unlocking more precise, equitable and sustainable care.

Improving care and improving margins will no longer be mutually exclusive

For years, digital health innovation has been framed as a trade-off between improving care and improving margins. In 2026, that framing will continue to break down. Earlier patient identification and engagement throughout the care journey reduces avoidable admissions, supports more efficient use of resources and improves predictability – both clinical and financial – across the system.

In 2026, the platforms that attract adoption and investment won’t be those that promise insight alone, but those that can draw a clear line between earlier identification, better outcomes, more efficient use of resources and which create value across entire healthcare systems. Aligning clinical impact with financial sustainability like this is not a compromise; it is what enables care to be delivered responsibly, at scale and over the long term.

Clinical decision-making will become the new frontier for AI adoption

For clinicians, the challenge has never been knowing what good care looks like. It is determining the right next step often under intense time pressure and across an expanding landscape of data, guidelines, trial protocols and clinical knowledge.

In 2026, AI adoption will be driven by its ability to support decision-making without adding to the burden. Multimodal, guideline-aligned approaches will also reshape how life sciences engage with healthcare systems, moving away from ad-hoc, trial-by-trial interactions towards longer-term, pathway-level collaboration grounded in shared patient benefit.

Supporting the people behind the patients will become essential

Healthcare does not take place in silos. Outcomes are shaped not only by clinicians, but by families, carers, social workers and community teams yet these groups often operate with limited visibility.  

In 2026, diagnostics and decision-support systems will increasingly be judged by how well they support this wider ecosystem. Shared, timely insight across the network can help prevent care plans from quietly unravelling once a patient leaves the clinic. By equipping everyone involved with clearer context, AI will help make sure clinical decisions translate into lasting real-world outcomes.

The question for 2026 is no longer whether AI belongs in healthcare, but whether it will be used to change what truly matters: closing patient care gaps and improving patient quality and safety in a compliant and financially sustainable manner.