AI has arrived in CVRM. Now healthcare systems must implement it.

By Jeanne Esteves, MSc, Senior Medical Writer and Akila Raghavan, MSc, Account Director

The conversation about AI has shifted from possibility to implementation

Not long ago, conversations about artificial intelligence in healthcare often started with: does it actually work? Today, a growing body of evidence has largely answered that question, showing that AI can enhance the performance of screening and diagnostic tools, automate clinical workflows and even provide clinical decision support.

This shift was especially hard to ignore at the 2026 European Society of Cardiology (ESC) congress. The most interesting discussions moved beyond the capability of algorithms under standardized conditions to the real challenge: implementing AI in routine clinical care.

As we advance towards integrated CVRM care, we need to reflect the reality that cardiovascular, renal and metabolic conditions do not fall neatly within specialty boundaries. Their risk factors overlap and their biology intersects, yet data generated across primary care, cardiology, nephrology and endocrinology are still often reviewed in fragments. Connecting these insights will be essential to capturing the full interplay of a patient’s conditions and delivering coordinated, holistic care.

Seeing more in the data we already collect

AI offers a powerful opportunity to unlock new insights from the data healthcare already generates. In the CVRM space, that data comes from routine tests such as ECGs, echocardiograms, CT scans, MRIs, blood pressure measurements, laboratory tests, retinal images and even data from wearables or remote monitoring technologies. While these investigations could provide a rich picture of patient health, they are generally performed and interpreted to answer a specific clinical question.

What if the data generated by these familiar tools could tell us more?

One opportunity lies in identifying patients who may otherwise be overlooked. By processing a large array of data at speed, AI can help clinicians extract more from routine tests and identify patterns beyond conventional interpretation. At ESC 2026, we saw many examples of AI-assisted analysis helping clinicians extract more value from routine ECGs and echocardiograms by raising suspicion of cardiac conditions that may otherwise be overlooked. By automatically flagging relevant patterns, AI could prompt earlier referral and targeted diagnostic testing, while reducing the cognitive burden of considering a wide differential diagnosis for every patient. Applied more broadly across healthcare settings, including into primary care and other specialties, AI could help connect currently fragmented pathways and bring relevant expertise to the patient earlier.

A second opportunity lies in monitoring patients in cardiovascular care. While routine investigations provide a snapshot of patient health, wearables can continuously collect data such as heart rhythm, heart rate and physical activity in everyday life. AI could enable continuous, patient-centric monitoring, helping clinicians identify emerging risks, track changes over time and intervene when needed.

More broadly, AI could bring together data from across the patient journey to build a more holistic picture of cardiovascular, renal and metabolic health. By identifying how risks and conditions interact, it could support more coordinated decision-making and help clinicians tailor care around the whole patient, rather than individual diseases or isolated data points.

The next era of AI in CVRM

As the expanding clinical potential of AI becomes increasingly apparent, attention is shifting away from just what algorithms can achieve towards a more practical question: how can these tools be implemented successfully within real-world healthcare systems?

Discussions at ESC 2026 around the use of AI in clinical practice repeatedly reinforced that successful implementation will depend on whether clinicians trust the data, understand the intended role of the technology and can see how it will support their everyday practice. This highlights the essential role of education.

Successful implementation will also require wide stakeholder engagement. Key decision makers across the healthcare system, including hospital directors, payers, healthcare professionals and technology developers will need to establish where the tool should sit within the clinical pathway. Patient understanding and willingness to participate must also be considered. The pharmaceutical industry has a distinct opportunity to connect these perspectives, support education and evidence generation and help stakeholders explore where AI could address unmet needs across patient pathways.

Bringing data together will not automatically drive integrated care.

AI tools need a clear purpose within the clinical pathway, connecting existing information and supporting coordinated decision-making rather than creating another data silo. Crucially, it should not add to the volume of data and signals clinicians are already expected to manage. Instead, it should help make sense of that information, automating processes where possible, and integrating relevant insights into more seamless clinical workflows.

Ultimately, implementing AI effectively into healthcare systems could help reduce workload pressures, support clinical decision-making and enable all-round efficient and connected care.

As AI becomes embedded across CVRM pathways, engagement strategies will need to evolve alongside it. Pharmaceutical organizations will need to understand how AI is influencing clinical decision points, consider a broader set of stakeholders involved in adoption and tailor education to the distinct questions each audience will have. With implementation becoming the priority, three opportunities stand out:

1. Shape the AI opportunity in CVRM. Facilitate collaboration between multi-stakeholder working groups to identify priority clinical challenges, map decision points across patient pathways and define where AI has the greatest potential to improve outcomes.
2. Generate and communicate evidence for adoption. Develop real-world evidence programs, implementation case studies and educational initiatives that demonstrate the value of AI in routine CVRM practice and support wider adoption.
3. Build the partnerships required for scale. Create forums that bring together key clinicians, healthcare leaders, payers, technology providers and patients to share learnings, align on priorities and develop practical roadmaps for implementation.

AI has arrived in CVRM. As new applications continue to emerge across CVRM, the pharmaceutical industry needs to think beyond what the technology can do and start considering the role it can play in successfully implementing AI into everyday clinical practice.