Prevention is better than cure. That’s been a healthcare mantra since the Dutch philosopher Erasmus coined the phrase in the early 1500s. From the early detection of illnesses to predictive diagnostics, it’s an ideal that the healthcare sector is still pursuing some five centuries later.
Every year, around 800,000 people in the US suffer a stroke, costing roughly $56 billion in healthcare services, medication, and lost workdays. Cutting-edge technologies are now being deployed to help healthcare professionals detect strokes earlier. One example is the deployment of near-infrared sensors in smartphones and mirrors to detect subtle changes in facial vein structure, which cloud-based AI can monitor and analyze before raising an alarm. Research has shown that if a stroke is treated within the first 90 minutes, one in four patients remains symptom-free. To throw in another mantra: when it comes to prevention, timing is everything.
Use-cases like this are extremely encouraging, but while wearables, sensors, and artificial intelligence have offered some of the industry’s largest leaps forward, particularly when it comes to pattern recognition and data analysis, the full potential of this technology remains frustratingly capped. To function effectively, AI needs two fundamental things: speed and access.
Sensitive patient data is often locked within siloed systems, and compliance regulations like GDPR in Europe and HIPAA in the US make AI integration complex. Without the right network environment and data governance frameworks in place, data access remains a barrier to AI adoption in the healthcare sector.
Now, industry-specific cloud platforms, or “vertical clouds,” are emerging to remove these obstacles, offering AI-ready platforms that have security standards, legal compliance, and data governance frameworks baked in. As of 2025, nearly 40% of companies in other sectors are already adopting vertical clouds, with another 14% in the pilot phase and a further 17% considering adoption in 2026. Of all the industries that stand to benefit from a vertical cloud platform, healthcare, with the sheer volume of sensitive data it collects, is surely near the top of the list. A vertical cloud solves many of the data access challenges the health sector faces, particularly when it comes to security, privacy, and compliance, but there is still one remaining catch: speed.
Vertical clouds are mostly based on public cloud services, so while they offer a stable and regulatory-focused business platform, they are still vulnerable to the slowdowns and congestion typical of public cloud infrastructure. As demonstrated by the stroke example above, speed really is everything, so in order for AI applications like this to work effectively, ultra-low-latency and seamless connectivity are non-negotiable, and these are areas in which traditional public cloud networks still often fall short.
What are “Vertical Clouds” Exactly?
Industry-specific clouds, or vertical clouds, are designed to address the unique challenges of healthcare by embedding compliance, security, and governance frameworks directly into their architecture. Unlike traditional public or private clouds, which require extensive customization to meet regulatory requirements, vertical clouds are built with healthcare-specific safeguards from the start.
This means hospitals, research institutions, and medical AI developers can securely store and process patient data while remaining compliant with regulations like HIPAA and GDPR. Beyond security, these platforms enable seamless interoperability, allowing different healthcare providers to share AI-driven insights without running into the usual roadblocks of incompatible systems and fragmented data. By standardizing compliance and data-sharing protocols, vertical clouds lay the groundwork for AI applications to be deployed at scale rather than confined to isolated use cases.
Another key advantage of vertical clouds is their modular approach to AI integration. Instead of relying on one-size-fits-all solutions, these platforms allow healthcare providers to adopt AI tools that fit their specific needs – whether it’s predictive analytics for chronic disease management, automated diagnostic imaging, or real-time patient monitoring. This flexibility is critical as AI continues to evolve, enabling healthcare systems to adopt new innovations without overhauling their existing IT infrastructure. By providing a secure and AI-ready foundation, vertical clouds don’t just remove the technical and regulatory friction slowing AI adoption—they actively accelerate healthcare’s shift toward predictive, data-driven medicine.
Diagnosing the Problem
AI-powered healthcare applications depend on speed – both in terms of data processing and transmission. That is why a vertical cloud – on its own – may not be enough. Whether it’s an AI model detecting anomalies in MRI scans, a wearable device continuously monitoring cardiac rhythms, or a stroke detection system analyzing facial vein structures, near-instant data exchange and analysis is required in order for the technology work as intended. Any delay in transmitting and processing this data could mean the difference between early intervention and a critical medical emergency.
Yet, many healthcare organizations still rely on traditional public cloud infrastructure, which, while scalable and cost-effective, is not optimized for the ultra-low-latency performance that real-time AI applications demand. Congestion, unpredictable routing, and reliance on third-party networks can introduce unacceptable delays, hindering the very AI solutions designed to enhance patient outcomes.
This is where interconnection – the direct, high-speed exchange of data between networks, clouds, and service providers – becomes essential. By leveraging interconnection hubs, hospitals, research institutions, and AI service providers can bypass the unpredictable nature of the public Internet, ensuring that critical medical data flows securely, directly, and with minimal latency. Internet Exchanges (IXs) and Cloud Exchanges play a key role here, allowing organizations to connect their AI-driven healthcare platforms directly to cloud providers, data centers, and research networks without the inefficiencies of traditional internet routing. This not only accelerates AI-powered decision-making but also enhances data security and compliance by keeping sensitive patient information within controlled, high-performance environments.
As AI adoption in healthcare continues to grow, the ability to interconnect data sources, applications, and computing environments efficiently will determine how well the industry can harness the full potential of these technologies. Simply put, without fast and reliable connectivity, AI in healthcare will remain a powerful tool held back by infrastructure limitations.
About Dr. Thomas King
Dr. Thomas King has been CTO at DE-CIX since 2018. Dr. King has also overseen the technical implementation of DE-CIX’s international expansion in markets spanning from North America to Europe, the Middle East, India, Southeast Asia, and most recently Africa. He started his career as a member of the technical staff at DE-CIX in 2008. In 2010, Thomas King joined 1&1 Internet AG as a Product Manager responsible for mobile applications and mail.
