Healthcare faces an AI infrastructure crisis as investments surge
Aug 14, 2025
Hospitals and health systems are pouring billions into AI, but many lack the IT backbone to support them.
As reported in Healthcare IT News, while investments in AI are soaring, the infrastructure needed to deploy them effectively is lagging behind. From advanced analytics and predictive modeling to AI-powered care delivery, healthcare organizations are hitting roadblocks caused by outdated networks, siloed data systems, and security models designed for an earlier era.
With AI in healthcare projected to reach $149 billion by 2030, IoMT spending expected to climb to $814 billion by 2032, and remote patient monitoring forecast to hit $78 billion by 2029, the gap between ambition and readiness is widening fast.
This threatens to leave hospitals with advanced tools they can’t fully use. Without immediate action to modernize IT infrastructure, healthcare providers risk falling behind in deploying technologies that could improve patient care, streamline operations, and strengthen security.
Bandwidth, security, and ROI pressures create challenges
The article highlights how fundamental networking limitations are hampering innovation. The shift from 10-gigabit to 100-gigabit capacity has become essential to handle AI workloads, real-time analytics, and comprehensive EHR management.
At the same time, networks must support a growing mix of hospital-issued devices, personal devices, and smart medical equipment, many of which are deployed far beyond traditional hospital walls to serve metropolitan areas and remote populations.
The security challenge is equally urgent. As connected devices proliferate and edge computing becomes more common, the traditional “hub-and-spoke” network model is giving way to distributed architectures.
These expand the attack surface and require new strategies, such as zero trust frameworks, multi-factor authentication, and continuous access verification. Security now functions as the new perimeter, shifting the focus from devices to individual user authentication and ongoing access validation.
On top of the technical issues, CIOs must justify infrastructure upgrades to leadership in terms of measurable returns. This means moving beyond treating IT as a cost center and demonstrating direct links between infrastructure investments and improved patient outcomes, operational efficiency, and overall quality of care.
The opportunities: building a stronger AI backbone
While the infrastructure crisis poses risks, it also creates an opportunity for healthcare organizations to future-proof their digital operations. Modernizing the backbone now enables capabilities that go far beyond today’s pilot projects.
Upgrading to high-capacity, low-latency networks can support advanced AI workloads at scale, such as real-time imaging analysis that flags potential anomalies during a radiologist’s review, or predictive sepsis detection models that continuously monitor streams of vitals from connected patient devices.
Cloud-hybrid architectures can give providers the flexibility to run computationally intensive training jobs in the cloud while keeping patient-identifiable data in secure on-prem environments.
Addressing infrastructure gaps now also allows organizations to enhance security without slowing innovation. Zero trust architectures, combined with continuous authentication, can allow researchers to securely access de-identified patient datasets for AI model training without risking unauthorized access to live patient records.
Hypothetically, an oncology team could train a large language model to parse unstructured oncology notes for clinical trial eligibility while keeping sensitive identifiers protected at every step.
A strong infrastructure can help improve return on investment by making AI solutions easier to scale across multiple service lines. An AI Data Foundry acts as the central nervous system, providing the pipelines, governance, and compute resources that allow models to move seamlessly from one clinical domain to another.
For example, a health system that develops an AI model to predict patient readmissions could use the foundry to retrain it with domain-specific datasets for cardiology, orthopedics, and general surgery, while keeping the underlying architecture, quality controls, and compliance workflows consistent.
This modularity not only speeds deployment but also reduces technical debt, since each new use case can plug into an existing, validated infrastructure rather than requiring a rebuild. Interoperability ensures these AI capabilities integrate smoothly with EHR systems, imaging archives, and IoMT device data, which turns the infrastructure into an active enabler of innovation rather than a barrier to it.
Centific helps healthcare organizations close the gap
AI Data Foundry by Centific is designed to give organizations the complete environment they need to build, scale, and govern AI solutions without having to assemble and maintain complex infrastructure themselves. Centific offers:
Unified AI operations. A single platform for data ingestion, engineering, fine-tuning, deployment, and monitoring, reducing the complexity of managing multiple systems.
Flexible deployment models. Support for cloud, on-premises, and hybrid environments ensures compliance with data sovereignty requirements while enabling scalability.
Built-in governance and compliance. AI-enabled quality checks, human-in-the-loop processes, and adversarial testing help ensure safe, ethical, and regulatory-compliant AI deployment.
Domain-specific expertise. Access to medical subject matter experts, advanced annotation tools, and synthetic data generation fills training gaps while protecting patient privacy.
Centific helps healthcare providers move beyond pilot projects and toward AI solutions that improve patient outcomes, enhance care delivery, and transform the healthcare experience.
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Healthcare
Responsible AI
AI infrastructure
Health IT modernization
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