Healthcare AI Advances with Precision and Data Control

The following is a guest article by Paul Speciale, Chief Technology Evangelist and CMO at Scality

Artificial intelligence adoption is accelerating across nearly every industry, but healthcare and life sciences organizations are approaching the technology differently than many of their peers. While sectors such as financial services and manufacturing are rapidly expanding the use of generative AI and edge-based systems, healthcare organizations are prioritizing precision, governance, and data control as they scale AI into production.

That measured approach reflects the realities of the healthcare environment. Clinical and operational AI systems must deliver reliable outcomes, operate within strict regulatory frameworks, and protect highly sensitive patient data. As a result, healthcare organizations are favoring proven and explainable AI models while building infrastructure strategies designed to support long-term operational resilience.

New research from Freeform Dynamics, based on a survey of 504 enterprises actively running private AI environments, highlights how healthcare organizations are balancing innovation with risk management as AI adoption matures. 

Healthcare Favors Established AI Approaches

The survey reveals that healthcare and life sciences organizations continue to invest heavily in established AI technologies. Traditional machine learning leads adoption at 52%, while computer vision and image processing workloads account for 50% of deployments. Fine-tuned or customized AI models are also gaining traction at 44%. 

These numbers reflect the practical realities of healthcare AI. Machine learning models have long been used to support operational analytics, patient risk scoring, and predictive workflows. Meanwhile, computer vision is already deeply embedded in diagnostic imaging, pathology analysis, and radiology applications.

The report specifically notes that healthcare organizations show “notably lower LLM adoption,” likely due to concerns about “variability of output, hallucinations and regulatory constraints.” That caution is reflected in the survey data, with only 31% of healthcare organizations reporting adoption of RAG-enhanced foundation LLMs, well below financial services at 67% and manufacturing at 57%. 

This gap does not suggest healthcare is falling behind in AI adoption. Instead, it highlights a more selective and risk-aware deployment strategy. Healthcare organizations are focusing AI investments on areas where outcomes can be validated, monitored, and governed effectively.

AI in Healthcare is Expanding Beyond Pilots

Although healthcare organizations are moving cautiously with newer AI approaches, the survey makes clear that AI adoption overall is broadening rapidly across enterprises.

Among all respondents:

• 68% are active with at least three different AI genres 
• 29% are active with at least five AI categories 
• 54% report having an overall AI strategy 
• 49% say AI initiatives are generally well funded 

Healthcare organizations are participating in this broader evolution, particularly as AI use cases expand beyond isolated pilot projects into operational environments.

The report emphasizes that enterprises increasingly view AI as a strategic initiative tied to competitive differentiation, operational efficiency, and customer expectations. In healthcare, this can translate into faster diagnostics, improved clinical workflows, better resource utilization, and more personalized patient experiences.

At the same time, healthcare organizations face a uniquely high burden of responsibility around data governance, explainability, and compliance. This creates additional pressure to ensure AI systems are tightly aligned with organizational controls and infrastructure policies.

Private AI Gains Momentum in Healthcare

One of the strongest themes emerging from the research is the growing importance of private AI infrastructure, also referred to as sovereign AI.

Across all industries surveyed, 81% of organizations say private AI infrastructure they control is critical to their success. The report attributes this trend to concerns around sovereignty, compliance, data proximity, performance, and long-term cost management. 

These priorities are especially relevant in healthcare environments, where organizations must maintain strict oversight of patient records, imaging data, genomic datasets, and other highly regulated information.

Rather than relying entirely on public cloud AI services, many healthcare organizations are adopting hybrid or private AI architectures that allow them to keep data closer to clinical systems and internal governance controls.

The report notes that private AI can also reduce latency and improve throughput by keeping AI applications close to the data they rely on. For healthcare organizations handling large diagnostic images, real-time monitoring systems, or longitudinal patient records, these performance advantages can be significant.

Data Infrastructure is Becoming the Real AI Battleground

While public discussion around AI often centers on GPUs and compute power, the Freeform Dynamics research suggests enterprises increasingly recognize storage and data infrastructure as equally critical. The study found that:

• 57% of organizations prioritize storage performance to avoid AI bottlenecks 
• 54% prioritize compute and GPU availability 
• 52% focus on network bandwidth limitations 

In addition, 86% of respondents recognize that different stages of the AI pipeline require different storage approaches. 

For healthcare organizations, this is particularly important because AI workloads often span multiple data-intensive stages, from data preparation and cleansing to model training and runtime inference. These environments must also support long-term data storage and retention requirements while maintaining compliance, governance, and audit management across the entire AI lifecycle.

Healthcare environments also generate enormous volumes of unstructured data, including imaging files, clinical notes, research datasets, and telemetry streams from connected devices. Managing these diverse datasets efficiently requires infrastructure that can scale while maintaining strong security and resilience. 

The report further found that 91% of organizations running private AI environments rely meaningfully on object storage, with 44% using it extensively. Object storage is increasingly becoming foundational for AI pipelines because it supports scalability, lifecycle management, and the handling of massive data repositories.

Security and Resilience Remain Central Concerns

Healthcare’s cautious AI adoption strategy is also shaped by cybersecurity and operational resilience requirements. The survey found that enterprises place cybersecurity, operational resilience, regulatory compliance, and sovereignty among the most important factors influencing AI storage decisions. 

Respondents also identified concerns around data leakage and unauthorized access, ransomware attacks targeting AI pipeline data, data corruption and integrity issues, and the ability to recover systems and data quickly following an incident. These concerns are amplified in healthcare, where AI failures or compromised data can directly affect patient outcomes and increase regulatory and compliance exposure.

Healthcare organizations must also navigate stringent data privacy and governance requirements, including HIPAA regulations in the United States and GDPR requirements across Europe. As AI initiatives expand across clinical, operational, and research environments, organizations are under increasing pressure to ensure sensitive patient and healthcare data remains secure, auditable, and properly governed throughout the AI lifecycle.

As a result, healthcare organizations are increasingly recognizing that AI infrastructure decisions cannot focus solely on performance. Security, recoverability, and governance must be integrated across the entire AI lifecycle. 

Healthcare’s AI Future will be Data-Centric

The research suggests healthcare organizations are not resisting AI adoption. Instead, they are building toward a more deliberate and sustainable model for operational AI.

Compared with industries such as manufacturing and financial services, healthcare may appear more conservative in adopting large language models and distributed AI systems. However, healthcare’s emphasis on explainability, governance, and trusted data pipelines may ultimately position the sector for more sustainable long-term AI deployment.

The report concludes that organizations with more AI experience tend to adopt more strategic infrastructure planning approaches, prioritize versatile platforms over siloed point solutions, and define storage requirements earlier in the deployment lifecycle. 

For healthcare organizations, these lessons are especially relevant. As AI adoption expands across diagnostics, operations, research, and patient engagement, success will increasingly depend on the ability to operationalize AI securely and at scale. The next phase of healthcare AI will likely be defined less by headline-grabbing models and more by the underlying data infrastructure that enables trusted, resilient, and compliant AI systems.

About Paul Speciale

Paul Speciale is a data storage and cloud industry veteran with over 20 years of experience with small and large companies. Paul is currently the Chief Technology Evangelist and CMO for Scality, leading the team across activities ranging from building awareness to content development and lead generation, as well as being a spokesperson for the company.