The rise of AI and data-driven insights has brought significant transformation to medical research and patient care. In the life sciences, from pharmaceutical manufacturers to biotechnology companies to genomics researchers, we’re seeing unprecedented breakthroughs thanks to largescale data analysis. Healthcare providers, in turn, are rethinking care delivery, offering more personalized medical treatment and disease prevention. Increasingly, these sectors are merging, as data-driven research in the life sciences influences and shapes direct patient care.
The life science and healthcare ecosystem is responsible for a deluge of data that’s increasing exponentially every year. Sources can include:
- Data from genomics, proteomics and other omics technologies
- Clinical data from provider notes and lab results
- Medical IoT data from patient monitoring devices
- Medical imagery such as MRI or CT scan data
In fact, about 30% of the world’s data volume is now being generated by the healthcare industry.[1] To extract value from all that data, life science and healthcare companies will need a way to make sense of it. Many organizations in these sectors are investing in data and AI, and the global healthcare AI market is growing rapidly. For example, roughly 85% of surveyed biopharma executives say they’re planning to invest in data, digital and AI this year for research and development (R&D) as well as for supply chain resiliency.[2]
AI is enabling a wide variety of use cases, including disease detection, personalized patient care and drug discovery. To support these AI initiatives, life science and healthcare companies need robust IT infrastructure that delivers the necessary performance while protecting sensitive data—from high-performance computing (HPC) to lightning-fast storage to private connectivity solutions.
Three prominent use cases for AI in the life sciences and healthcare
AI offers enormous promise for the advancement of medical research and improvement of health outcomes. We’re already seeing important breakthroughs, and we’ve only scratched the surface of its potential. Let’s explore three use cases where AI is accelerating progress in the life sciences and healthcare.
Early detection of disease
AI is helping clinicians make the right diagnosis faster and provide treatment interventions sooner. It provides several capabilities that accelerate disease detection:
- Genomic analysis to detect genetic markers of disease
- Predictive modeling to prevent and manage contagious disease outbreaks
- Medical image analysis that goes beyond what the human eye can see
- Monitoring patients through wearable devices that track vital signs and detect irregularities
Consider the story of Harrison.ai, a Sydney-based medical technology company that’s using AI to make early detection solutions available to clinicians. By applying AI to chest X-ray images, Harrison.ai is helping providers speed up the diagnosis of cancers and other critical illnesses. They rely on infrastructure from NVIDIA, hosted in an Equinix data center, to accelerate development of their AI-powered solutions. This private AI deployment resulted in 8x the data processing capability and reduced training of their models from months to days. The solution led to a greater than 45% improvement in accuracy of diagnoses.
Personalized patient care
As is the case in many industries, AI is enabling more personalized experiences in healthcare. This includes capabilities like:
- Personalized drug therapies and other treatments—often referred to as “precision medicine”
- Tailored treatment plans that factor in patient data, medical history, genetic info, lifestyle factors and more
- Improved diagnostics and imaging
- Better prognostic predictions
- Analysis of unstructured data like clinical notes and medical literature—which constitutes around 80% of healthcare data
To deliver personalized healthcare, organizations must be able to ingest and aggregate data from many sources. Often, they share data across institutions to accelerate progress. Take, for example, the Children’s Cancer Institute in Australia. To improve outcomes for children with cancer, they needed to exchange data with other cancer research institutions around the world. This required an AI architecture that both protects their sensitive data and enables secure access to clouds and partners to streamline collaboration. They enable this solution by storing their genomic data at Equinix, adjacent to the clouds, and then sharing their analysis with their research partner across a private, high-performance interconnection solution.
Drug discovery and development
Traditionally, drug discovery and development are time-consuming, costly processes. With AI, these processes are not only faster and less expensive; the AI-discovered drugs can also have better success rates in early clinical trials.
Pharma companies are applying AI to drug development in several ways:
- Identifying novel proteins and genes that can be targeted for treatment
- Generating drug molecules and optimizing existing drug compounds
- Screening and evaluating compound libraries for potential drug candidates
- Optimizing clinical trials
- Streamlining manufacturing processes to accelerate drug development time
One Equinix customer, a major global pharmaceutical company, needed to analyze vast quantities of data to identify promising drug targets, optimize clinical trials and better understand patient responses to their medications. With the goal of bringing new treatments to market faster and more efficiently, they invested in AI infrastructure at Equinix to help them accelerate drug R&D at more affordable costs by more quickly eliminating compounds that don’t have potential. This included a turnkey AI solution that eliminates GPU constraints while providing predictable costs and builds AI capacity at Equinix.
AI challenges in the life sciences and healthcare, and how to tackle them
Data management for life science and healthcare companies is complex. Fragmented data across disparate systems can lead to flawed results. Therefore, it’s crucial for organizations in this sector to have the right data strategy to address the data ingestion, aggregation and storage requirements of their AI initiatives. To do this, they’ll need not only high-performance storage hardware but a strategy for cleaning data and integrating various data sources.
Data privacy concerns are also significant in healthcare and the life sciences. Companies often work with patients’ sensitive personal information, and they’re subject to strict regulatory requirements. Protecting against data breaches is essential. A private AI approach can alleviate many of these issues since it allows companies to use private infrastructure for their AI workloads. If you do this in a cloud-neutral location where you still have access to leading clouds, network services, SaaS solutions and more, you won’t be sacrificing fast, reliable, secure connectivity to key business partners.
For life sciences institutions where latency is a primary concern due to real-time application performance requirements, doing AI inference in edge locations makes a big difference. Thus, you need a platform of data centers that’s in all the right places for your business.
The best place to get AI-ready
Because of the huge volumes of data generated in healthcare and the drive to extract value from that data to improve patient care, companies in the industry need scalable infrastructure that can keep up with data growth. How are you going to take advantage of your data to access insights that advance medical research and healthcare? You can start by putting your AI infrastructure in a high-performance data center that’s AI-ready.
Equinix data centers have the global reach to support AI initiatives by worldwide research institutions. With approximately 2,000 network service providers on our platform, you can ingest and aggregate data for AI analysis. We offer direct, private connectivity to a large ecosystem of clouds and other service providers, as well as business partners, so you can collaborate and share datasets seamlessly. More than half of the life science and healthcare companies in the Fortune 500 are deployed at Equinix. In our data centers, you can deploy dedicated infrastructure to support data privacy in facilities with robust physical security measures.
The potential of AI in healthcare and the life sciences is enormous. Learn more about preparing for your AI future with AI-ready infrastructure.
[1] The healthcare data explosion, RBC Capital Markets.
[2] Bill Coyle, Rahul Pathak, Raluca Cenusa and Divyata Manvati, Breakthroughs or bottlenecks? Pharma industry outlook, trends and strategies for 2025, ZS, January 21, 2025.
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