The way biomedical research is conducted has changed dramatically over the last decade. At the center of this transformation is cloud computing, a technology that has quietly become one of the most powerful tools available to scientists, clinicians, and healthcare institutions worldwide.

What is cloud computing, and why does it matter in biomedicine?

Cloud computing can be understood simply as the delivery of computing services such as storage, processing power, databases, networking, on demand over the internet without requiring local infrastructure. Instead of a hospital needing to buy and maintain its own supercomputers, it can simply rent the resources it needs from providers like AWS, Google Cloud, or Microsoft Azure.

In biomedicine, this matters enormously. A recent 2024 review published in Heliyon by Sachdeva et al. describes how cloud computing has become a transformative force in healthcare, offering scalable, on-demand resources for managing the vast amounts of data that modern medicine generates [1]. From electronic medical records to genome sequencing, the data volumes involved are simply too large for traditional local infrastructure.

Key applications in biomedical research

1. Genomics and bioinformatics

One of the most exciting applications is in genomics. Sequencing a single human genome produces gigabytes of raw data. Analyzing thousands of genomes, as modern studies require,
demands enormous computational power. Cloud platforms make this feasible for research groups that could never afford dedicated supercomputing clusters.

The genomic data scale problem

1 genome
~200 GB raw data
1,000 genomes
~200 TB — local server limit
NIH All of Us
1+ PB — cloud only

Traditional local servers max out around 10–50 TB. Cloud platforms scale to petabytes on demand.

Sachdeva et al. [1] highlight that scientists are increasingly using cloud computing to integrate data from systems biology, data mining, and genomics to solve complex biomedical challenges. Navale & Bourne [2] further note that biomedical research has become a digital data-intensive endeavor, relying on secure and scalable computing that has traditionally been purchased and maintained locally, a burden that cloud computing now eliminates.

Platforms like the NIH’s STRIDES initiative and Terra (built on Google Cloud) allow researchers to analyze petabyte-scale genomic datasets collaboratively, without moving the data at all, bringing the analysis to the data rather than the other way around.

2. Electronic medical records and telemedicine

Cloud infrastructure has enabled a new generation of electronic medical record (EMR) systems that are accessible from anywhere, shareable across institutions, and far more resilient than on-premise alternatives. During the COVID-19 pandemic, this proved critical: telemedicine platforms scaled rapidly on cloud infrastructure to handle an unprecedented surge in remote consultations.

3. Personalized medicine and AI

Perhaps the most exciting frontier is the combination of cloud computing with artificial intelligence. Cloud platforms provide both the storage for training data and the GPU resources needed to train large models. This is enabling personalized treatment recommendations, AI-assisted diagnosis from medical imaging, and predictive analytics for disease risk, all at a scale that was impossible just ten years ago. As Navale & Bourne [2] observe, cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems.

Challenges and ethical concerns

Cloud computing in healthcare is not without risks. The same 2024 review [1] highlights that security risks associated with storing health information, particularly genetic data, in the cloud can pose considerable threats to both patients and healthcare organizations if not properly managed. Privacy and data sovereignty concerns are especially complex, as regulations differ significantly across countries.

Key challenges include:

  • Data privacy: Who owns patient data stored on commercial cloud servers?
  • Cybersecurity: Cloud systems are attractive targets for ransomware and data breaches.
  • Regulatory compliance: Healthcare data is subject to strict regulations (HIPAA in the US, GDPR in Europe) that cloud providers must accommodate.
  • Vendor lock-in: Dependence on a single cloud provider can be risky for institutions.

Why this matters

Cloud computing is not just a technical convenience, it is fundamentally democratizing biomedical research. A small research group at a university in a developing country can now access the same computational power as a large pharmaceutical company. Collaborative platforms allow scientists across the world to work on shared datasets in real time.

As Sachdeva et al. conclude, cloud computing’s role in healthcare will only grow as data volumes increase and AI becomes more deeply embedded in clinical practice [1]. Understanding this technology, its capabilities and its risks — is increasingly essential literacy for anyone working in the life sciences.

References

[1] Sachdeva, S., Bhatia, S., Al Harrasi, A., Shah, Y. A., Anwer, K., Philip, A. K., Shah, S. F. A., Khan, A., & Ahsan Halim, S. (2024). Unraveling the role of cloud computing in health care system and biomedical sciences. Heliyon, 10(7), e29044. https://doi.org/10.1016/j.heliyon.2024.e29044

[2] Navale, V., & Bourne, P. E. (2018). Cloud computing applications for biomedical science: A perspective. PLOS Computational Biology, 14(6), e1006144. https://doi.org/10.1371/journal.pcbi.1006144