Global Intelligent Hospital Beds Market Size, Growth Drivers & Insights to 2034

According to a newly published market research report by 24LifeSciences, global intelligent hospital beds market is valued at USD 2.54 billion in 2026 and is projected to reach USD 4.92 billion by 2034, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period.

Intelligent hospital beds, a cornerstone of modern healthcare infrastructure, represent a significant evolution from traditional medical beds by integrating advanced technologies within the Internet of Medical Things (IoMT) ecosystem. These smart beds are equipped with sensors that continuously monitor critical patient vital signs including respiration, pulse, and body temperature, transmitting real-time data to central monitoring stations and electronic health records. This capability enables healthcare professionals to perform remote patient monitoring and respond proactively to changes in patient conditions, enhancing both safety and clinical outcomes while addressing staffing efficiency challenges.

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Genomics software and digital platforms are transforming how complex genetic data is handled, interpreted, and used in real-world settings. Instead of letting huge data sets sit in silos, these systems make the information usable and scalable for labs, clinicians, researchers, and healthcare teams who need to turn raw sequences into meaningful insights. At the core of this space are tools that support bioinformatics workflows, allowing scientists to run pipelines that are repeatable, traceable, and easier to manage without constant manual intervention.

Modern genomics platforms bring together variant interpretation engines powered by AI, secure data pipelines that handle high throughput sequencing results, and interfaces that help different teams collaborate without drowning in spreadsheets. When genomic platforms are built with enterprise-grade design, they don’t just process data — they help organizations scale, adapt, and integrate with other systems like electronic health records or research databases.

In addition to core bioinformatics, successful genomic solutions often tie into broader development patterns such as SaaS and enterprise app development. This means the same platform can serve multiple groups, whether it’s a research lab running cohort studies, a clinic interpreting patient genomes, or a bio-tech startup exploring new treatments. Scalability, interoperability, and secure architecture are essential.