HPE Blog, UK & Ireland

How AI in healthcare is transforming medical imaging

Medical imaging is amongst the most promising clinical applications of AI, and its ability to detect and qualify a wide arrange of medical conditions. Medical imaging is fundamental in clinical diagnosis, patient treatment and medical research. Leveraging computer-aided diagnostics can drastically improve accuracy and specificity for the detection of even the smallest radiographic abnormalities.

Medical imaging produces huge datasets, which would traditionally be analysed in real-time by radiologists. However, in the light of a global pandemic, demand is mounting, and backlogs are growing. It is more important than ever that healthcare professionals’ time is spent efficiently. AI has the capability to analyse vast datasets, thousands of times faster than any human; deriving insight that might be missed, or overlooked, by the human eye.

AI-powered medical imaging is proving extremely valuable in cancer detection using quantitative analysis of very fine image alterations. Prostate cancer, the most prevalent neoplasm in men, has seen a notable increase in detection in the last five years – purely by introducing AI technologies to the MRI process.

Other uses of AI in medical imaging include:

  • Identifying cardiovascular abnormalities by measuring the structures of the heart. This progresses something as simple as an x-ray from an initial screening tool, to a more reliable assessment.
  • Detecting fractures and other skeletal injuries. AI can be used to detect hard-to-see fractures or soft tissue injuries, allowing medical professionals to make more confident choices around treatment or surgery. AI radiology uses unbiased algorithms to review imaging in trauma patients.
  • Helping to diagnose neurological diseases. Devastating and degenerative diseases such as ALS remain without a cure, but newly identified biomarkers have improved the accuracy and speed of diagnoses with the help of AI.
  • Spotting thoracic conditions quickly. Diagnosis of conditions such as pneumonia are vitally important to patient outcomes. AI facilitates the immediate analysis of imaging – a proactive approach that reduces the need to wait for radiologists or specialists to become available in busy, and often stretched, healthcare environments.

AI imaging requires wider digital transformation considerations

As with any next generation technology, AI adoption introduces a number of challenges that should be considered as part of the wider digital transformation strategy.

As the use of AI-powered medical imaging broadens and the technology improves, file sizes are becoming larger and as such, archival and on-demand storage needs to adapt to meet demand. With data privacy and sensitivity also a growing concern, AI only exacerbates the need for secure, scalable, compliant storage.

However, justification of capital expenditure on technical infrastructure and solutions can be difficult. All too often healthcare technology professionals are tasked with digital transformation within stringent boundaries – technical, regulatory, budgetary; with the potential for large-scale seamless growth.

The impact of AI on the future of healthcare make the short-term infrastructure enhancements and cultural shift worthwhile. 

HPE partners with Intel, providing powerful AI solutions that deliver insight from machine learning with faster turnaround times; whilst maintaining reduced costs and compliance with privacy regulations. Our experts continue to help healthcare organisations across the globe make their AI and digital transformation a reality. Whilst AI will never remove the need for highly skilled healthcare professionals; it’ll significantly alleviates the day-to-day, human-centric analysis to deliver better patient outcomes.

Read more about how HPE Scalable Storage is helping healthcare providers with medical imaging data.

 Find out more about how HPE is transforming healthcare services.

Jennifer Reilly
Hewlett Packard Enterprise



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Jennifer shares insights and technology trends that are impacting the healthcare and life sciences industries.

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