August 20, 2025
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August 20, 2025

100,000 Whole-Body Scans Reveal Why Early Imaging Is the Future of Preventive Care

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100,000 Whole-Body Scans Reveal Why Early Imaging Is the Future of Preventive Care

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The UK Biobank’s milestone of 100,000 full-body MRI scans proves what Ezra champions: early imaging at scale doesn’t just detect disease, it rewrites the timeline for preventive care. These results show that what works on a population level can and should be mirrored personally: through proactive, comprehensive scanning.

Key Stats at a Glance

  • Scale milestone: 100,000 participants scanned via full-body MRI in the UK Biobank study1,2
  • Scan scope: more than 1 billion total images of the brain, heart, other organs
  • Diseases targeted early: Cancer, arthritis, and heart disease3,4
  • AI-enabled insights: Almost 17,000 published using UK Biobank data
  • Repeat value: All participants are invited to rescan every few years

Why 100,000 Whole-Body Scans Matter

Earlier this summer, the UK Biobank reached a landmark in medical research: scanning 100,000 people using advanced imaging methods5.

Scanning methods used:

The huge amount of data from this study is available to scientists around the world who are using it to gain greater insights into the early emergence of different diseases. These insights will ultimately provide radiologists and healthcare providers with a greater ability to detect diseases like cancer at earlier, less-serious stages6.

Access to large datasets gives scientists more information from which they can draw meaningful conclusions. Using a small dataset is like looking at a blurry, low-resolution image; you might get a general idea of what it is, but the details are lacking. A large dataset is like switching to high resolution. The details become sharp, and the picture makes more sense.

This article will outline the scale and impact of this study and what it means for you in terms of proactive healthcare.

What the UK Biobank Study Investigated 

The scale and scope of the imaging project 

Since 2014, UK Biobank researchers have been scanning participants’ brains, hearts, livers, joints, and more, collecting over a billion anonymised images. The process was intensive, and included7:

  • Data collection over 11 years.
  • Five types of imaging.
  • 5-hour appointments per participant.
  • Imaging across four sites in England.

Furthermore, the participants are invited back every few years to map changes over time.

Diseases detected earlier with imaging and AI

By combining high-resolution imaging with artificial intelligence (AI), the project has identified early indicators of dementia, liver disease, psychiatric conditions, and heart anomalies. AI tools can analyse vast amounts of data in minutes, spotting patterns that would take human radiologists years to process.

Real-world impact on research and diagnosis

The biobank has already powered research that has contributed to nearly 17,000 peer-reviewed papers worldwide. The research has been used to 

  • Predict the onset of age-related diseases8,9.
  • Spot dementia early to enable proactive healthcare choices10.
  • Understand the causes of cardiovascular and liver disease3.
  • Explore the physiological relationship between the heart and the brain4.
  • Understand the impact of alcohol on the brain11.

From Population to Personal: What This Means for You 200

Why wait for symptoms? 

Many serious conditions, including some cancers and cardiovascular disease, are silent in their early stages. By the time they emerge, treatment options can be limited. Imaging before illness strikes allows health mapping: creating a detailed reference point to compare against future scans, making changes easier to spot early on12.

The case for proactive scanning 

“Fix the roof while the sun is shining” is a good template for a proactive approach to healthcare. Having a scan when you feel healthy can spot problems before they become serious, allowing you to take preventive steps to stop issues from worsening13,14. Many conditions have a better prognosis when detected early, and more treatment options are available. Understanding your baseline health can provide peace of mind and help in spotting future problems.

Ezra’s role in the early detection ecosystem

While research from the UK Biobank is driving new insights into the early detection of disease, Ezra is already using a preventative approach. We offer AI-assisted full-body MRI scans covering 13 organs in as little as 30 minutes, with results reviewed by an expert radiologist.

How Advanced Imaging Unlocks a New Era in Prevention 200

What MRI sees that other scans miss 

MRI uses no radiation and captures high-resolution images of soft tissue, ideal for detecting early signs of disease that don’t show symptoms. MRI can spot a range of issues in multiple organs, including early signs of cancer15–18.

AI’s game-changing role in image interpretation

Ezra uses AI to assist radiologists, speeding up and enhancing images to help spot potential anomalies faster and more accurately. AI is increasingly becoming an indispensable tool for assessing medical imaging19–21, as demonstrated in the UK Biobank study, and the UK government has recently outlined a roadmap to increase the use of AI in hospitals to drive more effective medicine.

Repeat imaging: a health timeline, not a snapshot 

The UK Biobank re-scans participants every few years, creating a long-term health record. Ezra helps you do the same, on a personal level. By tracking subtle changes year on year, you too can build a health timeline.

Summary: A Blueprint for Proactive Health

The UK Biobank’s 100,000-person imaging milestone shows what’s possible when advanced scanning is implemented over the long term, even in people without any symptoms: it provides detailed insights into how the body functions and helps early detection and disease prevention. This isn’t just a win for science, it's a blueprint for our personal health.

If you want to be proactive about your health, why not book an Ezra full-body MRI? Our annual scan catches potential cancer earlier by leveraging AI in the screening process, making it more efficient, affordable, and faster.

Understand your risk for cancer with our 5 minute quiz.

Our scan is designed to detect potential cancer early.

References

1. The world’s largest imaging project reaches milestone of 100,000 scans. UK Biobank. July 15, 2025. Accessed August 5, 2025. https://www.ukbiobank.ac.uk/discoveries-and-impact/major-achievements/the-worlds-largest-imaging-project-reaches-milestone-of-100000-scans/ 

2. Bourigault E, Jamaludin A, Hamdi A. UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentation. Published online April 9, 2025. doi:10.48550/arXiv.2504.06908 

3. Jackson E, Dennis A, Alkhouri N, et al. Cardiac and liver impairment on multiorgan MRI and risk of major adverse cardiovascular and liver events. Nat Med. 2025;31(7):2289-2296. doi:10.1038/s41591-025-03654-2 

4. Zhao B, Li T, Fan Z, et al. Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images. Science. Published online June 2, 2023. doi:10.1126/science.abn6598 

5. The world’s largest imaging project reaches milestone of 100,000 scans. UK Biobank. July 15, 2025. Accessed August 13, 2025. https://www.ukbiobank.ac.uk/discoveries-and-impact/major-achievements/the-worlds-largest-imaging-project-reaches-milestone-of-100000-scans/ 

6. Major achievements. UK Biobank. July 15, 2025. Accessed August 13, 2025. https://www.ukbiobank.ac.uk/discoveries-and-impact/major-achievements/ 

7. Foster PJ, Atan D, Khawaja A, et al. Cohort profile: rationale and methods of UK Biobank repeat imaging study eye measures to study dementia. BMJ Open. 2023;13(6):e069258. doi:10.1136/bmjopen-2022-069258 

8. Ji MX, Thanaj M, Nehale-Ezzine L, Whitcher B, Thomas EL, Bell JD. Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank. Published online April 17, 2025:2025.03.16.25323714. doi:10.1101/2025.03.16.25323714 

9. Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic. NeuroImage: Clinical. 2022;36:103273. doi:10.1016/j.nicl.2022.103273 

10. Li A, Lian J, Vardhanabhuti V. Multi-modal machine learning approach for early detection of neurodegenerative diseases leveraging brain MRI and wearable sensor data. PLOS Digital Health. 2025;4(4):e0000795. doi:10.1371/journal.pdig.0000795 

11. Daviet R, Aydogan G, Jagannathan K, et al. Associations between alcohol consumption and gray and white matter volumes in the UK Biobank. Nat Commun. 2022;13(1):1175. doi:10.1038/s41467-022-28735-5 

12. Why is early cancer diagnosis important? Cancer Research UK. April 2, 2015. Accessed August 13, 2025. https://www.cancerresearchuk.org/about-cancer/spot-cancer-early/why-is-early-diagnosis-important 

13. Kwee RM, Kwee TC. Whole‐body MRI for preventive health screening: A systematic review of the literature. J Magn Reson Imaging. 2019;50(5):1489-1503. doi:10.1002/jmri.26736 

14. Goehde SC, Hunold P, Vogt FM, et al. Full-Body Cardiovascular and Tumor MRI for Early Detection of Disease: Feasibility and Initial Experience in 298 Subjects. American Journal of Roentgenology. 2005;184(2):598-611. doi:10.2214/ajr.184.2.01840598 

15. MRI for Cancer | Magnetic Resonance Imaging Test. Accessed June 2, 2025. https://www.cancer.org/cancer/diagnosis-staging/tests/imaging-tests/mri-for-cancer.html 

16. Eggen AC, Wind TT, Bosma I, et al. Value of screening and follow‐up brain MRI scans in patients with metastatic melanoma. Cancer Med. 2021;10(23):8395-8404. doi:10.1002/cam4.4342 

17. Morrow M, Waters J, Morris E. MRI for breast cancer screening, diagnosis, and treatment. The Lancet. 2011;378(9805):1804-1811. doi:10.1016/S0140-6736(11)61350-0 

18. Murphy G, Haider M, Ghai S, Sreeharsha B. The Expanding Role of MRI in Prostate Cancer. American Journal of Roentgenology. 2013;201(6):1229-1238. doi:10.2214/AJR.12.10178 

19. Gore JC. Artificial intelligence in medical imaging. Magn Reson Imaging. 2020;68:A1-A4. doi:10.1016/j.mri.2019.12.006 

20. Fan H, Luo Y, Gu F, et al. Artificial intelligence-based MRI radiomics and radiogenomics in glioma. Cancer Imaging. 2024;24(1):36. doi:10.1186/s40644-024-00682-y 

21. Frizzell TO, Glashutter M, Liu CC, et al. Artificial intelligence in brain MRI analysis of Alzheimer’s disease over the past 12 years: A systematic review. Ageing Res Rev. 2022;77:101614. doi:10.1016/j.arr.2022.101614