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Beyond the human eye – AI’s groundbreaking role in medical image recognition

Haaga-Helia is a partner in FAIR, actively creating the formation of this frontier entity, as well as offering companies a wide range of services to support them in their development.


Elisa Laatikainen

Haaga-Helia ammattikorkeakoulu

Teemu Moilanen

Haaga-Helia ammattikorkeakoulu

Published : 20.12.2023

One of the most crucial elements of the healtcare sector is medical imaging. Doctors and Radiologists around the world utilize imaging such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and Ultrasound daily to collect vital information about their patients, enabling them to make correct diagnoses and treatment plans in critical situations. (Graff & Sidky 2015)

In recent decades, advances in medical imaging machines have revolutionized humanity’s ability to gather information about the inner workings of a human body (Open Medscience). With access to this important information, medical professionals have been able to detect anomalies and provide better care to their patients at much earlier stages than ever before.

Whilst scans are valuable in content, they do not come without their challenges. A single scan can contain hundreds of individual images, with crucial patient data hidden in grayscale (Ge et al. 2022). The interpretation of such images requires a high level of expertise, and still the correct processing and analysis of the materials takes time. In many societies, this has led to long patient wait times, even backlogs of imaging (Omofoye et al. 2023). It also doesn’t help that, for example, cardiovascular diseases (diagnosed with medical imaging) show ever-increasing numbers in the statistics (Roth et al. 2021).

Redifining Radiology: AIATELLA

Just like CT and MRI technology in their day, Artificial Intelligence (AI) is currently revolutionizing the field of healthcare. AI’s ability to process massive amounts of data offers the healthcare industry unprecedented potential to transform the way patients are helped (Berry 2023). By processing images in a fraction of the time it takes human eye to (Butrym 2023), AI can help doctors and radiologists speed up the detection of medical anomalies. In addition to expediting this vital point of medical care on an individual level, it also helps the healthcare industry to speed up processes, eliminate backlogs and deliver treatment to patients faster.

One of the companies harnessing this power into pioneering products is AIATELLA, developing an AI solution for analyzing CT, MRI, and ultrasound images of cardiovascular diseases. AIATELLA’s software, which has already been successfully piloted with the iconic healthcare institution NHS (National Health Service, United Kingdom), uses AI to identify cardiovascular diseases from huge datasets, alerting medical professionals of anomalies with amazing accuracy and speed. AIATELLA goes ones step further, not only do they identify disease, but they quantify them. This providides doctors with everything they need to make a fast and accurate diagnosis. AIATELLA’s mission is to support doctors in providing precision medical care to their patients by being able to process significant amounts of image data at an unprecedented speed and accuracy.

Finnish AI Region FAIR (EDIH) and Haaga-Helia supporting companies in their development needs

Although the AI revolution currently offers unprecedented opportunities for medical technology (MedTech), there are also challenges that must be overcome in order to develop these products into their final form. Especially in MedTech, an algorithm must be developed with high precision, trained on massive amounts of data, and validated for medical use, just to name a few elements that must be addressed to complete the development process. Here, organizations with a societal focus can play an important role, offering support and resources that facilitate these laborious undertakings.

Enter the Finnish AI Region FAIR, a newly formed European Digital Innovation Hub (EDIH), boosting the development of AI based solutions and processes. EDIHs are European Commission’s flagship initiatives, supporting SMEs in creating new digital solutions that change the global landscape. Haaga-Helia is a partner in FAIR, actively creating the formation of this frontier entity, as well as offering companies a wide range of services to support them in their development. In addition to providing insights into data collection and algorithms, management consultancy and business development are also included in Haaga-Helia’s services.

AIATELLA’s collaboration with FAIR supports them with valuable resources in addition to AIATELLA’s own development team.

Collaboration with FAIR came at a perfect time, as it can help us optimise our development. This ultimately allows us to bring the benefit of our AI to patients faster.
– AIATELLA’s CEO and founder Jack Parker

The service at Haaga-Helia relies on an individual approach and the support of a nominated customer manager. The expertise is customised based on the situation of each respective company, ensuring the right development tools, specific to the company needs. The service and expertise is based on a novel concept of a comprehensive needs analysis, leading up to a tailored roadmap of support and services.

In the realms of image recognition, AI marks a significant leap in medical diagnostics. Companies like AIATELLA are at the forefront of this change, and we at FAIR, take immense pride in supporting their mission to leverage AI for better patient care, and better future.

Finnish AI Region FAIR is a European Digital Innovation Hub (EDIH), one of European Commission’s flagship initiatives to revolutionize the digitalisation of European companies. FAIR concentrates in supporting companies utilize AI, helping them make a leap in productivity and speeding up their AI processes by a decade. Haaga-Helia University of Applied Sciences is a proud member of FAIR.

With FAIR, you become the future.


Ge, Y., Zhang, Q., Sun, Y., Shen, Y., Wang, X. 2022. Grayscale medical image segmentation method based on 2D&3D object detection with deep learning. BMC Medical Imaging.

Graff, C. & Sidky, E. 2015. Compressive sensing in medical imaging. National Library of Medicine.

Omofoye, T., Vlahos, I., Marom, E., Bassett, R., Blasinska, K., Ye, X., Tan, B., Yang, W. 2023. Backlogs in formal interpretation of radiology examinations: a pilot global survey. Science Direct.

Editing: Marianne Wegmüller

Picture: AdobeStock