Hospitals primarily capture medical images using sophisticated and costly equipment like CT or MRI scanners. Operating these machines and interpreting their results necessitates specialized professionals. However, the growing need for medical imaging is outpacing the availability of experts qualified to manage these devices and analyze the data they produce. Consequently, radiologists and other medical imaging specialists are experiencing a significant increase in workload. This escalation can lead to burnout, impacting the sustainability of healthcare delivery and lengthening patient wait times, potentially requiring patients to travel further for essential medical services. In response to this issue, a new initiative is underway to make medical imaging technology more widely accessible. This project intends to leverage artificial intelligence (AI) to enable professionals with less specialization to both acquire and interpret medical images.

A consortium led by Amsterdam UMC (Amsterdam, the Netherlands) is implementing the AI4AI project that seeks to integrate AI into the development of technologies supporting the use of cost-effective and/or portable devices such as ultrasound and ultra-low-field MRI. The objective is to broaden the range of healthcare professionals who can operate imaging devices — including general practitioners, sonographers, and specialist nurses — thus diminishing the reliance on highly specialized experts. The application of AI in this context has the potential to significantly reduce the strain on medical staff and associated costs.

The AI4AI project is expansive, targeting various diseases and medical specialties. It encompasses the analysis of conditions like stroke and brain tumors, visualization and interpretation of organ tissue perfusion during surgery, quantification of fetal biomarkers for detecting pregnancy abnormalities, identification of patients in need of invasive coronary artery treatment or heart disease diagnosis, enhancing workflows in image-guided radiotherapy, prioritizing referrals for urgent care, screening and triaging of severe visual disorders, selecting patients suitable for immunotherapy, and refining imaging processes for assessing orthopedic implants.

"With this project, we want to contribute to bringing medical imaging closer to patients’ living environment and make it more accessible for patients,” said Ivana Išgum, Amsterdam UMC Professor of Artificial Intelligence and Medical Imaging and coordinator of the national consortium implementing the AI4AI project. “In addition, hospital care in developing countries may not always be accessible to everyone. There may also be fewer highly skilled experts available. We also hope to contribute to more accessible healthcare for people in these countries."

"AI technology that can support the creation, interpretation and reporting of medical imaging studies has the potential to shorten waiting lists and reduce workload and perhaps also improve quality,” added Amsterdam UMC Radiologist Nils Planken. “The correct use of diagnostics outside the hospital has the potential to prevent patients from being sent to the hospital, or to sending patients to the hospital in an even more targeted way." 

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