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How doctors can detect more cases of breast cancer earlier

Researchers have developed a model to predict the risk of developing breast cancer within one to five years.

Two blue-toned mammogram images side by side, with red markings around small areas of breast tissue.
The AI model mimics how radiologists work in practice. The X-ray images show how the AI model focuses on the same areas as doctors when looking for potential signs for cancer.
Petter Bjørklund COMMUNICATIONS ADVISER
Presented by: Presented by: UiT The Arctic University of Norway
Published

How likely are you to develop breast cancer later in life?

This question may be answered by artificial intelligence (AI) in the future.

This is shown by Solveig Thrun’s research at the SFI Visual Intelligence, an AI centre at UiT The Arctic University of Norway.

She has developed an AI model that can measure a person's risk of getting breast cancer within one to five years.

Thrun believes it could become a valuable tool in the battle against breast cancer.

Portrait of Solveig Thrun.
"The AI model knows what to look for. In many ways, it imitates how radiologists work in practice," says Solveig Thrun.

A better chance of recovery

Every year, around 5,000 women in Norway develop breast cancer. It is one the most common types of cancer, both in Norway and elsewhere.

Although the cancer is often detected early, it is sometimes diagnosed too late. In the worst cases, it may have spread to other parts of the body, such as the brain, lymph nodes, lungs, or bones.

The AI model could help doctors detect more cases of breast cancer earlier.

“The earlier the cancer is detected, the faster the patients can get the treatment they need. Then they have a better chance of getting healthy again,” says Thrun.

Shorter waiting times until the next examination

The goal is to develop an AI model that can be used in the national BreastScreen Norway programme.

It offers all women in Norway between the ages of 50 and 69 the opportunity to undergo mammography screening every two years. Mammography is an X-ray examination of the breasts.

Women receive the invitation regardless of how high or low their risk of developing cancer may be.

The screening programme is therefore not very personalised. This can be a disadvantage for women with the highest risk.

“Some women might have a risk of getting cancer within a year after their examination. If they return two years later for their next screening, the cancer may have developed and worsened,” Thrun explains.

With the AI model, follow-up care can be tailored to each individual patient according to their level of risk.

“This could lead to shorter follow-up intervals for women at high risk,” the researcher says.

Woman in a white top examining her breast with her hands against a light background.
Around 5,000 women in Norway develop breast cancer each year.

Fewer unnecessary screenings

The model could also help ensure that women at low risk get screened less frequently. This would spare them from undergoing numerous examinations.

"If a woman is invited for screening every third year instead of every second year, she is exposed to less X-ray radiation. It's also reassuring to know that you don't have to be screened as often," says Thrun.

The AI model can analyse the images in just a few seconds. Thrun says this could give doctors more time to help patients.

"This means healthcare professionals can spend more time following up and treating women with the highest risk," she says.

The model 'thinks' like a radiologist

The AI model has been trained on thousands of X-ray images of breasts. This enables it to recognise what breasts look like and what may be signs of breast cancer.

It measures the risk by comparing X-ray images from previous examinations with images from the most recent screening.

In this way, it can identify how the breast tissue has changed, just as doctors do.

"The AI model knows what to look for. In many ways, it imitates how radiologists work in practice," says Thrun.

Portrait of Solveig Hofvind
Solveig Hofvind, head of BreastScreen Norway, believes Thrun's research is very promising.

Cancer Registry: Very promising

The project is a collaboration with the Cancer Registry of Norway at the Norwegian Institute of Public Health (NIPH), which heads BreastScreen Norway.

Solveig Hofvind is head of BreastScreen Norway and also holds a part-time professorship in radiography at UiT. She believes Thrun's research is very promising.

"It will be very exciting to follow this research," she says.

Ingrid Stenstadvold Ross, general secretary at the Norwegian Cancer Society, also believes that this type of AI research will become important in the future.

"AI will without a doubt be an important tool for diagnosing cancer," says Ross.

Thorough testing is important

Both Hofvind and Ross say that there is still a long way to go before these models can be put into use.

This is to ensure that they are properly tested and fulfill strict health, environmental, and safety requirements.

Portrait of Ingrid Stenstadvold Ross.
"AI is meant to be a tool that makes breast cancer screening better and safer, not a shortcut that goes on the expense of its quality," says Ingrid Stenstadvold Ross.

"We need to be absolutely sure that AI models provide women with a service that is at least as good as, or better than, what is currently offered. They must also be CE-marked before they can be used in BreastScreen Norway," Hofvind explains.

"The technology is developing rapidly. There is a significant need for thorough testing before implementation and continuous evaluation after AI models are put into use," says Ross.

AI will not replace doctors

Many people are worried that AI will take over doctors' jobs. Could Thrun's model eventually replace healthcare professionals?

Not at all, says the researcher.

"The point of the model is to help doctors. Just like humans, AI can also make mistakes. There will always be a need for doctors who can examine and assess the X-ray images," says Thrun.

"AI is meant to be a tool that makes breast cancer screening better and safer, not a shortcut that goes on the expense of its quality," says Ingrid Stenstadvold Ross.

References:

Sun et al. From Exams to Trajectories: Neural Controlled Differential Equations for Longitudinal Mammography Vision-Language Pretraining (Abstract), 29th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2026), 2026.

Thrun et al. Longitudinal Multi-View Breast Cancer Risk Prediction (PDF), 29th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2026), 2026.

Thrun et al. Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction (PDF), 28th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2025), 2025.

SFI Visual Intelligence

  • A centre for research-based innovation (SFI) that aims to devlop new deep learning methods for extracting important information from different and complex image data.

  • The centre aims to develop better AI tools, for example to detect heart diseases and cancers, surveil and detect natural resources, and monitoring the environment, climate, and potential natural disasters.

  • The centre has an interdisciplinary approach. Methodologies developed for a particular research area can be often be applied to other disciplinary areas.

  • The centre's research partners are UiT – The Arctic University of Norway, the University of Oslo, and the Norwegian Computing Center.

  • The centre also consists of a consortium of user partners, such as the University Hospital of Northern Norway, the Cancer Registry of Norway, Equinor, Institute of Marine Research, GE Vingmed Ultrasound, Kongsberg Satellite Services, and Aker BP.

  • Visual Intelligence is one of two SFI centres at UiT. The other one is Dsolve.

Read more about Visual Intelligence here.

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Read the Norwegian version of this article on forskning.no

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