AI research at KI paves the way for more targeted and globally equal healthcare

“At the moment, AI is being used where it’s least needed,” he says. “We’ve inverted that perspective and taken it to environments that lack the necessary expertise and resources.” 

Professor Lundin is currently collaborating with Kenya and Tanzania, where there is a desperate need for pathologists. 

“In Sweden, we think we’re in trouble with our 30 to 40 pathologists per million population, but in sub-Saharan Africa, there are fewer than one per million.” 

He hopes to use AI and digital technology to spread access to image-based diagnostics. The components for digitising samples derive from the mobile phone industry and data can easily be transmitted elsewhere. 

“The one assessing the microscope images could be sitting in another city, or even country,” he says. 

Screening of 400 million women

In another project, researchers are working with cervical cancer. In the Nordic region, the disease is prevented through screening and vaccination, but in many low-income countries, it is the most common cancer-related cause of death amongst women. 

The World Health Organisation (WHO) has set a target to have 70 per cent of women of screening age tested by 2030. 

“To reach it, 400 million more women need to be screened globally, which will be hard to achieve without automated methods.”

Researchers have been working with staff to screen over 3,000 women at Kenya’s Kinondo Hospital, and a further 600 women in primary care in Tanzania. A nurse takes swabs from the ectocervix, after which the cell sample is placed on a microscope slide, prepared and digitised. AI then analyses the sample and an experienced pathologist verifies the AI’s response remotely. 

Here, the researchers could show that the AI was no less accurate than an expert. They have also analysed samples for the presence of the virus that causes cervical cancer. In many countries, this is the primary way of screening for the diseases, and the women who test positive need to provide supplementary tissue samples.

“But in regions where 25–30 per cent of the women are positive, this is difficult to implement – in which case AI-mediated sample analysis can be a ‘middleman’ that takes some of the pressure off the system,” says Professor Lundin. 

Finds parasites in seconds

Intestinal parasites spread via the soil form a group of “neglected diseases”, despite the fact that some 1.5 billion people around the world are carriers, and 20 to 30 per cent of children in some regions are actually infected. The infections are treated with deworming preparations that are taken as a one-time pill, which has led to mass-treatment. Repeated treatment, however, can give rise to drug resistance. 

“Our method makes it possible for doctors to only treat those who actually become re-infected after the first treatment,” he says.

The researchers tested faecal samples from 2,500 schoolchildren. When a microscopist analyses the samples on the hunt for worm eggs, it takes 10 to 15 minutes per slide; for AI, it takes a matter of seconds.

“And in more than ten per cent of the cases, AI found eggs that the human expert had missed,” says Professor Lundin. “There could be just one or two eggs per slide, so it’s like looking for a needle in a haystack.” 

One challenge is that samples need preparing, as reagents can vary between production batches and laboratories. 

“AI is like a student,” he continues. “If the samples look different from one occasion to the next, the AI is less effective.”

Because of this, the AI model needs to be adapted to local conditions through standardised routines and quality controls, as the researchers described in The British Medical Journal in 2025. 

“When AI is introduced locally, the model should be adjusted by manually quality assuring the first 50 to 100 samples,” explains Professor Lundin. 

But a few years down the line, this could be history. 

“Maybe we won’t need to dye preparations like we do today. We’ve had to do this to help the human eye but AI might be able to detect anomalies without it.” 

Professor Lundin stresses that AI has the potential to reduce global inequality if used responsibly in a trust-building and educational way. 

“We’ve seen a great deal of support for these developments in low-resource environments, especially given the lack of experts. AI can’t just be high-tech for rich countries, as the greatest need is in low-resource countries.” 

Support for personalised medication

While AI is often used to find anomalies in different kinds of image, it can also analyse data to find medically relevant patterns, such as a subgroup of patients standing out for reacting particularly well – or badly – to a certain drug. 

Helga Westerlind, docent of epidemiology at Karolinska Institutet’s Department of Medicine in Solna, is developing AI models to search for such new patterns in an extensive bank of data. 

In one project, she is focusing on patients with the autoimmune disease rheumatoid arthritis. On receiving a diagnosis, most patients are given the immunosuppressant methotrexate – despite the fact that a third of them need to go off the drug within a year due to its ineffectiveness or side-effects, and that other therapeutic options are available.