Noul’s AI-powered malaria diagnostics gains global attention after US clinical validation

As climate change and global mobility reshape the epidemiological landscape, malaria is no longer a concern exclusive to the developing world. Rising travel-related cases and rare incidents of local transmission have prompted renewed urgency in improving diagnostic readiness, even in high-income countries like the U.S.

In this context, U.S.-based diagnostics leader Labcorp has validated the clinical performance of miLab MAL, a next-generation AI-powered malaria diagnostic platform developed by Korean biotech company Noul.

The study, published in the Journal of Clinical Microbiology, found that the tool achieved 100 percent sensitivity and specificity in identifying malaria from 408 blood samples collected at Labcorp’s reference laboratories.

Outperforming experts: US study highlights AI’s diagnostic precision

Dr. Sanjai Nagendra, Medical Director at Labcorp’s Center for Esoteric Testing and Atlantic Regional Laboratories, says AI-powered platforms like miLab MAL could help close diagnostic gaps in both high- and low-resource settings. (Photo courtesy of Dr. Sanjai Nagendra)

In an online interview with Korea Biomedical Review, Dr. Sanjai Nagendra, Medical Director of the Center for Esoteric Testing and Atlantic Regional Laboratories at Labcorp, stressed that miLab MAL detected two positive cases that had been missed by seasoned microscopy experts -- including pathologists -- both of which were later confirmed to have extremely low parasitemia levels, under 0.1 percent.

“This platform detected malaria in two cases that even highly trained professionals overlooked," Nagendra said. “That shows miLab MAL is more sensitive than conventional microscopy. And it didn’t misidentify other blood cell artifacts either, which speaks to its high specificity.”

The study compared miLab MAL’s AI-driven diagnostic results against those of traditional microscopy. While human experts identified nine positive and 399 negative samples, miLab MAL correctly identified all of these and additionally detected two more positive cases, demonstrating its superior sensitivity in low-parasitemia scenarios.

This heightened accuracy is partly due to the platform’s ability to scan up to 200,000 red blood cells per sample, compared to the roughly 1,000 typically reviewed during manual microscopy.

“Microscopists often have to review 300 or more fields under the microscope to detect low levels of parasitemia,” Nagendra said. “miLab MAL automates this process, bringing more consistency and objectivity to malaria diagnostics.”

Although malaria is not endemic in the U.S., travel-related cases are on the rise.

“It’s now possible for someone to be in a malaria-endemic region one day and back in the U.S. the next, already infected,” Nagendra noted. “Although we have many laboratories across the country, diagnostic expertise in malaria is limited and often confined to a few highly trained professionals.”

This creates a diagnostic gap, especially as global travel increases and direct flights to endemic regions become more common, he added.

He also pointed to rare but alarming incidents of locally transmitted malaria in the U.S., where domestic mosquitoes become carriers after biting infected travelers.

“In this context, having fast and accurate diagnostic capabilities is increasingly critical -- not just in the developing world, but also in high-income countries.”

(Credit: Getty Images)

Bridging diagnostic gaps in endemic regions through AI and automation

Globally, diagnostic access remains a major barrier to malaria control.

“In many endemic regions, especially in sub-Saharan Africa, issues like poverty, conflict, and corruption prevent people from reaching healthcare facilities,” he said. “Clinics are under-equipped, and trained personnel are scarce.”

Patients often don’t know what they’re suffering from, and even if they seek care, accurate diagnosis can be elusive, he added.

Traditional diagnostic methods such as thin and thick blood smears require precise slide preparation and manual review under a microscope -- a process that takes time and expert skill.

While PCR testing is available, it offers only a binary result and does not quantify parasitic load. Rapid diagnostic tests (RDTs) are widely used in low-income settings but are often insufficiently sensitive.

In contrast, miLab MAL combines robotics, imaging, and deep learning with Noul’s proprietary NGSI (Next-Generation Staining and Imaging) technology to deliver highly automated and accurate diagnostics. The system not only detects the presence of parasites, but can also monitor parasitemia levels over time, providing actionable clinical insights that go beyond yes-or-no answers.

“One of the platform’s major strengths is the ease of training,” Nagendra said. “Unlike traditional microscopy, which takes months or years to master, training for miLab MAL can be completed in days to weeks.”

It can even be done virtually, and this makes the platform especially suitable for deployment in resource-limited regions where expert microscopists are scarce, he added.

Currently, miLab MAL is being used in 28 countries and is part of collaborations with public health institutions, including the Swiss Tropical and Public Health Institute, the Korea Disease Control and Prevention Agency, and Novartis. The platform is also deployed in malaria-endemic regions such as Ghana, Nigeria, and Southeast Asia.

Notably, miLab MAL is the only digital microscope-based malaria diagnostic solution featured in the WHO-Unitaid malaria diagnostic landscape report and is a certified member of the Swiss Malaria Group.

Unlike RDTs, which rely on antigen detection and are susceptible to genetic deletions such as pfhrp2/3, miLab MAL uses morphological analysis, allowing it to diagnose malaria even in cases where rapid tests fail.

While the Labcorp study focused solely on Plasmodium falciparum, the most dangerous and most common species in imported cases, Nagendra acknowledged the importance of expanding validation to other strains.

“We did not include other species like P. vivax or P. malariae,” he said. “Future studies should assess the tool’s performance in endemic areas where mixed infections are common, and I understand that Noul is already pursuing such research in sub-Saharan Africa and Southeast Asia.”

Nagendra also emphasized that diagnostic innovation remains essential despite the availability of a malaria vaccine.

“The vaccine offers some protection, but it doesn’t eliminate the disease,” he said. “Early diagnosis, particularly in vaccinated individuals with low parasitemia, is still critical.”

He concluded that improving diagnostic access at the community level must be a global priority.

“People in malaria-endemic regions often walk hours or days to reach healthcare,” he said. “Bringing diagnostic tools like miLab MAL closer to where patients live—and ensuring treatment is also available nearby—is vital.”

While the WHO’s goal of malaria eradication by 2030 may be ambitious, he remains cautiously optimistic.

“With continued innovation, including AI-based diagnostics and expanded access to care, we can make substantial progress,” he said. “Maybe not by 2030, but certainly within the coming decades, we can reduce the global burden significantly.”