A new artificial intelligence system that
examines the shape and structure of blood cells could significantly
improve how diseases such as leukemia are diagnosed. Researchers say the
tool can identify abnormal cells with greater accuracy and consistency
than human specialists, potentially reducing missed or uncertain
diagnoses.
The system, known as CytoDiffusion,
relies on generative AI, the same type of technology used in image
generators such as DALL-E, to analyze blood cell appearance in detail.
Rather than focusing only on obvious patterns, it studies subtle
variations in how cells look under a microscope.
Moving Beyond Pattern Recognition
Many existing medical AI tools are trained to sort images into
predefined categories. In contrast, the team behind CytoDiffusion
demonstrated that their approach can recognize the full range of normal
blood cell appearances and reliably flag rare or unusual cells that may
signal disease. The work was led by researchers from the University of
Cambridge, University College London, and Queen Mary University of
London, and the findings were published in Nature Machine Intelligence.
Identifying small differences in blood cell size, shape, and
structure is central to diagnosing many blood disorders. However,
learning to do this well can take years of experience, and even highly
trained doctors may disagree when reviewing complex cases.
"We've all got many different types of blood cells that have
different properties and different roles within our body," said Simon
Deltadahl from Cambridge's Department of Applied Mathematics and
Theoretical Physics, the study's first author. "White blood cells
specialize in fighting infection, for example. But knowing what an
unusual or diseased blood cell looks like under a microscope is an
important part of diagnosing many diseases."
Handling the Scale of Blood Analysis
A standard blood smear can contain thousands of individual cells, far
more than a person can realistically examine one by one. "Humans can't
look at all the cells in a smear -- it's just not possible," Deltadahl
said. "Our model can automate that process, triage the routine cases,
and highlight anything unusual for human review."
This challenge is familiar to clinicians. "The clinical challenge I
faced as a junior hematology doctor was that after a day of work, I
would face a lot of blood films to analyze," said co-senior author Dr.
Suthesh Sivapalaratnam from Queen Mary University of London. "As I was
analyzing them in the late hours, I became convinced AI would do a
better job than me."
Training on an Unprecedented Dataset
To build CytoDiffusion, the
researchers trained it on more than half a million blood smear images
collected at Addenbrooke's Hospital in Cambridge. The dataset, described
as the largest of its kind, includes common blood cell types, rare
examples, and features that often confuse automated systems.
Instead of simply learning how to separate cells into fixed
categories, the AI models the entire range of how blood cells can
appear. This makes it more resilient to differences between hospitals,
microscopes, and staining techniques, while also improving its ability
to detect rare or abnormal cells.
Detecting Leukemia With Greater Confidence
When tested, CytoDiffusion identified abnormal cells associated with
leukemia with much higher sensitivity than existing systems. It also
performed as well as or better than current leading models, even when
trained with far fewer examples, and was able to quantify how confident
it was in its own predictions.
"When we tested its accuracy, the system was slightly better than
humans," said Deltadahl. "But where it really stood out was in knowing
when it was uncertain. Our model would never say it was certain and then
be wrong, but that is something that humans sometimes do."
Co-senior author Professor Michael Roberts from Cambridge's
Department of Applied Mathematics and Theoretical Physics said the
system was evaluated against real-world challenges faced by medical AI.
"We evaluated our method against many of the challenges seen in
real-world AI, such as never-before-seen images, images captured by
different machines and the degree of uncertainty in the labels," he
said. "This framework gives a multi-faceted view of model performance
which we believe will be beneficial to researchers."
When AI Images Fool Human Experts
The team also found that CytoDiffusion can generate synthetic images
of blood cells that look indistinguishable from real ones. In a 'Turing
test' involving ten experienced hematologists, the specialists were no
better than random chance at telling real images apart from those
created by the AI.
"That really surprised me," Deltadahl said. "These are people who stare at blood cells all day, and even they couldn't tell."
Opening Data to the Global Research Community
As part of the project, the researchers are releasing what they
describe as the world's largest publicly available collection of
peripheral blood smear images, totaling more than half a million
samples.
"By making this resource open, we hope to empower researchers
worldwide to build and test new AI models, democratize access to
high-quality medical data, and ultimately contribute to better patient
care," Deltadahl said.
Supporting, Not Replacing, Clinicians
Despite the strong results, the researchers emphasize that
CytoDiffusion is not intended to replace trained doctors. Instead, it is
designed to assist clinicians by quickly flagging concerning cases and
automatically processing routine samples.
"The true value of healthcare AI lies not in approximating human
expertise at lower cost, but in enabling greater diagnostic, prognostic,
and prescriptive power than either experts or simple statistical models
can achieve," said co-senior author Professor Parashkev Nachev from
UCL. "Our work suggests that generative AI will be central to this
mission, transforming not only the fidelity of clinical support systems
but their insight into the limits of their own knowledge. This
'metacognitive' awareness -- knowing what one does not know -- is
critical to clinical decision-making, and here we show machines may be
better at it than we are."
The team notes that additional research is needed to increase the
system's speed and to validate its performance across more diverse
patient populations to ensure accuracy and fairness.
The research received support from the Trinity Challenge,
Wellcome, the British Heart Foundation, Cambridge University Hospitals
NHS Trust, Barts Health NHS Trust, the NIHR Cambridge Biomedical
Research Centre, NIHR UCLH Biomedical Research Centre, and NHS Blood and
Transplant. The work was carried out by the Imaging working group
within the BloodCounts! consortium, which aims to improve blood
diagnostics worldwide using AI. Simon Deltadahl is a Member of Lucy
Cavendish College, Cambridge.
- Simon Deltadahl, Julian Gilbey, Christine Van Laer, Nancy Boeckx,
Mathie P. G. Leers, Tanya Freeman, Laura Aiken, Timothy Farren, Matthew
Smith, Mohamad Zeina, Stephen MacDonald, Daniel Gleghorn, Martijn Schut,
Folkert Asselbergs, Sujoy Kar, Sophie Williams, Mickey Koh, Yvonne
Henskens, Norbert de Wit, Umberto D’Alessandro, Bubacarr Bah, Ousman
Secka, Rajeev Gupta, Sara Trompeter, Christine van Laer, Gordon A.
Awandare, Kwabena Sarpong, Lucas Amenga-Etego, Willem H. Ouwehand, James
H. F. Rudd, James HF Rudd, Concetta Piazzese, Joseph Taylor, Nicholas
Gleadall, Carola-Bibiane Schönlieb, Suthesh Sivapalaratnam, Michael
Roberts, Parashkev Nachev. Deep generative classification of blood cell morphology. Nature Machine Intelligence, 2025; 7 (11): 1791 DOI: 10.1038/s42256-025-01122-7
Courtesy:
University of Cambridge. "This AI spots dangerous blood cells doctors
often miss." ScienceDaily. ScienceDaily, 13 January 2026.
<www.sciencedaily.com/releases/2026/01/260112214317.htm>.