A study published in the leading journal Nature Medicine has shown an artificial intelligence (AI) system that scans images of the retina can detect the signs of diabetes, high blood pressure, high cholesterol, gout, osteoporosis and thyroid disease in seconds.
The program – called Reti-Pioneer – is a step towards being able to diagnose many different conditions from a scan of the eye – providing people a quicker diagnosis for common conditions and increasing access to crucial testing.
Associate Professor Lisa Zhuoting Zhu, Head of Ophthalmic Epidemiology at CERA is one of the leading authors on the paper, and says this technology is making diagnosing disease more efficient, particularly in remote or regional communities.
“This technology will be a real benefit to public health,” she says.
“Patients would be able to get information about their health instantly and start interventions as soon as possible instead of waiting for more time-consuming test results.”
In a flash
Reti-Pioneer uses several AI systems to scan images of the eye and check for subtle signs of disease that would be impossible to spot otherwise.
These models are trained using thousands of images of eyes from people both with and without disease. They are taught to spot signs of illness that can’t be picked up by the naked eye.
A scan of the eye is not only cheaper and easier to perform than forms of diagnosis like a blood test but also delivers results in seconds.
The research, primarily carried out in primary healthcare centres in China, found the system provides accurate screening to help doctors make decisions about patients’ health without waiting for these slower test results.
“China has one of the most efficient healthcare systems in the world – the results of a blood test can come back to patients in nine hours,” Associate Professor Zhu says.
“But this system is even quicker.
“In countries like Australia, Singapore and the United States test results can take a few days – or even a week if samples need to be taken to a lab from remote communities.
“If a patient can be flagged on the spot for a condition like diabetes, we can start interventions while waiting for the results of more advanced screenings that take time.”
Another advantage of the system is that it only requires a basic fundus camera, rather than specialised equipment. This means it could be used in GP clinics, optometrists, community pharmacies and travelling clinics.
Associate Professor Zhu says systems like these have a real potential to close access gaps.
“AI platforms like this can significantly increase access to healthcare, particularly those in regional and remote Aboriginal communities.”
Read the research
Zhang, X., Li, Q., Liang, Y. et al. AI framework for multidisease detection via retinal imaging. Nat Med (2026). https://doi.org/10.1038/s41591-026-04359-w
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