For most of the last century, healthcare has been built around one idea: find the disease, then treat it. This model has saved millions of lives. But it has also come at a cost. Although people are well aware of the saying “Prevention is better than cure”, the steps required for prevention have stayed on the sidelines, resulting in a system that waits for people to fall sick before it acts. As treatment costs keep rising, it’s becoming difficult for poor and middle-class families in India to get treatment on time, often leading to a single serious illness that can wipe out years of savings. Today’s healthcare is reactive. This puts growing pressure on the system, delaying access to care and can put patient safety at risk.
Now, how do we change this? A healthcare push built around early detection and timely intervention can stop many people from sliding into chronic and serious conditions in the first place. Catching risk early means people can change their lifestyle, start treatment sooner and avoid expensive hospital stays later. This approach is better for the patient and it reduces the overall cost of care significantly. It also reduces the burden on healthcare system and makes it possible for the healthcare workers to address the patients in a more systematic and timely manner.
Is this realistic? I believe the answer is yes, largely because of the progress artificial intelligence has made in the last few years. One good example is the use of retinal images, pictures of the back of the eye, to detect early signs of cardiovascular, kidney, neurological and autoimmune conditions. This is a fairly new field called Oculomics and the idea behind it is simple: the eye can act as a window to the health of the rest of the body.
How can a photograph of the eye tell us anything about the heart or the kidneys? Let me try to explain the science behind it and how it could be used in practice.
The retina is the only place in the human body where we can directly see blood vessels and nerve tissue, in this case the optic nerve, without making a single cut. This matters because many of the most common chronic diseases start by damaging small blood vessels and nerves. High blood sugar and high blood pressure, for instance, quietly harm the body’s microvasculature and nerve structures over many years, often without any symptoms.
In the retina, this damage leaves behind small structural and textural changes long before anything looks clinically wrong. The changes are spread across the entire image and are far too subtle for a doctor to read reliably by eye.
This is exactly the kind of problem deep learning is good at. A large AI vision model can read the whole retinal image at once. It looks at fine details such as the width of blood vessels and how much they twist and branch, broader texture patterns across the retina and early signs of nerve damage. It then condenses all of this into a single representation of what the retina is telling us about the state of the blood vessels in the body. Because these signs appear in the retina early and stay there, they offer a useful opportunity. They can point to risk well before conventional tests pick anything up and long before there is serious damage to major organs such as the heart, brain or kidneys.
The potential applications are wide. Research is exploring retinal signals for early risk assessment of heart conditions such as coronary heart disease and atherosclerosis. In neurology, the list includes dementia, Alzheimer’s disease and stroke, as well as psychiatric conditions such as schizophrenia, bipolar disorder and major depressive disorder. For the kidneys, there is early detection of chronic kidney disease and renal dysfunction. Work is also under way on liver assessment and on autoimmune, infectious and inflammatory conditions such as rheumatoid arthritis.
AI-powered retinal imaging is emerging as a promising approach to the early assessment of systemic health risks, particularly cardiovascular disease. Using a handheld retinal camera, images of both eyes can be captured in under a minute. Deep learning models analyse vascular and retinal patterns in these images to generate estimated short-term and long-term risk scores. These assessments can be aligned with established clinical tools such as the Framingham Risk Score (FRS) and the Atherosclerotic Cardiovascular Disease (ASCVD) risk score, which are widely used by healthcare professionals.
With results available within seconds, this technology has the potential to expand access to preliminary risk screening across general physician practices, diabetes clinics, primary and community healthcare centres, and outreach camps. In areas where access to blood tests, ECGs and specialist consultations may be limited, handheld retinal imaging could enable healthcare workers to screen more people efficiently and refer to individuals identified as potentially high-risk for further clinical evaluation.
While Oculomics is still an evolving field, continued research, rigorous clinical validation and the development of retinal image biobanks supported by health data from the Indian population will be critical to advancing its clinical utility. Such efforts could help establish more representative and effective early screening approaches tailored to India’s healthcare needs.
As the field advances, a quick, non-invasive retinal scan could become a valuable tool in identifying potential health risks earlier, enabling timely medical intervention and helping reduce the long-term burden of preventable complications.
Authored by Venkatakrishnan S, Chief Technology Officer and Head of R&D at Forus Health