We build AI tools to detect oral and breast cancer: IISc Professor Phaneendra

We build AI tools to detect oral and breast cancer: IISc Professor Phaneendra Prof Phaneendra K Yalavarthy's research interests include AI for medical imaging, computational methods in medical imaging, medical image processing, and Cyber-Physical Systems.

Key points

• 13 min readOct 10, 2026 07: 30 AM IST First published on: Oct 10, 2026 at 07: 30 AM IST.
• Global breast cancer cases to hit 3.5 m by 2050: Lancet study demonstrates deaths climbed in India, cases spiralled in younger women.
• A postgraduate in Physics from Sri Sathya Sai University, Prof Phaneendra K Yalavarthy additionally has an M.Sc.
• A postgraduate in Physics from Sri Sathya Sai University, he additionally has an M.Sc.
• Prof Phaneendra: The Indian Administration felt that sector-specific solutions needed to be developed at scale, with AI as the enabler.

In practice, a postgraduate in Physics from Sri Sathya Sai University, Prof Phaneendra K Yalavarthy additionally has an M.Sc. In engineering from IISc, and a PhD in Engineering Sciences from Dartmouth College, USA. (Express photo). Written by: Venkatesh Kannaiah.

Prof Phaneendra K Yalavarthy is the Chief Project Manager of Translational AI for Networked Universal Healthcare (TANUH), an AI Centre of Excellence in Healthcare at IISc, Bengaluru, and a professor in the Department of Computational and Data Sciences (CDS).

TANUH, a non-profit, develops and deploys AI-driven diagnostics and decision-support tools for early detection and management of non-communicable diseases throughout India.

Phaneendra's research interests include AI for medical imaging, computational methods in medical imaging, medical image processing, and Cyber-Physical Systems.

Meanwhile, a postgraduate in Physics from Sri Sathya Sai University, he additionally has an M.Sc. In engineering from IISc, and a PhD in Engineering Sciences from Dartmouth College, USA.

Phaneendra spoke to indianexpress.com on the goals of TANUH, efforts to fight non-communicable diseases in India, using AI to fight oral and breast cancer, and building a digital public infrastructure layer to backing neurological healthcare. Edited excerpts.

Venkatesh Kannaiah: Tell us regarding the origins of TANUH and its areas of focus.

Prof Phaneendra: The Indian Administration felt that sector-specific solutions needed to be developed at scale, with AI as the enabler. TANUH was set up as an AI Centre of Excellence focused on healthcare.

It is one of four such centres. Others are focused on education, agriculture, and sustainable cities and are located in various cities.

As around 70% of the current disease burden in India is from them, we focus on non-communicable diseases. We are building AI applications for health workers like nurses or ASHA workers involved in primary care with a community focus. Our focus is not on patients or doctors per se.

We focus on two of the three cancers that the WHO has identified as requiring population-level screening: oral cancer and breast cancer.

We additionally work on retinal and vascular diseases. Vascular diseases encompass liver and kidney diseases, cardiovascular risk, and diabetes. Gestational diabetes, mental health, and neurology are other interest areas.

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On the neurology side, we are working with the Centre for Brain Research at IISc to develop a foundation model for the Indian brain. Venkatesh Kannaiah: Tell us regarding what TANUH seeks to achieve.

Prof Phaneendra: Our goal is early detection, prevention, and management of non-communicable diseases through AI.

We try to build AI solutions at scale that can be applied by frontline health workers. The user interface of our products is multilingual, intuitive, and gesture-based. They are additionally voice-based. We call this point-of-care design: tools built specifically around frontline health workers and to work in the field, unlike a hospital. It is a controlled setup. Venkatesh Kannaiah: Tell us regarding your cancer screening tools.

Prof Phaneendra: There is Aarogya Aarohan, an oral cancer and pre-cancer screening solution applied by frontline health workers.

As residents chew tobacco, paan, or gutkha, india has a massive burden of oral cancer. They develop lesions that are indicative of a potential for cancer, but there are no other symptoms.

We employ a mobile phone app to take pictures of the lesion to identify whether it is pre-cancerous. This AI-based solution has won plenty of awards, and we have screened more than 70, 000 patients.

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Notably, the cancer registry indicates to us the places with a high incidence of oral cancer. Varanasi and Mathura in Uttar Pradesh, Dakshina Kannada district in Karnataka, and Thanjavur in Tamil Nadu are among the affected regions. Our work is additionally focused on these regions.

As for breast cancer, women above 40 years of age are projected to undergo screening. In India, around 20 crore women are in this age group, but we only have around 3500 mammogram machines, and each machine can only do regarding 20 screenings per day. Hence, only around 1% of the target population is getting screened.

Moreover, if you compare with Western societies, Indian women obtain breast cancer earlier. Our highest incidence is between the ages of 40 and 50, whereas in the West, the incidence is between the ages of 50 and 60. That additionally means that we should screen them earlier. High-risk patients are regarding 4 to 5 per cent of the general population.

We have built a tool to predict breast cancer risk. We have a questionnaire that is reinforcement-learning-driven, and we can find out whether they are high- or low-risk. It is an India-specific risk stratification for breast cancer. We are trying to identify those at higher risk and then obtain them to mammogram screening. It is not a fixed questionnaire. Depending on how they answered the previous questions, it keeps changing the next question, but only 10 questions are asked.

Venkatesh Kannaiah: How confident are you regarding your predictions on breast cancer?

Prof Phaneendra: We are regarding 83% accurate compared to a mammogram. There is something called Negative Predictive Value (NPV), meaning that when we say someone is not at high risk, we are 97 per cent confident.

When we identify someone as high risk, it is called Positive Predictive Value. That number is low. For us, it is only regarding 60% to 65%. There is no risk to a patient even if he is wrongly referred for a mammogram test, and if the patient is high risk, the mammogram can confirm the same. Venkatesh Kannaiah: Tell us regarding other tools under development.

Prof Phaneendra: We additionally do retinal vascular screening. Seventy percent of our current disease burden in India is either from diabetes, cardiovascular disease, liver disease, or kidney disease.

They overlap in plenty of ways. Someone who is a diabetic is additionally at highly high risk for cardiovascular disease or a kidney difficulty. Why IISc's novel method holds promise for oral cancer treatment.

As of now, all non-communicable disease clinics at Community Health Centres and district hospitals run the test for diabetes and cardiovascular disease.

With a special handheld camera (Fundus Camera), we can take a picture of the eye without dilating the pupil and find out whether the person has diabetes, cardiovascular disease, liver disease, or kidney disease.

Ideally, one should do a blood test to identify if the patient has any of these four diseases. Now, with merely an image, we can tell whether they should be tested for any of these diseases.

We additionally try to provide what are called biochemical parameters. For kidney disease, you need to know eGFR (estimated glomerular filtration rate). If your eGFR is below 60, you are considered to have chronic kidney disease. We additionally try to provide an estimate of what the eGFR could be, so that they can be referred for a kidney test.

In the Pradhan Mantri Jan Arogya Yojana (PM-JAY) scheme. It provides health insurance coverage, 30 per cent of the budget goes towards dialysis, and it's quite a burden. It is additionally open to abuse and fraud. We are developing an AI-based anti-fraud tool.

We take an ultrasound image of the kidney. It indicates whether the patient needs dialysis, so that those who don't need it should not be able to claim the funds. This is not visible in a normal ultrasound. You need to run it through an AI tool to obtain the result. This tool is now being rolled out in various states.

Another matter is with gestational diabetes. It is at present detected at around 24 weeks into the pregnancy. Using AI, we are trying to predict it within the first 14 weeks.

There are what we call modifiable risk factors that have an impact. The age at which women become pregnant has been pushed a little further. If you are older, you have a higher probability of developing gestational diabetes. Nowadays, a high percentage of pregnant women in India are being diagnosed with gestational diabetes. We are now building a tool for early prediction and early intervention.

For now, we build AI tools to detect oral and breast cancer: IISc Professor remains the part of the story worth watching, and further updates are likely as more details are confirmed.