Internationally, there is an increasing interest in the use of AI for treating cancer by facilitating new therapies and by diagnosing patients at early stages. Doctors can also select appropriate treatment by identifying patients at high risk (say those who are likely to develop pancreatic cancer up to three years earlier). This is game-changing, since most get diagnosed when there is advanced cancer or when cancer has metastasized.

AIMS researchers have developed a supercomputer and AI helps doctors to identify the best cancer therapies (out of so many available) for their patients. A supercomputer, a server and AI help doctors to understand the genetic mutations in their patients. Doctors can select the most appropriate therapy for such mutations. To illustrate, a HER2 breast cancer is cross-referenced to therapy that has worked for most patients of similar genetic make-up. Doctors, thus, make informed, faster and precise therapy choices.

Here iOncology AI is used. The supercomputer is located at Pune. The server is located at Jhajjar (at National Cancer Institute). iOncology AI aims to sequence genomes of 3000 cancer patients at AIMS, Delhi. They try to correlate this data to diverse cancer therapies to get the most efficacious therapy. The model has been tested on breast and ovarian cancer patients and has recorded 75 per cent accuracy as compared to the clinical diagnosis. Genomic data is more powerful tool for medical researchers and doctors. The system is being validated in several hospitals in MP. After studying the clinical data and genomic make-up of several thousand cancer patients, the model will be able to help the doctors in selecting the appropriate treatment for the next patient. It becomes targeted treatment.

A doctor will have to upload a scan or histopathology report on the platform. The trained AI will be able to flag automatically certain anomalies. It may also indicate a very small tumour that a radiologist could miss. The system is useful in early detection of cancers.

Harvard Medical School has developed a specific tool for colon cancers.

Patient’s confidentiality is maintained. A radiologist is able to see the scans uploaded by him with personal details along with anonymized analysis of other scans. A clinician will be able to see the clinical history scans of his own patients.