Anand Mahindra Reacts To AI Model's Success In Predicting Breast Cancer 5 Years In Advance

A deep learning (DL) model based on mammography has been created by researchers at Jameel Clinic for Machine Learning and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). This model may help detect precancerous changes in high-risk women. When estimating the possibility of breast cancer from a mammogram, this artificial intelligence (AI) system named Mirai can perform better than existing risk-assessment algorithms. Business tycoon Anand Mahindra cited a post on X discussing AI that helped in detection of breast cancer five years in advance.

Reposting the post, the Mahindra boss stated, “If this is accurate, then AI is going to be of significantly more value to us than we imagined and much earlier than we had imagined.”

With more than 2 lakh views, social media users couldn’t agree more.

A person mentioned, “This is very accurate; we are also exploring AI to detect cardiac arrhythmia from heartbeats and detect COVID from patient coughs. AI will change the way we treat patients.”

Another user stated, “That’s the best use of technology anyway. Assisting human kind to become better and reduce risks.”

“AI, Machine Learning, Big data analysis based systems will drastically improve the living standards of life of every strata of society,” read another comment.

A user went on to suggest, “It’s high time we add AI in healthcare as a subject in medical studies. Tech enabled doctors can do wonders in prevention, diagnosis & treatment of diseases.”

In the meanwhile, Mirai was tested on held-out patients from Massachusetts General Hospital (MGH) as well as other datasets, according to a study published in Science Translational Medicine. Mirai was trained using a sizable screening mammography dataset from MGH.

There are four modules in Mirai. To construct an illustration of the mammogram, an image aggregator module first gathers and processes all conventional mammography images. From there, it aggregates the image data from all of the views. A risk-factor prediction module then uses the mammography to anticipate the patient’s risk factors, if needed. In the last stage, an additive-hazard layer uses the patient risk variables and the mammography analysis to forecast the patient’s risk annually for the following five years.

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