AI-enabled solutions in areas such as medical imaging are helping to address pressing challenges in healthcare such as staffing shortages and aging populations. However, accessing silos of relevant data spread across the different hospitals, geographies, and other health systems while complying with regulatory policies is a massive challenge.
Commenting on the collaboration, Dr Azad Moopen, Founder, Chairman and Managing Director of Aster DM Healthcare, said “The Secure Federated Learning initiative will help analyse data and support the development of a predictive mechanism for patients, the opportunity for a second opinion on treatments, and most importantly, affirming data security and confidentiality of patients. This collaborative platform with world leaders will open doors to many players in the sector to participate in developing accessible healthcare solutions.”
Nivruti Rai, Country Head, Intel India and Vice President, Intel Foundry Services, said “AI applications are at the cusp of revolutionising healthcare through timely and effective screening, diagnosis and treatment of diseases. This solution will be offered as a service to be used by both AI researchers and data custodians in their pursuit of advancing AI innovation and extensive impact in healthcare. It marks a paradigm shift by getting the computer to the data rather than getting the data to the computer.”
Dr Vidur Mahajan, Chief Executive Officer, CARPL.ai, said “There is no doubt that de-centralised data storage and subsequent training of AI models in a federated manner is the future, especially since lack of generalisability of AI is becoming a bigger problem. Our mission is to take AI from bench to clinic, and this is another example of the same.”
Federated Learning (FL) is a method of training AI algorithms with data stored at multiple decentralised sources without moving that data. To facilitate the adoption of federated learning, Intel has led the development of OpenFLopen source framework for training machine learning algorithms that provide a solution to “data silos” by leveraging Intel’s security technology.
The demonstration of the capability of this platform was done using hospital data from the Kerala, Bengaluru, and Vijayawada clusters of Aster Hospital. Over 125,000 chest X-Ray images, including 18573 images selected from over 30,000 unique patient data from Bengaluru, were used to train a CheXNet AI model using a two-node/site approach – Bengaluru and Vijayawada – Federated Learning to detect abnormalities in the X-Ray report. The 18,573 unique images, in addition, provided a 3 per cent accuracy boost due to real-world data that was otherwise not available for training the AI model.
The success of this pilot has demonstrated engagement to the next level, which is to democratise access to health data across organisational and geographical boundaries without compromising on the data privacy and security aspects.