Digital India Taking a Step Higher with Artificial Intelligence Implication

Digital India Taking a Step Higher with Artificial Intelligence Implication

Machines have overtaken man in every aspect of life. From autonomous cars to robots, this is the time of artificial intelligence. Countries such as the United States, Canada, Europe, and China have already begun witnessing the rapid evolution of artificial intelligence (AI). India is not far behind. Due to the presence of multi-billion dollar global software product companies, IT companies with their research and development bases in India have focused on artificial intelligence machine learning thus heading towards a digital India. A database of candidate profiles interested in working for topmost global organizations, commonly known as talent pool have started to draw more attention toward India. Majority of machine learning talent pool is extended across five cities: Pune /Mumbai belt, Chennai, Hyderabad, Delhi, and Bangalore.

AI has taken the next big step in development by entering into medical science. Numerous industries including healthcare have been hampered by an influx of new technologies such as machine learning, big data, and cloud, apart from artificial intelligence. A critical step in healthcare is analyzing and compiling all medical records and past history and to be able to gain the ability to transform data into insights is both a strategic imperative and competitive advantage across the healthcare sector.

Artificial Intelligence implanted in Medical Science Technologies Staging Positive Results

India witnesses a huge opportunity in terms of AI solutions, and Intel in collaboration with Philips is one of the first companies to make it happen. The Bengaluru Campus of Philips has driven insights based on analytics applications running on edge devices, in the clinics. This is being done to solve some of the most critical healthcare challenges by using various applications. These applications are mainly used for screening of diseases by using medical imaging such as MRI, ultrasound, and X-ray. Deep learning inference models for two healthcare use cases were tested–one on CT scans for lung segmentation and the other on X-rays for bone-age-prediction. Both of these were tested using OpenVINO toolkit and Intel Xeon Scalable processors. Both these tests were successful since a speed improvement of 38 times was achieved over the baseline for lung-segmentation model and 188 times for the bone-age-prediction model by Philips and Intel.

It is concurred that AI can be a real game changer in the field of medical science and healthcare along with personal and operational context. AI can come to the rescue of healthcare where machines will be able to handle OPD related queries and manage common symptoms and diagnose them likewise. This in turn will allow doctors and hospitals to be more productive and help better patient outcomes.

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