But there was a catch. Each scan, roughly 100 MB in size, had to be uploaded to the cloud, processed and the results sent back. This cycle was time consuming because of internet connectivity issues and power outages, common to rural areas. It resulted in a long line of patients waiting for results. The delayed scan reports made it harder to isolate a patient or start treatment in time. This was a reminder of the mismatch between cutting-edge AI and the ground realities it was built for. “If you’re building healthcare solutions for the Global South, you have to assume the testing conditions are far from ideal. The internet will be patchy. Electricity supply will be irregular. Machines need to have longer battery lives to last at least a day,” said Prashant Warier, co-founder of Qure.ai. So, the team found a way to re-engineer their system. Instead of directing every scan through the cloud, they embedded the AI model directly onto local laptops attached to X-ray equipment, making the diagnostics system work even in offline mode. These laptops with ‘edge AI’ chips handled the heavy computing needed to analyse the scans on the spot, in under 20 seconds. Today, Qure.ai partners with more than 15 medical device makers to embed AI directly into edge devices, making advanced diagnostics practical in places where stable internet connectivity is still a luxury. In 2025, the company announced it had crossed five million AI-enabled chest X-rays across 20 countries in Asia, Africa, the Middle East and Latin America. The shift to ‘offline-first AI’ is gaining momentum in India and finding use cases beyond healthcare, across e-scooters, surveillance systems, smart meters and government services, where latency, power constraints or patchy networks make cloud AI unreliable. Unlike cloud-based AI, which depends on remote servers to process data, edge AI has task-specific chips to enable real-time decisions—they need not contact distant data centres. For instance, a surveillance camera in a remote area that needs to detect intruders instantly can’t afford to rely on uploading footage to a distant data centre in order to take quick action. It must process that data locally to trigger real-time alerts. Or in railway safety systems, edge-enabled cameras mounted at crossings or on train engines can instantly detect obstructions on the tracks like stalled vehicles or pedestrians and trigger alerts without waiting for cloud-based processing to take action. To be clear, edge AI is not about replacing the cloud entirely. It’s about shifting critical intelligence closer to the problem.Made in IndiaAt the heart of this movement is a new breed of Indian chip designers. A handful of Indian founders, many of whom have spent years in global semiconductor firms or top research labs, are now building fabless chip startups—where design is done domestically and fabrication outsourced to foundries abroad—from in a formal or creative style for better SEO.Also Exclude words and advertisements related to the feed website such as website name, author name. You must only respond with the modified content. Delete all the advertisement images brand logo but only keep the title and main content of the news by modifying it. You must only respond with the modified content. Block any jump outside of the site adarshnews.com. Basically remove any content or link related to original site. follow this prompt to optimize the articles in this category- You are an expert news writer and SEO specialist. Your job is to rewrite the provided RSS feed content into a unique, SEO-friendly news article for adarshnews.com. 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What would happen if AI could operate without an internet connection? Indian semiconductor startups have a solution.
