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AI in Indian HealthTech

India is one of the countries in the world with the most room for innovative, sustainable and scalable healthcare technology to improve lives. This is largely due to vast inequalities in healthcare distribution, glaring lack of trained healthcare clinicians and infrastructure, and low government spending on healthcare. In a country with 1 billion people majorly equipped with internet connections and smartphones, it is painstakingly difficult to name more than a handful of examples of digital technology that have impacted healthcare outcomes significantly or been used widely.  AI in Indian HealthTech

Artificial Intelligence (AI) is the technology that can help Indian healthcare succeed and make a difference at scale as it can be priced for the country and developed to tackle its constraints. AI boils down to redistributing scarce expert knowledge to a large number of beneficiaries by training algorithms machines to replicate this knowledge if implemented correctly.

A plethora of examples exist regarding digital innovation that has demonstrated success in screening, prevention, and treatment in India and for each of these, there are likely dozens of comparable projects ongoing. Almost all the country’s healthcare is pre-digital, paper medical records and film-based radiology are still more common than their electronic counterparts. Even seemingly simple systems such as an online appointment-booking system at the country’s largest public hospitals in New Delhi can have a large impact in this setting by sparing patients long waits and saving numerous trips to the hospital for those who can ill-afford to take a day off.

A past few years have seen some examples of dedicated hardware and technology engineered for the unique challenges of Indian health ecosystem, including low-cost vital parameter monitors for use in the primary healthcare setting, products for tuberculosis medication adherence monitoring (one of India’s most significant public health issues), and telemedicine programs that provide clinical expertise to areas without doctors. These applications are even more mature than the AI applications, which have begun to emerge over the last 5 years. AI applications were primarily used for screening, monitoring, and diagnostic assistance. However, they have also found applications through algorithms that analyze chest X-rays and other radiology images, read ECGs and spot abnormal patterns, automatically scan pathology slides and even assess fundus images for signs of retinopathy.

Some of the medicine branches in the country have been more successful than others at fostering the development of innovation and adopting it. A clear leader on both these counts is ophthalmology, with a relatively broad range of innovative technologies. These include high-quality imaging of both retina and cornea using smartphone-coupled devices, artificial intelligence for the screening of diabetic retinopathy – being developed and tested, and then brought into clinical use. The contribution of private-sector has a lot to do with this and would not have been possible without the foresight shown by a set of well-organized large private eye care centres in the South of India that facilitated the data collection and piloted the new technologies.

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