It's the data, stupid: Inflection point for Artificial Intelligence in Indian healthcare

Highlights：

• India needs a different approach to suite her unique requirements. In this regard, each of the five stages in AI development lifecycle has been analyzed in the light of current on-ground realities.

• These boil down to three fold challenges of how to increase adoption of digital health, prevent data feudalism and create maximum value from data.

• Data being the common denominator requires liberal policies enabling secondary use, if AI has to become a reality for Indian Healthcare.

• Restrictive healthcare data policies and laws can slow down adoption of digitization, perpetuate status-quo, be biased towards the incumbent players, cause Industry stagnation and thus will do more harm than good. It is therefore the data policies that will make or break AI in Indian Healthcare.

摘要

•Indian Healthcare is fast growing and with significant chunk of it being in small, fragmented, informal sector. There is an inclination to merely mimic the western approach in the on-going policy making and legislative exercises, which can have serious fallouts for Indian Healthcare.•India needs a different approach to suite her unique requirements. In this regard, each of the five stages in AI development lifecycle has been analyzed in the light of current on-ground realities.•These boil down to three fold challenges of how to increase adoption of digital health, prevent data feudalism and create maximum value from data.•Data being the common denominator requires liberal policies enabling secondary use, if AI has to become a reality for Indian Healthcare.•Restrictive healthcare data policies and laws can slow down adoption of digitization, perpetuate status-quo, be biased towards the incumbent players, cause Industry stagnation and thus will do more harm than good. It is therefore the data policies that will make or break AI in Indian Healthcare.