AI in healthcare has guzzled millions of dollars; but where's the real world impact?
At a macro level; it's simple: fill the gap of highly skilled manpower in finance/medicine/banking (the list goes on) with faster and cheaper AI systems. And yet, there's not been a single breakaway success at scale - the competition is wide open and that's a challenge we are lapping up hungrily.
AI is great; but it's data hungry, needs expensive infrastructure (you can't have enough GPUs), and possibly requires the top 0.1% of the global engineering workforce to work on its challenges. This, here, is the paradigm that needs to change: AI needs to be affordable, accessible and able to run in limited infrastructure for critical applications like diagnosing diseases. All of this - at scale and without losing accuracy.
At ChironX these challenges keep us awake at night. How do we develop sensible business cases with data and resource constraints? How can a solution scale from a multispeciality hospital in Mumbai or Dubai to a block clinic in rural Botswana or Odisha? Can we make the medical fraternity love our product? Can we build higher systemic efficiency? I believe our journey has just started...
October 10, 2017