The growth of computer-processing power has exponentially increased over the last six decades. More specifically, according to an info-graphic by Experts Exchange, we’ve contributed to and witnessed a 1 trillion-fold increase in computing performance. This powerful statistic fosters different meanings to each of us when we think about what we’re capable of today, but what does it mean for the application of AI towards healthcare infrastructures and treatments?
The concept of AI has been around for decades; companies across all verticals are increasingly aware of the potential their data holds, and thus, how it can affect their competitive advantage within their landscape should they find ways to gain more insight and monetize it successfully. Personalized medicine, a mission associated with many of today’s healthcare companies and startups, has been talked about for a long time – the availability of large data sets and the development of advanced algorithms has enabled these organizations to target AI applications towards their “niche”, main value propositions within the marketplace to get ahead of the curve. But is there an exact formula for success in pursuing the implementation of these applications?
Industry experts note there have been significant “breakthroughs” already and agree that there is untapped, massive potential for AI to accelerate drug discoveries and timescales around clinical trials, and ultimately deliver quicker diagnoses and personalized medicines that result in better patient outcomes. Just recently, however, IBM’s Watson supercomputer gave unsafe and incorrect recommendations for the treatment of cancer – in this case, was this is an issue of inaccessible data, unrealistic timelines, or unrealistic expectations of technologies we have yet to truly understand? In my humble opinion from what I’ve seen in the market, “setbacks” like this shouldn’t be considered failures. Instead, they should be viewed as getting one step closer to success when considering the complexity of these technologies and the initiatives they’re aimed towards. As Thomas Edison famously said, “I have not failed. I’ve just found 10,000 ways that won’t work”.
So, will the resources that healthcare companies and startups dedicate towards advancements in their AI capabilities pay off in the end, or will it hold them back from achieving something greater in the short-term?
Over the next five to ten years, the truth will be narrated by the data itself; hopefully, we can look back and say we have a much better understanding than we did today in the ever-changing technological landscape.
If the proponents of these techniques are right, AI and machine learning will usher in an era of quicker, cheaper and more-effective drug discovery.