Don’t look now, but we’re increasingly surrounded by AI, or artificial intelligence.
As a recent Wired piece describes the landscape, “That app that delivers you late-night egg rolls? AI. The chatbot that pops up when you’re buying new kicks? AI. Tweets, stories, posts in your feed, the search results you return, even the people you swipe right or left; artificial intelligence had an invisible hand in what (and who) you see on the internet.”
But while many consider the AI potential in healthcare to be more or less off the charts, storage remains one of the ongoing, unintentional brakes. As the article puts it, “AI is only just beginning to change the way doctors see, diagnose, treat, and monitor patients. The potential to save lives and money is tremendous; one report estimates big data-crunching algorithms could save medicine and pharma up to $100 billion a year, as a result of AI-assisted efficiencies in clinical trials, research, and decision-making in the doctor’s office.”
The article notes that AI algorithms get better the more data they see, and “health data is practically hemorrhaging out of mobile devices, wearables, and electronic medical files.” But that’s where the optimism runs into reality, because AI needs data to work well, and while there’s more healthcare data all the time, health systems’ “siloed storage systems don’t make it easy to share that data with each other, let alone with an artificial intelligence. Until that changes, AI won’t be curing the world of, well, probably anything.”
Still, with flash storage spreading, the writer notes that 2017 “saw artificial intelligence begin demonstrating real concrete usefulness inside exam rooms and out. In the doctor’s office, AI is already helping dermatologists tell cancerous growths from harmless spots, diagnose rare genetic conditions using facial recognition algorithms, and lending an assist in reading X-rays and other medical images.”
The technology is seeping into the practice of medicine at every level, not just at the stage of final device approval. It’s now baked into the way biomedical researchers sift through tsunamis of genetic data and pharma firms discover new drugs. It’s how public health officials predict the next epidemic, and keep track of opioid hot spots. And it’s increasingly how doctors and scientists try to make sense their data-drenched realities.