The use of Artificial Intelligence (AI) is catching on big time in healthcare and medicine. Essentially, AI is designed to figure out the best solutions for problems by processing information and making smart decisions about diagnoses and prognoses.
So, AI isn’t just your typical programmed algorithm; it learns from data exposure. It’s not like someone manually sets the rules; it’s more like a super-smart computer that independently figures out the best solutions. The layers of AI dig into data on their own, pick out valuable info, and use it to make smart decisions about whatever problem it’s dealing with.
There are two main types of AI: machine learning and deep learning. Machine learning involves these layers of neural networks that crunch data, and deep learning takes it up a notch by letting the system recognize patterns in different layers.
Back in healthcare, when AI first hit the scene, it outdid radiologists in imaging tasks. Geoffrey Hinton, this big shot in deep learning, even said radiologist training should stop because AI could analyze breast and heart images better. He later backtracked a bit, saying roles like radiologists would change because of AI, not vanish.
According to Gartner, this advisory company, AI is supposed to create more jobs than it cuts, especially in healthcare. But, in other industries, jobs might take a hit.
People are hoping AI will be a game-changer in healthcare by handling the boring stuff and freeing up time. The idea is that humans and AI will work together to find the best solutions. Think about it – huge amounts of data can be a headache for us, but for AI, it’s a walk in the park. It can pull out useful results, and we can make sense of them.
In the real-life medical scene, there’s this programme called Deep Patient. It uses deep learning to predict diseases, trained on a massive database of 700,000 patient records from a New York hospital. Deep Patient can spot patterns that hint at conditions like liver cancer and schizophrenia. Some folks think programmes like this could replace doctors in far-off places or ease their crazy workload.
Now, when it comes to making drugs, companies are putting their money on AI to speed things up and save some cash. One big pharma company is using IBM Watson (a set of artificial intelligence (AI) technologies and services developed by IBM), this machine learning system, to hunt for immune-oncology drugs. And it looks like others are jumping on the AI bandwagon too.
Looking ahead, though, not everyone’s gung-ho about AI. Most big companies aren’t in a rush to go all-in on AI right now. Sure, AI is getting better at predicting drug development targets, but there’s a bigger issue with keeping patient info hush-hush. According to some studies on big US health systems, cybersecurity is a bigger deal right now than diving headfirst into AI.
Even the people who want more AI are facing some slow progress. It’s not just about getting AI on board; it’s about finding new folks who can handle the tech side of AI. And since AIs need real data to work, that could mean a bunch of new jobs, maybe even more than the jobs that might disappear.