The Impact of AI in Healthcare

A scientist in a cutting-edge medical lab uses a tablet to interact with virtual data displays, surrounded by advanced technology including test tubes and multiple digital screens. Credit: Cleveland Clinic

AI has had significant breakthroughs in many fields in 2025. One of the fastest advancing fields with AI has been healthcare. AI is already rewriting the rules in hospitals by rapidly analyzing medical images and flagging subtle anomalies that our human eyes might miss. It identifies key details during operations and surgeries, saving time for doctors and nurses to focus on other aspects of the medical task at hand.

There are many other concrete examples where AI is making a difference on the ground. For instance, IBM iX has shown how their AI-powered diagnostic tools have proven to be adept at catching early signs of lung cancer by analyzing CT scans in just a few seconds. That's a remarkable time, which could allow hospitals to double the number of patients that the doctors see in one day.

IBM iX's AI tool is capable of providing insights and facts after analyzing patients; these simple details could mean the difference between life and death, another example of how AI could potentially save lives in healthcare.

So, how does this technology achieve such precision and speed compared to traditional methods? IBM iX's AI tool utilizes deep neural networks, or deep learning, and vast knowledge of previous datasets. After receiving this data, it comprehends the values and uses that to fill in potential knowledge gaps that it may have, which is where the advanced neural networks play a part. In other words, the AI practically becomes a replacement for a human radiologist - except it's ultra-precise, tireless, quick, efficient, can pick up intricate patterns from various datasets, and thinks before it acts. Based on the number of positive adjectives I used in that sentence, you can already tell that AI is going to be good at this. Like, really, really, good.

But, like any other rapidly developing innovation in history, there are concerns. First, people are worried about data privacy. AI is essentially just a robot, and when you're handing all of those datasets with personal medical information to a robot, someone could intercept it. Although AI is incredibly smart, it doesn't seem to have gained self-awareness. This means it may not be able to know what information needs to be guarded in secrecy. Secondly, academic research from BMC Medical Research highlights the risk of bias in training data.

To put it simply, the AI could suffer from problems with being biased, based on how it was taught to recognize data, or the data that it was fed for pattern recognition. No matter what, bias is unavoidable. It's impossible to completely erase bias from this picture, because we are the ones teaching the AI in the end. We code it. And we, as humans, are almost always biased.