AI in healthcare: separating reality from hype
The healthcare industry is awash with promises about artificial intelligence’s transformative potential. From drug discovery to diagnostic assistance to operational efficiency, the claims about what AI can accomplish seem boundless—but how much is realistic, and how much is hype?
“I am cautiously optimistic, but that’s my personal opinion,” says Viktor Lazarevich, CTO and co-founder of Digiteum, with over 20 years of experience helping organizations implement digital technologies. “Cautiously optimistic means that I do not agree with the majority of the hype. But I still believe that there are a lot of areas where AI can be incredibly helpful.”
This balanced perspective is increasingly important as healthcare organizations navigate technology decisions in an environment filled with exaggerated claims and unrealistic expectations.
The problem with excessive hype is that it obscures genuine opportunities. As Lazarevich explains:
“There’s a tremendous amount of hype. They promise you that everything is going to be AI-driven, and drug discovery is going to be made instantly and with 100% success. There are a lot of promises like this. And it’s like mud to water, so to speak. It makes other promises, which are completely normal, also look unrealistic, just because there is so much hype.”
This “hype distortion field” makes it difficult for healthcare decision-makers to identify realistic, achievable applications of AI technology—potentially delaying implementation of solutions that could deliver genuine value.
Like previous waves of technology innovation, AI is following a predictable pattern of overinflated expectations followed by more realistic assessment.
“It happens with every wave of technological innovation,” Lazarevich notes. “It happened with the internet in the nineties. It happened with mobile and social in the early 2010s. There’s always a huge wave of hype, and then it gets grounded.”
For healthcare organizations, recognizing this pattern can help inform more realistic timelines and expectations for AI implementation.
“I personally believe that after some time, people get more aware of what AI means, what it can and can’t do. After that, all these overly optimistic promises just drop, and we will be left with the essence—what we actually can do and what actually can happen.”
When the hype fades, what remains are concrete, achievable benefits that can significantly impact healthcare operations. Lazarevich identifies several key areas:
1. Dramatic time reduction for data analysis
“Number one is reducing wasted time. AI can do something in an instant that requires people a couple months of work, especially when we’re talking about large volumes of data.”
In healthcare contexts, this might include analyzing clinical trial results, identifying patterns across patient populations, or extracting insights from research literature—tasks that would require weeks or months of human effort.
2. Automation of repetitive tasks
“A lot of processes, even nowadays in the healthcare industry, are manual. There is someone who goes through document after document, trying to find some pattern. AI can do it in a minute. A person needs to do it in a couple of months.”By automating these repetitive tasks, healthcare organizations can redirect highly trained professionals from data processing to data interpretation and decision-making.
Support for human decision-making.
3. Support for human decision-making
While AI can dramatically accelerate certain processes, Lazarevich emphasizes an important limitation:
“AI cannot replace human creativity, human spark. AI can help by providing some ideas, maybe some hypotheses to test, maybe guide through some protocols and best practices, but it cannot replace humans, so AI cannot do it by itself.”
This perspective positions AI as a decision support tool rather than a decision replacement—enhancing human capabilities rather than supplanting them.
For healthcare organizations seeking to capitalize on AI’s realistic potential, the key is building the proper foundation. As Lazarevich emphasizes:
“AI isn’t feasible without a solid data foundation. If your data isn’t clean, well-structured, and interoperable, you simply can’t move forward—it’s a fundamental prerequisite.”
By focusing first on data readiness, healthcare organizations can position themselves to implement AI solutions that deliver on their promises rather than falling short of hyped expectations.
The path to effective AI implementation in healthcare requires neither blind enthusiasm nor excessive skepticism, but rather a balanced perspective that acknowledges both possibilities and limitations.
By separating realistic benefits from exaggerated claims, healthcare organizations can make informed decisions about where to invest their resources—potentially achieving significant improvements in efficiency, accuracy, and insight without being misled by unrealistic promises.
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