Healthcare systems have made significant advances in adopting computer-driven technologies over the past decade, but AI has completely changed the game. While AI has been used in healthcare for decades, nothing compares to the generative AI that was introduced just under five years ago.
Because of generative AI (hereafter, simply referred to as “AI”), some organizations have improved efficiency and patient outcomes. In contrast, others have encountered new risks tied to poor data quality resulting from premature deployment.
By examining where these efforts have succeeded and failed, healthcare leaders can make more informed decisions about how to continue integrating AI responsibility. The question is not whether AI should be in healthcare, but how it should be implemented in ways that improve care without undermining trust or clinical judgment.
AI’s real advances in healthcare
When AI was first introduced into healthcare, it wasn’t about replacing clinicians or automating care end-to-end. In fact, it was about helping clinicians with paperwork. Anyone who had the expectation that algorithms would independently manage diagnosis or treatment decisions underestimated both the complexity of medicine and the limits of current systems.
That said, recent years have marked a period of meaningful progress, much of which has occurred behind the scenes. Healthcare organizations have focused on infrastructure, validation, and narrow use cases rather than broad deployment. Many AI systems remain in controlled environments where performance can be evaluated before being exposed to real clinical risk.
In practice, organizations that have seen measurable benefits from AI have approached adopting it methodically. They invest in data quality and interoperability.
The reason some other places have failed is that they adopted it too quickly, thinking it could free up clinicians’ time. But this oversight frequently led to inadequate governance, resulting in uneven performance, unclear accountability, and growing skepticism among clinicians.
Studies show that AI systems trained on narrow or biased datasets can underperform for certain patient populations, reinforcing disparities rather than reducing them. These concerns have slowed adoption in many clinical settings.
As George Ellis, MD, a urologic surgeon and Founder of Coronado Beach Productions, explains, “AI models are only as good as the data on which they are trained. If this data is not diverse or contains historical biases, the AI might perpetuate or even exacerbate healthcare disparities, performing poorly for certain demographic groups.”
In short, medicine is complex, and technical capability alone is not enough. Effective AI deployment in healthcare depends on a refined scope, measurable outcomes, and clear human oversight.
Computers and clinical decision-making
When used correctly, one of the most visible advances in healthcare AI has been clinical decision support. Machine learning systems are being used for imaging studies, pathology slides, and diagnostic data. These tools match or exceed human accuracy in identifying early signs of disease, particularly when subtle visual patterns are involved.
Ellis notes this shift. “AI algorithms are consistently matching or surpassing human experts in interpreting medical imaging like X-rays, MRIs, CT scans, and pathology slides to detect subtle signs of diseases such as cancer, stroke, and diabetic retinopathy, often much earlier than a human can,” he says.
AI is also being applied to treatment planning. By integrating patient history, laboratory results, and genomic data, systems can support clinicians in selecting therapies that are better for the patient when all factors are considered. These tools are designed to inform decisions, not replace them, and their effectiveness depends on clinician interpretation.
Operational efficiency and administrative relief
Administrative tasks continue to be one of the biggest drivers behind clinician burnout. However, this is one area where AI systems can intervene to help clinicians automate documentation, eligibility verification, and coding.
“Artificial intelligence will help automate patient scheduling, eligibility verification, and data management and improve workflow by managing large volumes of data efficiently, reducing administrative burden and staff burnout,” Ellis explains.
Revenue cycle management has also benefited from advancements in artificial intelligence. AI tools can identify billing inconsistencies, reduce claim denials, and improve forecasting accuracy. While on the surface this doesn’t seem like a healthcare issue, it does help clinicians and medical staff worry about what matters most: their patients.
Augmentation, not replacement
Most healthcare leaders now view AI as a support tool rather than a substitute for clinicians. While algorithms excel at pattern recognition and scale, they cannot replicate professional judgment or patient relationships.
As Ellis states, “AI is primarily seen as an ‘augmented intelligence’ tool to support clinicians, and there are concerns about over-reliance and the potential for clinical de-skilling.”
The success of AI in healthcare will depend on integration that strengthens, rather than weakens, clinical expertise.
Digital Health Buzz!
Digital Health Buzz! aims to be the destination of choice when it comes to what’s happening in the digital health world. We are not about news and views, but informative articles and thoughts to apply in your business.
