Trust and AI Adoption in Medicine

If anyone thinks AI hasn’t been in healthcare for a long time, they haven’t been paying attention. The healthcare industry ‌adopted AI long before people even knew about it. In fact, it’s one of the fastest-growing industries with AI. As AI has evolved, though, so has its function in medicine. 

What started as a set of administrative tools to reduce the burden‌ has expanded into touching every piece of documentation, workflows, patient communication, and even decision-making. The opportunity for AI in healthcare is already active, but so is the risk. 

The challenge isn’t whether AI can be used (it already is). It’s whether it can be trusted. And trust in medicine is not something healthcare professionals can afford to get wrong. 

Moving faster than understanding

Even though AI has been in the healthcare industry for a while now, it is still experiencing a pacing problem. The reality is that AI offers a clear path to efficiency. And more healthcare professionals are willing to jump right in if it means charting is done faster and documentation is completed while talking to patients. 

The problem is that teams are implementing these tools before they fully understand the scope of how they work. They don’t understand how the data is being stored, or how it’s used, or where it goes. In many cases, AI is being layered into existing workflows without clear guardrails, simply because it seems like the next logical step. 

And that’s where the problem starts

AI isn’t good or bad, or trustworthy or untrustworthy. It’s just faulty. But mostly, it’s probabilistic. It generates outputs based on patterns, not certainty. And while those outputs can look polished and confident, that does not make them correct. 

In practice, AI behaves less like a system of record and more like an impulsive assistant. It will reply quickly and sound correct. But it will occasionally be wrong in ways that are easy to miss if you are not paying close attention. 

And in healthcare, that attention to detail matters

Trust is fragile, and AI can easilty break it. The healthcare industry has been under attack recently, and many people don’t trust medical professionals. The truth is, patients are more informed, and therefore more skeptical. And clinicians are overextended. 

AI comes in with the promise to help clinicians, but also potentially with more risk if not used correctly. When used well, AI can help reduce friction by streamlining documentation, improving access, and helping providers focus on patient care. But when used without transparency, it can create confusion, and in some cases, real harm. 

And there are already warning signs. 

Patients reviewing their medical records are beginning to notice AI-generated notes. In some instances, those notes include language or assumptions that the patients did not explicitly consent to. And that’s where tensions start to grow. 

If patients feel that decisions or records are being shaped by non-human systems they do not see or understand, their trust in their doctors will further decline. And once that trust is gone, it’s difficult to rebuild. 

Internal use doesn’t eliminate risk

Many organizations assume that using AI for internal purposes may have a lower risk. But professionals need to look back and re-evaluate those assumptions. Internal use doesn’t mean it’s isolated. Even when used for creating SOPs, user manuals, or internal documentation, it still holds sensitive data that could come back to the patient. 

When teams use browser-based AI tools, information often leaves the organization’s firewall. Even if the data seems harmless on its own, it can become sensitive when combined with other inputs over time. What looks like a small piece of operational detail may contribute to a much larger, unintended picture. 

That risk compounds when employees are experimenting independently. And they are, whether leadership wants to accept it or not. 

Most organizations do not yet have clear policies around what can and cannot be entered into AI tools or even which AI tools can be used. While most healthcare facilities have AI integrated, the staff may not know it. Without guidance, well-intentioned use can quickly lead to exposure. 

All AI use should be governed within the firewalls of the organizations and people within that organization should know how to properly use it. 

Slowing down to get it right

Health systems realize the most value from AI when they adopt it deliberately, define its purpose clearly, and establish guardrails as part of implementation rather than as an afterthought.

Asking the right questions will help organizations in implementing AI. What problem are you trying to solve? Where is time being lost? What processes are broken and inefficient? 

Without those answers, AI becomes a solution in search of a problem. And, well, that’s the problem. 

When asking through those questions first, the introduction of AI across each part of the healthcare organization will be much more intentional and pose lower risk. 

It’s important to define how AI will be used and establish what is off-limits. It’s also important to create policies before board adoption. Pilot tools in controlled environments and involve people who will actually use them to provide early feedback. And more importantly, have a healthy dose of skepticism. 

AI is a powerful tool, but it is still just a tool. It reflects the inputs it is given and the systems it is built on. Both of which are inherently imperfect. 

Trust should not be assumed; it should be earned through transparency and oversight.

Photo: ismagilov, Getty Images

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