The most dangerous AI coding error isn’t an obviously wrong answer. It’s a convincing one.
A system reads a medical record, recommends a code, assigns it high confidence, and moves on. Everything looks right. Until someone discovers a missing modifier, an overlooked clinical detail, or a procedure that doesn’t match the documentation.
Now the claim is denied. Payment is delayed. Someone has to unravel what went wrong.
And “the AI recommended it” isn’t a defense.
As healthcare organizations accelerate AI adoption, we’re asking the wrong question. It’s not how quickly AI can generate a code. It’s whether that recommendation can withstand scrutiny.
Because faster coding means nothing if we’re accelerating errors.
Confidence is not proof
Generative AI produces plausible answers. But medical coding isn’t a language exercise. It’s a discipline built on clinical evidence, precise rules, and decisions affecting reimbursement.
One word can change everything.
Left versus right. Biopsy versus removal. Personally performed versus medically directed anesthesia.
These distinctions determine which code is appropriate, whether a claim is compliant, and how a service gets reimbursed.
Yet AI demonstrations emphasize speed, automation rates, and confidence scores. Those metrics alone tell us little about whether the technology can be trusted.
A model’s confidence is not proof of accuracy. Neither is an impressive demonstration.
An explanation isn’t evidence
Interpretability is one of AI’s biggest challenges: understanding why complex models produce the answers they do.
Even researchers developing today’s most advanced models struggle to fully understand their internal decision-making.
For medical coding, this creates a critical distinction.
An AI system can recommend a code and generate a convincing explanation. But that explanation doesn’t necessarily reveal how the model arrived at its answer.
We can’t simply look under the hood and verify its thinking.
Instead, we need processes that independently evaluate recommendations against clinical documentation, coding guidelines, payer requirements, and relevant coding relationships.
That means examining supporting evidence, evaluating alternatives, and validating recommendations rather than trusting explanations at face value.
Even these safeguards aren’t foolproof. Multiple models can agree on the same mistake. Evidence can be incomplete or misapplied.
The goal isn’t AI that sounds certain. It’s AI whose recommendations can be challenged and independently verified.
What is the AI actually optimizing for?
Another challenge deserves attention: alignment.
In AI development, alignment concerns whether a system’s behavior reflects its intended goals and constraints.
For medical coding, that raises uncomfortable questions.
Is the system selecting the most accurate code supported by documentation? Is it taking shortcuts to improve performance metrics? Could optimizing for higher reimbursement encourage unsupported recommendations?
A system can perform well against one metric while creating risk elsewhere.
In healthcare, that’s not simply a technology problem. It’s a compliance problem.
Organizations must understand what their AI systems optimize for and establish safeguards against undesirable outcomes.
Getting an answer isn’t enough. We need confidence that the system is pursuing the right objective.
Human accountability isn’t optional
There’s a temptation to view human review as a temporary inconvenience on the road to full automation.
I think that misses the point.
Coding professionals interpret documentation, apply changing rules, identify exceptions, and recognize when something doesn’t add up.
AI should make that work faster, not eliminate the judgment behind it.
When technology presents a recommendation with supporting evidence, a coder can verify it, challenge it, or reject it. Those decisions can also improve future recommendations.
The objective isn’t removing expertise. It’s combining computational speed with the accountability healthcare demands.
Raise the standard
Healthcare executives evaluating AI coding solutions should ask harder questions.
What evidence supports each recommendation? How is it validated? What is the system optimizing for? How are errors corrected? Who is accountable when it gets something wrong?
If a vendor can’t answer those questions, an impressive automation rate should offer little reassurance.
AI has enormous potential to improve medical coding. But speed without accuracy creates risk, and automation without accountability creates exposure.
A code generated in seconds is impressive. A code you can defend is valuable.
Healthcare should demand both.
About the Author
Doug Marcey is Chief Technology Officer at Coronis Health, where he leads technology initiatives focused on artificial intelligence, automation, and data-driven innovation in healthcare revenue cycle operations.
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