Code written by AI still needs a human review, especially in healthcare. Here is what can go wrong when no one checks it before patient data gets involved.
An AI tool writes the code, the feature runs, and it gets merged. Nobody sat down and read through the actual implementation line by line, because it worked in testing and the deadline was close. This sequence happens constantly across healthcare software teams, and most of the time nothing visibly goes wrong right away. That is exactly what makes it risky.
Sonar's 2026 State of Code Developer Survey of over 1,100 professional developers found that AI now accounts for 42 percent of committed code, yet 96 percent of developers do not fully trust AI generated code, and only 48 percent say they always verify it before committing. That gap between adoption and verification is the entire problem in one statistic. Teams are shipping more AI written code than ever while admitting, in the same breath, that they are not consistently checking it before it goes live.
It is not that engineers do not care about quality. It is that reviewing AI generated code is harder and slower than reviewing code a teammate wrote, since a human reviewer cannot rely on the same shared context or coding habits they would recognize from a colleague. The same Sonar survey found that 38 percent of developers say reviewing AI generated code actually takes more effort than reviewing human written code. Combine that extra effort with a deadline, and skipping a thorough review becomes the path of least resistance, especially when the feature already appears to work correctly.
Unreviewed AI code does not fail in an obvious, dramatic way most of the time. It fails quietly, in a handful of predictable patterns. A data access pattern pulls more patient information than a feature actually needs, because scoping a query narrowly takes more thought than a fast implementation gets. A debug logging statement added during development captures full patient records and never gets removed before production. An edge case involving unusual input, a name with unexpected characters, a session with concurrent requests, was never tested because the happy path worked fine. None of these show up in a demo. All of them show up eventually, usually at the worst possible time, in front of a customer's security team or during an actual incident.
An engineer at a behavioral health platform uses an AI tool to build a feature that lets clinicians export a client's session history as a PDF. It works on the first try, generates a clean looking export, and gets merged the same afternoon since the sprint is already behind schedule. Three months later, a routine review finds that the export function pulls every field from the patient record, including fields the export feature was never meant to surface, and that the temporary file it generates sits in a shared directory with broader access than the rest of the application. Nobody wrote this code maliciously or carelessly. It simply never got the kind of scrutiny a feature touching a full patient record actually needed, because it looked finished the moment it worked.
Unreviewed code carries real risk in any industry, but the consequences compound differently when the code touches protected health information. A bug in an e-commerce checkout flow costs a company money and an apology. A bug in a healthcare feature that exposes one patient's session notes to another user, or logs a diagnosis somewhere it should never live, is a HIPAA disclosure the moment it happens, regardless of whether anyone outside the company ever finds out about it right away. The review step that catches this in a typical software company is a nice-to-have. In healthcare, it is the difference between a normal engineering process and a compliance failure waiting to be discovered.
A proper review of AI generated code goes beyond confirming a feature works. It traces exactly where patient data enters the code, follows it through every function that touches it, and checks whether access controls, encryption, and logging behave the way they should at each step. It tests the code against unusual input, not just the input a developer happened to try while building the feature. And it checks whether the code matches the vendor agreements and data handling standards already in place elsewhere in the product, rather than treating each new AI generated feature as its own island. This is the kind of review our AI Acceleration Sprint runs specifically for AI built healthcare features.
Mostly a process problem. Automated scanning tools help, but the Sonar research found that teams pairing AI code generation with structured verification processes saw meaningfully better outcomes than teams relying on tooling alone, including notably fewer production outages traced back to AI generated code. The tooling supports the review. It does not replace the judgment of an engineer who knows what a healthcare specific failure actually looks like and knows to look for it deliberately, rather than assuming a passing test suite is the same thing as a secure feature.
Skipping review does not save the time it appears to save. It defers that time to a worse moment, usually a customer's security questionnaire, a compliance audit, or an actual incident, each of which costs considerably more to resolve than a planned review would have. Our pricing page lays out what a structured review costs against that comparison, and our FAQ page covers a few more questions teams ask before deciding to build this review into their own process.
Resolve Health Tech reviews AI generated healthcare code the way it should have been reviewed before it ever touched patient data. Contact us to find out what a real review would catch in your own codebase.
Sana is a Technical Content Specialist at Resolve Health Tech. She specializes in breaking down complex architectural patterns, nearshore hiring trends, and software engineering workflows into actionable, human-friendly guides. Working alongside Resolve Health Tech's tech team, Sana ensures every piece of content is both highly readable and technically precise.