Should You Build Your Own AI Radiology Reporting Tool?

If you have tried AI on a synthetic dictation, building your own reporting tool can seem quite reasonable. A text box, a prompt and a result pane can make a useful prototype. We understand the appeal. RadPro began with a radiologist's vision of better reporting. The question is what you want to own after the prototype works.

There are good reasons to build.

Control is the obvious benefit. You can choose the report structure, decide how much editing the AI may do and fit the interface to a particular workflow. A narrow tool for a well-defined task may serve you better than a broad product. Building also teaches you what prompts handle well and where apparently fluent answers go wrong.

Cost can be attractive, too. For a small experiment, direct usage charges may be modest, and you avoid paying for features you do not need. But compare the whole cost: development time, hosting, security review, monitoring and ongoing support. Building can be the right choice when you have the skills and want that responsibility. A low charge for generating text does not describe the cost of running a service.

Patient information changes the project.

Start with synthetic material. Before introducing patient information, work out whose authority you need, which laws apply and what your suppliers do with the data. Ontario's PHIPA and the US HIPAA framework have different roles and requirements. Neither is satisfied by putting a privacy statement under a text box.

Ontario's privacy commissioner explains that custodians must take reasonable steps to protect personal health information against unauthorised access and other threats. In the US, HHS guidance on cloud computing explains risk analysis and business associate agreements where HIPAA applies, including obligations when a cloud service stores encrypted information without the key. Encryption alone does not settle the legal relationship.

For hospital radiologists, our own Privacy Policy requires hospital approval before patient information is entered into RadPro. A personal subscription does not supply that permission. Treat the authorisation for a tool you build as an explicit question for your organisation, too.

Protect more than the prompt.

De-identification is a process to check, not a promise that a name-removal expression makes text anonymous. Identifiers can appear in headers, copied correspondence and unexpected places. RadPro highlights detected identifiers for review and can replace them with placeholders before AI processing. Detection can miss things, so the review matters. We evaluate detector changes against labelled synthetic examples, including difficult formats, rather than assuming a clean-looking paragraph is safe.

A tool also needs encryption in transit and at rest, controlled access to keys and a plan for retention and deletion. RadPro encrypts stored report text and the mappings used to restore identifiers. Server-side checks constrain where report text can be processed; some features refuse text that has not passed the required privacy checks. These controls address different parts of the journey. None makes every possible input automatically de-identified.

Audit records should help explain who accessed or changed something without becoming another copy of the clinical text. RadPro records actions such as sign-ins and report access, and its admin report views show metadata rather than report text. Session controls and multi-factor authentication matter on shared workstations. For a home-built tool, ask whether debugging output, browser storage and backups respect the same rules.

Test what happens when the input fights back.

Pasted text can contain instructions that try to redirect an AI away from its task. Prompt-injection defence needs more than asking it to ignore malicious instructions. RadPro uses local pattern checks and, on eligible de-identified paths, an additional screening layer. That layer is not used for every request, and reporting can continue if it is unavailable. It is a defence with limits, not a guarantee that every attack is blocked. The radiologist still reviews the output.

The maintenance continues after a good demo.

A model change can alter omissions, wording, latency and how closely instructions are followed. Keep representative test cases and compare the output before accepting a change. Check negations, laterality, measurements, uncertainty and unsupported additions. RadPro uses synthetic tests and comparative evaluations to inform changes; that work does not remove the need to review each clinical report.

Uptime is another responsibility. Decide what happens during a timeout, an interrupted response or a service outage. RadPro has retry and fallback handling for some failures, but that is not a promise of uninterrupted service. You need a way to finish reporting when the assistant is unavailable. Updates, dependency fixes, incident response and support all remain work after the first successful run.

Why our team grew beyond the original idea.

RadPro started with a radiologist and a vision. It then expanded to include mainly security and compliance experts, whose work helped bring it to what it is today. The reporting idea was the beginning; making the surrounding service something people could responsibly use required broader expertise and continuing attention.

Build if that ownership is part of what you want. Choose an existing service if you would rather evaluate its safeguards and workflow than maintain your own. Read our security information, try a synthetic case on the free plan, and ask the same practical questions of RadPro that you would ask of your own tool.