A page of lab values is not a report. Patients want to know what their results mean, and clinicians want to spend their time on patient care rather than typing up the same explanations. AI-assisted reporting aims to bridge that gap, but not all approaches are equal.
What an AI-assisted report can do
At its best, an AI-assisted report takes the results for a set of tests, presents them in a clear, clinic-branded format, and offers an interpretation, possible causes and recommendations written for a clinician to review. The aim is to cut the time spent drafting, not to replace clinical judgement.
Six things to look for
1. Clinician review
The report should support a clinician reviewing it before it reaches a patient. Some clinics may choose automatic filing for speed, but that should be a deliberate choice by the clinic, with the responsibility that comes with it.
2. A clear intended purpose
Ask what the software is designed to do and what it is not. Decision-support tools should be explicit that they assist clinicians rather than make diagnoses. Check the provider's regulatory status, for example whether the software is registered with the MHRA and how it is classified.
3. Consistency
Every patient should receive a report to the same standard and structure, regardless of clinic size or plan. Consistent structure also makes reports easier for clinicians to review quickly.
4. Your branding
Patients receive reports from your clinic, so they should carry your name and look, not a supplier's.
5. Transparency about limitations
Reference ranges, context and clinical history matter. A good report is honest about what a blood test can and cannot show, and encourages appropriate follow-up rather than overstating certainty.
6. Data protection
Blood test data is special category health data under UK GDPR. Ask how data is stored, who can access it, how long it is kept, and what agreements are in place with the provider.
Review it yourself
The best way to judge AI reporting is to read a real example, ideally with a colleague, and ask whether you would be comfortable putting your clinic's name on it after a quick review. You can download an anonymised example on our example report page, which also explains how to check what you are reading.
Where it fits in your workflow
AI reporting works best as one stage in a joined-up process: order the test, receive the results, generate the report, have a clinician review it, and file it in the patient record. For Cliniko users, we describe this in our guide to integrating blood test reports with Cliniko.
This article is general information and does not constitute clinical or regulatory advice.