AI-assisted report writing in respiratory labs uses machine learning and natural language processing to help clinicians structure, draft, and refine reports based on patient test data. It does not replace clinical judgment. Instead, it acts as an intelligent layer on top of existing workflows, reducing manual effort, improving consistency, and helping busy labs keep pace with increasing patient volumes. Rezibase, Australia's most advanced respiratory and sleep solution, has embedded this capability directly into its pulmonary function test software in a way that is practical, guideline-aligned, and grounded in how respiratory scientists actually work.

TL;DR

• AI-assisted report writing supports, not replaces, the clinical expertise of respiratory scientists and reporting doctors.

• When built into purpose-designed pulmonary function test software, AI can meaningfully reduce documentation burden and improve report consistency.

• Current research highlights AI's growing role in respiratory care, from spirometry interpretation to ventilator management.

• Rezibase integrates AI-powered report writing within a structured, ATS guideline-aligned reporting workflow.

• Understanding what AI can and cannot do in this context helps labs adopt it confidently and responsibly.

About the Author: This article is written by the Rezibase team, specialists in respiratory and sleep lab software with over 37 years of combined experience supporting clinical physiology labs across Australia, New Zealand, the United Kingdom, and Ireland.

What Does AI-Assisted Report Writing Actually Mean in a Respiratory Lab?

AI-assisted report writing, in the context of a respiratory lab, refers to software that uses clinical data from a test such as a spirometry or full pulmonary function study to automatically generate structured, contextually appropriate language for a clinical report. The AI does not interpret the patient. It uses the discrete data already captured to suggest or draft text that a clinician then reviews, edits, and approves.

This distinction matters. The output is a starting point, not an endpoint.

Key functions AI typically supports in respiratory report writing:

• Generating structured report text based on test results and patient context

• Flagging inconsistencies or patterns that may require clinical attention

• Improving sentence structure and report clarity

• Ensuring language aligns with reporting standards such as ATS guidelines

What Is AI-Assisted Report Writing NOT?

This is where clarity matters most, especially for labs evaluating new technology.

AI-assisted report writing in this setting is not:

• A diagnostic tool. The AI does not make diagnoses. The reporting doctor does.

• A replacement for clinical judgment. No algorithm accounts for the full clinical picture the way a trained clinician does.

• An autonomous system. Every report still requires review and sign-off by an authorised medical professional.

• Infallible. Like any tool, it requires appropriate oversight and configuration.

Research published in 2026 by Soriano et al. in npj Primary Care Respiratory Medicine noted that AI is increasingly involved in monitoring and prediction functions in respiratory care, including shortening weaning from mechanical ventilation and guiding closed-loop strategies [The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review | npj Primary Care Respiratory Medicine]. Even in those more advanced applications, the literature consistently positions AI as an assistant to clinical decision-making, not a replacement for it.

What Does the Research Say About AI in Respiratory Care?

The evidence base for AI in respiratory medicine is growing, and the findings are worth understanding before making technology decisions.

A 2024 review by Al-Anazi noted that AI systems have demonstrated potential in detecting and categorising lung disorders using spirometry data [Artificial intelligence in respiratory care: Current scenario and ...]. This is directly relevant to respiratory labs, where spirometry is one of the most commonly performed tests.

Karthika et al., also writing in 2024 in Frontiers in Respiratory Care, highlighted that AI is already embedded in dual, hybrid, and intelligent ventilator modes, and will play a growing role in assisting clinicians managing ventilated patients [Frontiers | Artificial intelligence in respiratory care].

What these papers share is a consistent framing: AI as capability-enhancer, not decision-maker. That framing should guide how labs think about AI in their own workflows, including in report writing.

How Is Rezibase Using AI in Report Writing?

Rezibase has integrated AI-powered report writing into its streamlined doctor reporting workflow in a way that is deliberate, bounded, and clinician-facing.

Here is how it works in practice:

1. Test data is imported via Rezibase's Magic Import feature, which automatically extracts discrete data from device reports, including flow-volume loops, without manual re-entry.

2. The AI assists with report structure and language, suggesting text based on the extracted data and the reporting context.

3. The reporting doctor reviews the drafted report, uses the structured list view to manage their queue, and can use medical dictation alongside the AI-generated content.

4. Algorithms aligned to ATS guidelines support interpretation consistency, reducing the risk of reports drifting from current standards.

5. The doctor approves and signs off, retaining full clinical authority over the final document.

This is AI working within a defined clinical workflow, not operating outside of it. The goal is to reduce the low-value, time-consuming parts of report writing so clinicians can focus on the parts that genuinely require their expertise.

Why Does It Matter That Pulmonary Function Test Software Has This Built In?

Standalone AI tools added onto legacy systems create friction, compatibility issues, and data integrity risks. When AI is built natively into purpose-designed pulmonary function test software, the workflow is coherent from end to end.

Rezibase was designed by respiratory scientists Peter Rochford and the late Jeff Pretto specifically to address the frustrations of real labs: clunky software, double data entry, vendor lock-in, and systems that fall behind evolving standards. AI-assisted reporting is not a bolt-on feature. It sits within a platform that also manages normal values, accreditation, integrations with hospital systems (PAS, EMR, DICOM, and more), and the full patient lifecycle.

This matters because the clinical value of AI in report writing is only realised when the surrounding system is trustworthy. Garbage in, garbage out applies here as much as anywhere.

Frequently Asked Questions

Does AI make clinical decisions in Rezibase?
No. AI in Rezibase assists with drafting and structuring report text. All clinical decisions and final report sign-off remain with the reporting doctor.

Will AI-generated report suggestions always be correct?
No tool is infallible. The AI works with the discrete data available and the configuration in place. Clinician review is always required before a report is finalised.

Does using AI in report writing reduce compliance with ATS guidelines?
The opposite is intended. Rezibase's reporting algorithms are specifically designed to align with ATS guidelines, and the AI operates within that structure.

Is the AI feature available to all Rezibase users?
AI-powered report writing is part of Rezibase's streamlined doctor reporting module. Speak with the Rezibase team to confirm module availability for your site configuration.

How does AI interact with imported device data?
Rezibase's Magic Import function extracts discrete data from device reports automatically. The AI then works with that structured data to assist with report drafting, avoiding the risks associated with manual transcription.

Is this technology relevant for sleep reporting as well?
Rezibase covers both respiratory and sleep labs. While AI-assisted report writing is discussed here in the respiratory context, the platform's broader capabilities extend across sleep reporting workflows.

What if we want to move to Rezibase from our current system?
Switching is more straightforward than most labs expect. The Rezibase team manages the data migration process, and the platform's cloud-based design means there is no complex local infrastructure to navigate. Most transitions are handled with minimal disruption to daily operations.

About Rezibase

Rezibase is a cloud-based respiratory and sleep reporting platform built by respiratory scientists, for respiratory scientists. Trusted by over 35 sites including NHS hospitals in the UK and NSW Health in Australia, Rezibase combines AI-powered report writing, Magic Import, ATS-aligned algorithms, accreditation management, and deep hospital system integrations into a single, vendor-neutral platform. With 37 years of experience behind it and a transparent, no-lock-in pricing model, Rezibase is built to make respiratory and sleep labs more efficient, more accurate, and better equipped for modern clinical demands.

Curious about how AI-assisted reporting could work in your lab? Visit rezibase.com to learn more or book a conversation with the team.