Vendor source
Reviewed 11 May 2026
Completeness Comprehensive (86%)
Harrison.ai Chest X-Ray
Harrison.aiHarrison.ai Chest X-Ray is an AI clinical decision-support solution for chest X-ray, assisting clinicians to interpret chest X-Rays studies by detecting up to 124 findings + suspected tuberculosis. It acts as a second pair of eyes providing notification of suspected findings. Features include: • analysing up to three images per study including frontal and lateral images • a confidence bar displaying the likelihood of the finding and uncertainty of the AI model • customisable user interface that integrates seamlessly into PACS and RIS. • worklist triage
* In the US, FDA 510(k) clearance as CADt for five acute findings: pneumothorax, tension pneumothorax, pleural effusion, pneumoperiotneum, and verebral compression fracture.
** Formerly known as Annalise Enterprise CXR
Supported by 10 papers
General Information
Product name
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Main task
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Disease targeted
Regulatory Information
Pathway:
MDR
Class:
Class IIb
Verified by Health AI Register
Pathway:
510(k) cleared
Class:
Class II
Verified by Health AI Register
Other certifications
Not specified
Evidence & Research
Peer-Reviewed Papers
Evaluating the impact of AI assistance on decision-making in emergency doctors interpreting chest X-rays: a multi-reader multi-case study
Emergency Medicine Journal · Oct 2025
The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs
Journal of the American College of Radiology · Sep 2024
Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting
Radiology · Aug 2024
Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction
Radiology · Jan 2024
Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion
Radiology · Sep 2023
Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
JAMA Network Open · Dec 2022
Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma
The British Journal of Radiology · Mar 2022
Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study
BMJ Open · Dec 2021
Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography
BMJ Open · Dec 2021
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
The Lancet Digital Health · Jul 2021
Technical Papers
Poster BIR Annual Congress: Radiologist reporting productivity benefits from AI-assisted triage of CXR studies in clinical practice
Poster RSNA: How normal is a normal chest X-ray: Does a comprehensive artificial intelligence model identify significant findings in chest radiographs interpreted as normal in clinical practice?
Poster UKIO: Insights from implementation of an artificial intelligence assist device across a national radiology network
Poster ECR: Radiologist’s feedback post implementation of a comprehensive AI assist device for CXR across a large radiology network
Poster ECR: Remarkable vs Unremarkable Triage of Chest x-rays based on a comprehensive AI model – validation on a ground truthed, real world dataset.
Technical Specifications
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Source: Vendor | First published: Jul 9, 2024 | Last updated: May 11, 2026