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What is Rad AI?

Rad AI is an artificial intelligence tool designed to assist radiologists and medical imaging professionals with diagnostic tasks. The platform uses machine learning to analyse medical images, helping to identify potential abnormalities and improve diagnostic accuracy. It's built for hospital radiology departments, imaging centres, and clinical practices that want to reduce interpretation time while maintaining or improving diagnostic quality. The tool integrates into existing radiology workflows, allowing radiologists to use AI as a second reader or quality assurance checkpoint. By automating routine image analysis tasks, it can help radiologists focus on complex cases and reduce the cognitive load of reviewing large volumes of scans. The free pricing model makes it accessible to organisations wanting to trial AI-assisted radiology without upfront investment.

Key Features

Medical image analysis

Processes radiological images to detect and flag potential abnormalities

Multi-modality support

Works with various imaging types including X-ray, CT, and MRI scans

Diagnostic workflow integration

Fits into existing radiology software and PACS systems

Accuracy reporting

Provides confidence scores and reasoning for AI-generated findings

Batch processing

Can analyse multiple images and studies efficiently

Pros & Cons

Advantages

  • Free to use, removing cost barriers for smaller practices or research
  • Designed specifically for medical imaging, not a generalised tool
  • Reduces interpretation time on routine cases, freeing radiologists for complex work
  • Acts as a quality assurance check to catch missed findings

Limitations

  • Requires clinical validation before deployment in some jurisdictions; regulatory approval may be needed
  • Performance depends on image quality and may struggle with non-standard imaging protocols
  • Cannot fully replace radiologist expertise; requires qualified review of all results

Use Cases

Providing a second reader opinion for routine chest X-rays in busy emergency departments

Quality assurance checks on large batches of screening mammograms

Flagging priority cases in high-volume imaging centres to optimise radiologist workflow

Supporting radiologists in training by providing comparative analysis

Research applications in medical imaging studies and algorithm development