RadAssist+-AI for radiology imaging analysis.

Example

RadAssist+ analyzes brain MRIs to detect early signs of multiple sclerosis, identifying demyelinating lesions that might be missed in an initial read.

Scenario

In a patient presenting with vague neurological symptoms, the radiologist uses RadAssist+ to assess the MRI, which helps in detecting small, periventricular white matter lesions indicative of multiple sclerosis, leading to an earlier and more accurate diagnosis.

Example

The system detects subtle lung nodules in a chest CT scan and compares them with previous imaging to assess growth or stability.

Scenario

During a routine follow-up for a patient with a history of smoking, a radiologist uses RadAssist+ to compare current and previous CT scans. The AI detects a subtle increase in the size of a lung nodule, prompting further investigation and a timely intervention.

Example

RadAssist+ identifies and categorizes thyroid nodules in an ultrasound image, recommending further FNAC based on standardized risk stratification.

Scenario

A radiologist evaluates a thyroid ultrasound for a patient with a palpable nodule. RadAssist+ classifies the nodule as highly suspicious based on its characteristics, suggesting a fine needle aspiration biopsy (FNAC), which is later confirmed to be papillary thyroid carcinoma.