AI technology is widely used in the prediction and diagnosis of diseases where diagnosis is based on imaging and can help radiologists and pathologists identify the onset of disease in patients earlier, make informed clinical decisions, and decide on effective treatments.
AI-powered imaging has already contributed to significant improvements in the accuracy of diagnosis, reduced human error, efficiency of treatment, and improved patient outcomes.
This article looks at the potential of AI in early disease detection and diagnosis, and areas where AI-powered imaging is already helping to transform healthcare.
Medical imaging refers to several different technologies that are used to view the human body in order to diagnose, monitor or treat medical conditions. It mainly involves non-invasive visualization techniques to identify injuries, or diagnose diseases or chronic conditions.
Whether images are in the form of X-rays, CT or MRI scans, radiologists must devote valuable time to reviewing them. AI-enabled medical imaging tools can help significantly reduce this workload, while enabling faster diagnosis and earlier intervention by providing automated analysis.
The application of AI analysis to medical imaging also helps to identify problem areas or details that may be missed by the human eye.
Cardiovascular abnormalities can often be diagnosed by measuring the structure of the patient’s heart. AI-based medical imaging is used as a screening tool to help identify left atrial enlargement, including the automation of key procedures such as carinal angle measurement, which indicates a cardiac problem if it’s greater than 100 degrees.
AI-powered medical imaging technology helps diagnose neurological conditions such as amyotrophic lateral sclerosis (ALS) by successfully extracting relevant information from brain images and highlighting any images that show anomalous results. AI can also flag early-onset Alzheimer’s disease with around 84% accuracy, allowing researchers to find potential clinical trial candidates for drugs that slow neurodegeneration.
Medical imaging is widely used in the screening of cancers, particularly breast and colon. Tissue microcalcification, which shows as tiny white dots on a scan, can be an early sign of breast cancer but it’s difficult to identify the few malignant cases from the majority of benign cases. AI-powered imaging improves the accuracy and precision of this detection, helping to avoid false positives that may lead to unnecessary treatment.
The use of AI-powered MRI imaging has reduced the time it takes to classify a brain tumor from around 40 minutes to just a few minutes, with an accuracy of 98.56%. And a recent UK AI study found a non-invasive method to better classify tumors in children.
Injuries such as a hip fracture are common causes of long-term chronic pain among elderly patients, and reduced mobility can lead to poor outcomes. AI-powered tools can detect subtle variations in medical images and are being widely used to detect fractures or dislocations that are hard to detect using standard imaging techniques with the human eye.
In a critical care scenario such as a stroke, AI-powered medical imaging can reduce the time between a CT scan and diagnosis by over half an hour, improving the patient outcome.
Chronic kidney disease often shows few symptoms until it is very advanced, creating the need for a swift and accurate assessment to gauge the severity. A renal biopsy can detect the amount of fibrosis and tubular atrophy, but it requires a pathologist with the necessary expertise. So AI algorithms have been trained to assess the damage and predict how long a kidney will function adequately in a patient.
AI-powered imaging technology now enables clinicians to identify abnormalities and detect diseases with a higher level of precision and speed than ever before. The application of AI in the assessment of scans has led to accurate evaluations being performed automatically, and this in turn has reduced the workload of healthcare professionals, and improved the efficiency and quality of treatment.
As the technology develops, AI’s ability to improve the detection of rarer and more complicated diseases, plus enable better predictive and preventative medicine, will continue to grow.