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How AI Is Changing Radiology
Radiology is a field that includes diagnosing and treating a variety of diseases using radiological equipment. This can specifically include X-rays, mammography, sonography, CAT scans, and MRI technology.
Healthcare IT News reports that the use of artificial intelligence (AI) by radiologists saw an increase of 30 percent from 2015 to 2020. Artificial intelligence is quickly changing how clinicians work in radiology and how the equipment they use can better serve patients and produce better outcomes.
Radiology AI Can Boost the Speed of Clinical Trials
It’s crucial that clinical trials run as quickly, yet as safely and effectively as possible in order to get new medications and treatments on the market. A variety of medical imaging is part of almost all clinical trials. Artificial intelligence and deep machine learning can play a major role in this process. There are several examples of how radiology AI can speed up clinical trials.
- Improve Longitudinal Measurements:Completing cross-sectional CT images automatically can result in highly repeatable calculations that will allow the identification of extremely small changes that occur in response to treatment.
- Track Neurological Progress:Machine learning can quickly and effectively measure changes in brain volume and structure. This can show a clinician if a drug or procedure is slowing the spread of disease.
- View Multiple Images More Quickly: Some imaging devices provide several images in quick succession. AI in radiology can effectively analyze dozens of images faster than an individual person.
Radiology AI Can Help Reduce Errors
According to Stat, death from medical errors is the leading cause of accidental death in the United States. Radiology AI can help medical professionals reduce errors and ultimately save lives. One way to reduce errors is to make sure each part of producing and interpreting medical images is as clean and efficient as possible.
Artificial intelligence can play a part in literally every step of the imaging process. This includes the initial process of taking the image, interpreting detailed findings, safe storage of the image, and determining follow-up healthcare for each patient. The following are a few specific examples of improvements using AI.
- Offer Optimal Image Analysis: AI not only can improve the quality of each imagebut can detect at the moment of imaging if the data is high enough quality for optimal analysis. If it isn’t, the system can alert the radiologist to decide if another scan is necessary.
- Recognize Abnormal Patterns: Algorithms can more easily recognize patterns than individual clinicians. AI can identify abnormalities in patterns that may help to more quickly pinpoint disease, masses, and fractures. This can reduce potential errors in diagnosis.
- Integrate Images into Patient Records: Radiology AI can facilitate the incorporation of each imaging result within the facility’s electronic medical record system. This can eliminate hospital errors by reducing the chance of misfiling a patient’s records.
- Provide Guidelines for Treatment Protocol: Artificial Intelligence can also alert clinicians of potential treatment options for each patient. AI can even make predictions regarding which treatments are most likely effective for the patient.
Radiology AI Can Improve Images in Less Time
Radiologists have many tools at their disposal when diagnosing or treating disease. Reading, interpreting, and reporting on various images is one of their primary responsibilities. There are several specific ways that artificial intelligence can improve images and ultimately improve healthcare outcomes.
- Low Dose Scans: Deep learning can now improve the quality and overall image when implementing low-dose scans. Both MRI and CAT scans are often difficult for patients because of the long scan times as well as the radiation exposure. Quicker scans reduce the time as well as the amount of radiation a patient receives.
- MRI Scans:Artificial intelligence is now in use to speed up the process of traditional MRI scans. The results are as accurate as traditional scans. It’s also important to note that rapid generating MRI scans using AI are diagnostically interchangeable with other types of MRI scans.
- AIR Recon DL: This image reconstruction software can reduce noise during the image reconstruction process, which is critical to receiving clearer images. AIR™ Recon DL from GE Healthcare uses deep learning algorithms to improve images by using raw data to reduce excessive noise.
Radiology AI Can Improve Workflows
Whether a radiologist is working with an MRI machine, running a PET scan, or even completing basic X-rays, medical imaging procedures are sometimes tedious and time-consuming processes. An end-to-end digital workflow becomes more productive when implementing artificial intelligence in every area of the process.
AI improves workflows when it involves more than just the radiologist. The process begins when a patient first schedules an appointment. It also involves creating a paperless work environment that stores all records digitally and creates an easy retrieval process. Retrieving a patient’s records and all relevant data is time-consuming. An AI system can bring together all of the following information quickly and with a low rate of errors:
- Previous Imaging Exams
- Previous Reports for Each Image
- Pharmaceutical History and Information
- All Lab and Pathology Reports
- Tracking Activity from Wearables
Radiology AI Can Identify Disease Earlier
Deep learning and algorithms can now spot abnormalities and detect disease more quickly than physicians. There are several specific types of conditions and diseases that artificial intelligence in radiology is finding.
- Earlier Detection of Lung Cancer: Lung cancer is one of the deadliest cancers, generally having a poor prognosis. Artificial intelligence systems can now figure out what cancer looks like after studying large data sets of thousands of scans that include healthy lungs and those with cancer. Instead of an individual having to look at each abnormality, radiology AI can learn what cancer nodules look like and identify tumors on its own.
- Virtual Native Enhancement: Heart imaging is now using artificial intelligence in a process similar to contrast-enhanced magnetic resonance scans. Virtual native enhancement can produce high-quality images of scars in the heart without the need of injecting contrast agents that are normally in use for cardiovascular magnetic resonance scans.
- Predict and Detect Alzheimer’s Disease: Millions of individuals are currently living with Alzheimer’s Disease in the United States. With the assistance of AI technology, there are now computer algorithms that can predict and diagnose Alzheimer’s with great accuracy.
AI implementation will continue to dramatically change the field of radiology in the future. Whether it involves more quickly diagnosing disease, speeding up clinical trials, or simply improving daily workflows, artificial intelligence is now part of promoting healthier lives.