The Role of AI in Medical Imaging Diagnostics

By Katie Brenneman, Writer, Researcher
X: @KatieBWrites93

Like other aspects of modern society, AI (Artificial Intelligence) dominates conversations in the medical field. Its current and future benefits for healthcare professionals are remarkable and, for patients, potentially lifesaving. AI in medical imaging diagnostics is changing the future of radiology by assisting doctors in making more accurate, personalized diagnoses for patients.

Radiology has advanced significantly since the development of the MRI in the 1970s. Within a few years, with Picture Archiving and Communication Systems (PACS), radiology evolved from film-based imaging to digital. Next came 3D imaging and interventional radiology, which provided better images for minimally invasive procedures. The results were sharper, more precise images and quicker patient recovery time. Now, AI is ready to guide radiology into a cutting-edge technological future. With AI, imaging becomes even more precise than interventional radiology, with better patient outcomes. Here’s how AI can make a difference in radiological healthcare.

Understanding AI and Medical Imaging

AI (Artificial Intelligence) is a machine’s ability to mimic human intelligence through human traits like reasoning, perceiving, problem-solving, and creativity. Using machine learning, AI “intelligence” is based on algorithms trained to find certain data. These algorithms allow AI to learn new data and information to improve efficacy over time.

All medical imaging uses narrow AI to analyze images for the absence or presence of specific markers. It can find pneumonia in an X-ray or locate pulmonary nodules, or brain bleeds on CT scans. Narrow AI has proven incredibly beneficial to radiologists, allowing them to improve patient care and be more efficient.

Medical imaging is a growing market where radiologists use forms of radiation with body tissue to assess a medical condition in image format. It includes various technologies that help healthcare professionals view the human body to diagnose, observe, and treat illnesses and diseases. Typically, medical imaging involves staff using non-invasive visualization tools to identify chronic conditions, diseases, or other injuries.

Applying AI to Medical Imaging

You can apply AI analysis to medical imaging to identify potential problems that are small and missed by the human eye. AI imaging can highlight and analyze data in a medical report and differentiate between signals that indicate noise and those that show disease. Here are other ways in which using AI medical imaging can improve diagnoses.

Image Recognition
Through image recognition, AI imaging can detect medical problems earlier. AI automates reading images, no matter the type — CT scans, X-rays, ultrasounds, or MRIs — and quickly, saving radiologists time. Software specialists write AI imaging applications and use computer-aided algorithms to determine normal findings from abnormal ones. The deep learning algorithm is responsible for these tasks, making detecting abnormalities and disease types in images easier.

Many imaging types are necessary to detect gene mutations that may indicate conditions such as Down syndrome, Huntington’s disease, and sickle cell anemia. AI imaging tools make a clear difference, allowing for early genetic mutation diagnoses, which can save and improve some patients’ lives. This benefit alone shows how much the industry needs advanced imaging and detection.

Pattern Analysis
With proper training, an AI machine learning program can use pattern analysis to find repeated markers in bio-data more accurately than people. AI easily identifies new patterns that people cannot see or hear. Doctors who notice previously unknown patterns may develop further information to help diagnose, understand, and treat medical conditions better.

Process Improvement
AI can work wonders for the healthcare industry, but only if it is managed through process improvement. Inefficiency and waste are two factors that often hound healthcare companies and facilities. Physicians can implement process improvement by reviewing repetitive, time-consuming tasks, streamlining products or services, and saving money through better allocation of time, resources, and employees.

By integrating process improvement with AI in medical imaging, healthcare workers can, through formal or informal processes, analyze, review, redesign, implement change, or add other resources to ensure they have the best process systems in place.

The Benefits of AI in Medical Imaging

There are various ways that AI benefits the healthcare system and the public. It accelerates research speed and assists clinicians in improved diagnoses and treatments. Aside from helping to identify tumors, illnesses, and deformities from imaging, AI also improves radiology in the following areas:

Improved Accuracy
AI machine learning helps doctors identify diseases like cancer and cardiovascular conditions more accurately and consistently. The U.S. Food and Drug Administration approved roughly 420 AI medical imaging programs with 80 – 90% image accuracy rates. Better accuracy means that AI imaging can help identify medical conditions that are trickier to diagnose, such as strokes and aneurysms. With AI, doctors can diagnose diseases early, giving patients access to treatment faster.

Increased Speed
AI medical imaging allows for faster diagnoses and treatment. Healthcare professionals can detect medical conditions more quickly, leading to earlier interventions. Where radiologists once used valuable time to read these images, an AI tool can use algorithms and round-the-clock computer power to identify a disease quickly, saving physicians precious time.

Contrast and Dose Reduction
A lesser-known benefit of AI medical imaging is optimized radiation dosing, which reduces radiation doses and intravenous contrast agents in cancer patients. Pediatric patients, in particular, can benefit here as a reduction in radiation minimizes any elevated cancer risk in children and young adult patients.

AI deep learning also advances and speeds up imaging reconstruction and processing. It processes images with low to no radiation dose while maintaining excellent image quality. Eventually, this type of AI deep learning could eliminate CT scans entirely.

The Impact of AI on Patient Care

Aside from increasing the speed and ability to diagnose diseases while remaining accurate, AI medical imaging positively impacts patient care. Some of these benefits include:

  • Improved satisfaction amongst patients due to faster diagnoses.
  • Reduced radiation exposure because of image accuracy.
  • Improved prioritization, where scans are automatically prioritized based on the severity of the diagnosis.
  • Improved patient access to radiology globally by making decisions without human bias.

Additionally, healthcare systems that employ AI technologies can offer patients more personalized treatment. AI learns and stores patient preferences and can provide real-time patient recommendations anytime.

Revolutionizing the Healthcare Industry with AI

The relationship between medical imaging and AI is brimming with potential. Many practices and hospitals are already beginning to use AI for medical imaging, especially for its subset of machine learning capabilities.

AI medical imaging provides workflow efficiency, diagnostic accuracy, and personalized patient care. Improving care and patient outcomes will revolutionize the healthcare industry and benefit patients in many ways. Continued process improvement in AI-powered medical imaging will enable doctors to provide precise and customized patient care for years.