What Is Medical Imaging?

Medical imaging, or taking pictures of the body’s interior anatomy, structures, and processes to diagnose, treat, and evaluate patient conditions, is among the most powerful tools for improving patient care. Further, medical imaging used in other clinical procedures, such as during surgery to guide the placement of complex IV lines, is helping to improve efficiency and reduce procedure times.

Advancements in medical imaging technology, including the integration of artificial intelligence (AI) and edge computing, allow healthcare providers today to see more with higher fidelity. AI is also being used to streamline workflows, automating real-time image analysis and analysis of previously collected image data to provide highly accurate insights to providers. This decreases the time waiting for results and empowers providers with information they need to provide better care.

Types of Medical Imaging Technology

The type of medical imaging used depends on patient symptoms and the part of the body being examined.

X-Ray Imaging

X-ray imaging, also known as radiography, is used by clinicians to diagnose conditions such as broken bones, tumors, pneumonia, and cavities. This type of imaging passes X-rays, a form of electromagnetic radiation, through the body to produce images of internal bones, joints, tissues, and organs on film or digital media. The denser the tissue, the less X-rays pass through, which affects how objects appear in the resulting image. Bones, which have a high density, appear white, while soft tissues with a lower density appear gray in an X-ray image. The amount of radiation exposure from X-rays is considered nominal; however, lead aprons are commonly used to protect patients during an X-ray procedure.

Computed Tomography (CT)

Computed tomography (CT) imaging uses a combination of X-rays and computer technology to scan a patient’s body for detailed pictures. A narrow beam of X-rays is aimed at and rotated quickly around the body to produce cross-sectional images. CT scans can help healthcare professionals plan procedures such as surgery, biopsy, and radiation therapy and can be an alternative to some types of exploratory or diagnostic surgery.

Magnetic Resonance Imaging (MRI)

While CT imaging uses X-rays, magnetic resonance imaging (MRI) uses strong magnetic fields and radio waves to produce three-dimensional detailed pictures of a patient’s interior anatomy and physiological processes. MRI provides better contrast of organs and soft tissues in images, which can help diagnose such conditions as torn ligaments and herniated discs.

Nuclear Imaging

Nuclear imaging uses radioactive tracers, called radiopharmaceuticals, to assess bodily functions and to diagnose and treat disease. Two of the most common nuclear imaging procedures are single photon emission computed tomography (SPECT), which provides 3D images of organ functions, and positron emission tomography (PET) scans, which can help reveal the metabolic or biochemical function of a patient’s tissues and organs. PET scans are often used for cancer diagnosis and treatment.

Molecular Imaging

Molecular imaging is a form of medical imaging that includes the use of various methods (MRI, CT, PET) with a contrast agent to visualize, characterize, and quantify biological processes in a patient. Molecular imaging is also used extensively in precision medicine during the drug discovery process to provide insights into the 3D structures of proteins and other biological entities.

Ultrasound Imaging

Ultrasound, also called sonography, uses high-frequency sound waves to show structures inside a patient’s body. It is best used to learn about soft tissue conditions, which include organs, glands, muscles, tendons, and blood vessels. Ultrasound is often helpful in diagnosing or ruling out potential causes of patient symptoms.

Artificial Intelligence (AI) in Medical Imaging

Radiography is among the most promising applications for AI, computer vision, and large language models. AI in medical imaging can interpret images faster than humans, recognizing patterns and anomalies that aren’t immediately obvious. For diagnosis, AI-enabled medical imaging can help clinicians perform their evaluations more quickly while removing variances and providing guidance on optimal patient positioning in scanners. Deep learning in medical imaging can help improve the diagnosis of cancer at earlier and more interventional stages.

Surgeons, too, can benefit from advanced AI in imaging. Neural networks can transform CT scans into precise 3D models that allow surgical teams to determine the best approach to an operation. The result is faster operating times, one of several benefits from AI imaging applications that help doctors target the right intervention at the right time.

AI is already being integrated into medical imaging applications and delivering value. Expert assessments forecast that by 2026 annual healthcare benefits from AI applications used for preliminary and automated image diagnosis will reach an estimated US$5 billion and US$3 billion, respectively.¹

Medical Imaging Use Cases

Early and accurate diagnosis not only improves patient outcomes but also lowers overall healthcare costs, often by preventing or reducing the need for invasive or compounding procedures.

Healthcare organizations are empowering their care providers with AI-enabled medical imaging devices to help them more efficiently and accurately diagnose conditions. This results in quicker turnaround times and a faster path to treatment for patients.

A few examples of the benefits of AI-assisted medical imaging include:

• AI-assisted detection of pancreatic cystic lesions: Pancreatic cystic lesions can evolve into pancreatic cancer. Accurate and early detection is critical to identify people at risk. Through AI imaging analysis of CT scans, radiologists can now provide a more precise diagnosis, monitor lesions throughout treatment, and aid in the prediction of lesion evolution.
• Reduced radiation therapy (RT) planning time: Radiation therapy (RT) planning is a complex process that relies on advanced imaging technology. Contouring organs at risk (OARs) is an essential step in the planning process during which RT professionals manually contour tens of organs on a CT dataset or other modality. This process is monotonous and time consuming, and the resulting contours can often lack consistency because they differ from specialist to specialist. AI-based automated organ contouring solutions can now help boost the efficiency and consistency of RT while freeing up professionals to focus on other important tasks.
• Faster identification of life-threatening conditions: X-ray images are used to identify a number of conditions and determine the best course of care. First-in-first-out radiology workflows can result in long turnaround times in diagnosis. Additionally, uncommon or hard-to-detect conditions, such as pneumothorax, can lead to life-threatening conditions if undiagnosed. AI-enhanced X-ray devices that automate initial image analysis are helping to identify and flag key findings faster, including those requiring priority radiologist review. This helps increase staff productivity and improve the speed and accuracy of diagnosis.

The Future of Medical Imaging

Advancements in medical imaging technology will continue to provide healthcare professionals and specialists with higher-resolution data, enabling providers to deliver more rapid and precise patient care across medical disciplines.

AI and machine learning will increasingly become the go-to tools for medical image capture and interpretation, potentially enabling visualization at the cellular level as technology and computing innovations continue to advance.

Healthcare organizations with connected digital systems will be able to combine data generated across all imaging devices into a single master file for a comprehensive view of a patient’s condition. AI and predictive analytics can then be applied to that data to inform diagnoses and treatment decisions.

Powerful MRIs and CT scans currently deliver 2D medical imaging; however, virtual reality (VR) and augmented reality (AR) technologies will likely bring transformation through 3D renderings. This capability will enable providers to rotate, zoom in, or make cross-sections of the 3D image using peripheral devices. Further, this capability could eventually allow projecting images like a hologram, where professionals can explore potential issues in detail before undertaking high-risk procedures.

Technological advancements will deliver new and evolving imaging techniques. For example, interventional radiology, in which minimally invasive medical treatments use medical imaging as a guide, will continue to grow in adoption as an option to conventional surgeries, offering less risk, less pain, and shorter recovery time for patients. Compressed sensing, an AI-powered technology that produces high-quality images from less data by extrapolating the missing pieces, will gain further traction and capabilities, helping to lower radiation dosages with CT scans and shorten MRI scanning times.

Preparing for the Future Today

To implement effective medical imaging solutions today with an eye on future-forward investment, it’s important to evaluate not only the individual solution but also the existing digital infrastructure that will support it and future innovative tools. Connected systems on a highly reliable and scalable platform are key to unlocking greater insights and efficiencies from shared data.

Working closely with technology partners to streamline the planning, implementation, deployment, and optimization processes can help enhance medical imaging system performance and responsiveness, improve data use, and accelerate time to value.