{"id":9203,"date":"2025-06-26T20:35:29","date_gmt":"2025-06-26T20:35:29","guid":{"rendered":"https:\/\/www.vivian.com\/community\/?p=9203"},"modified":"2025-06-26T20:35:29","modified_gmt":"2025-06-26T20:35:29","slug":"how-artificial-intelligence-is-transforming-medical-imaging","status":"publish","type":"post","link":"https:\/\/www.vivian.com\/community\/industry-trends\/how-artificial-intelligence-is-transforming-medical-imaging\/","title":{"rendered":"How Artificial Intelligence Is Transforming Medical Imaging"},"content":{"rendered":"

A decade ago, deep learning prototypes wowed conferences but rarely touched patients. By June 2025, <\/span>777 artificial intelligence-enabled devices<\/span><\/a> had received Food and Drug Administration (FDA) clearance, and <\/span>two-thirds of U.S. radiology departments<\/span><\/a> used AI in some capacity. This rapid shift pairs radiologists\u2019 pattern-recognition skills with machines that never tire, promising faster scans, sharper pictures and earlier answers.<\/span><\/p>\n

FDA Approvals Mark AI\u2019s Clinical Coming-of-Age<\/b><\/h3>\n

The FDA continuously updates its <\/span>list of devices that utilize AI and machine learning (ML)<\/span><\/a> technologies, which has shown exponential growth since 2018. Algorithms for stroke, breast cancer and lung nodule detection dominate the list. AI\/ML has become a tool that radiology departments and other healthcare areas nationwide utilize to improve patient care.<\/span><\/p>\n

Because these products are regulated as software-as-a-medical-device (SaMD), vendors must prove safety, effectiveness and, often, a detailed plan for routine updates. The agency\u2019s 2024 cross-center framework further streamlines the review process, encouraging AI innovators while protecting patients.<\/span><\/p>\n

How AI Supports Patient Care<\/b><\/h3>\n

\"AI<\/p>\n

Slashes Scan Times and Dose<\/b><\/h4>\n

AI isn\u2019t just for interpreting images. It\u2019s also remaking how they\u2019re acquired. Deep-learning reconstruction algorithms clarify low-dose CT or limited-echo MRI data so sharply that technologists can cut radiation or magnet time without losing detail. These cuts help make these scans safer for patients and providers.<\/span><\/p>\n

The National Institute of Biomedical Imaging and Bioengineering\u2019s (NIBIB) informatics program funds teams refining reconstruction networks to preserve quantitative accuracy. At the Massachusetts Institute of Technology (MIT), researchers took it a step further, releasing <\/span>FeatUp.<\/span><\/a> This model-agnostic method boosts spatial resolution within any vision network, making it easier to obtain submillimeter detail from standard scanners.<\/span><\/p>\n

Ultrasound also benefits. The University of Wisconsin\u2019s medical physics group pairs AI beamformers with point-of-care probes, bringing cardiology-grade clarity to handheld devices. Faster scans mean shorter breath-holds, happier patients and more appointment slots each day. Patients notice the value even if they\u2019ve never heard of algorithms.<\/span><\/p>\n

Flags Urgent Cases<\/b><\/h4>\n

In busy trauma centers, thousands of cross-sectional images pour in each hour. <\/span>AI triage tools<\/span><\/a> watch in the background, pushing suspected hemorrhages or pulmonary embolisms to the top of a worklist so radiologists read them first. At the <\/span>Radiology Society of North America (RSNA) 2024<\/span><\/a> sessions, one discussion focused on AI workload relief, including measurable drops in turnaround time for critical findings and a tangible decrease in radiologist burnout.<\/span><\/p>\n

However, Harvard Medical School researchers caution that human-algorithm teamwork <\/span>doesn\u2019t work for every radiologist<\/span><\/a>. While some radiologists accept helpful suggestions, others are distracted by them. Its multisite study indicated that training and interface design mattered as much as model accuracy, with integrations tailored for a clinician and AI technology partnership to get the desired result.<\/span><\/p>\n

Turns Raw Pixels into Precise Diagnoses<\/b><\/h4>\n

The FDA cleared the first AI imaging tool <\/span>capable of predicting a woman\u2019s breast cancer risk<\/span><\/a> over the next 5 years using a standard 2D mammogram. Unlike current risk models that rely on a patient\u2019s family history of breast cancer and age, the Clarity Breast platform uses advanced AI to analyze the actual mammogram to look for subtle patterns in the breast tissue that could indicate the development of breast cancer in the future.\u00a0<\/span><\/p>\n

These mammograms may look perfectly normal to the human eye, but AI analysis can provide advanced warning that could make a big difference. Armed with this information, patients can take a more proactive approach to their cancer screenings and follow-up care before actual signs of the disease even appear. By moving beyond detection to prevention, AI can help healthcare professionals save more lives. The Clarity Breast system is anticipated to launch in late 2025.<\/span><\/p>\n

Extracts More Data with Fewer Biopsies<\/b><\/h4>\n

The human eye mostly sees shades of gray within each 3D pixel or voxel in a CT or MRI scan, but AI can measure dozens of properties inside every voxel. These measurements include how bright it is, whether the surface appears rough or smooth, how irregular its shape appears and many other factors. Collectively, the thousands of measurements AI compiles are called radiomic features.\u00a0<\/span><\/p>\n

The <\/span>National Cancer Institute\u2019s (NCI) Quantitative Imaging Network<\/span><\/a> explains that radiomics uses AI to automatically quantify radiographic characteristics of the tumor phenotype, turning pictures into objective data points that clinicians can analyze much like lab values. Why does this matter?<\/span><\/p>\n