Top companies in AI-powered medical imaging in 2025

Around 75% of all medical malpractice claims against radiologists are related to diagnostic errors.¹ Many radiology errors can be traced back to breakdowns in communication during the imaging or reporting process.²

AI algorithms can be trained to analyze medical images and identify patterns and abnormalities that may be missed by human eyes. This not only saves time, but it also helps to improve the accuracy of diagnoses and treatment plans.

Explore how AI can be used in medical imaging and top medical imaging companies:

Leading medical imaging companies

AIMultiple prepared a sortable list of all AI-powered medical imaging companies. We focused on startups and left out market leaders even though they are also integrating AI technologies into their existing product lines.

Butterfly Network

Butterfly aims to bring a different perspective on medical imaging with both hardware and software solutions. Butterfly IQ is a portable mobile device that uses ultrasound-on-chip technology, which makes it the world’s first handheld entire-body ultrasound framework.

The device also claims to have the capability of detecting diseases in real-time while scanning. Dr. Jonathan Rothberg, chairman of Butterfly Network, is a recipient of National Medal of Technology and Innovation from the White House. Recently, Butterfly iQ3 announced FDA clearance.³

Arterys

The company built the first tech product to visualize & quantify blood flow in the body using any MRI. Arterys also received the first FDA approval for clinical cloud-based deep learning in healthcare.

Furthermore, Arterys, a pioneer in four-dimensional (4D) cloud-based imaging, has been awarded “Best New Radiology Vendor” and “Best New Software” in the 2016 Minnies Awards. Arterys has ranked as one of the World’s 50 Most Innovative Companies⁴ by FastCompany in 2019. Arterys’ Lung-AI platform helps to reduce missed detections by 42 to 70%.⁵

Gauss Surgical Inc.

Gauss Surgical, part of Stryker received CE (Conformité Européenne) Mark for its Triton System for iPad, the world’s first and only mobile platform for real-time monitoring of surgical blood loss.

Sigtuple

The company‘s innovative solutions aim to solve the problems caused by the chronic shortage of trained medical practitioners in India.

Freenome

The medical imaging device company raised 70.6M within only two years of its launch. Freenome works on detecting cancer by imaging blood cells.

Enlitic

The firm uses deep learning techniques to analyze the data extracted from radiology images. A study suggests that radiologists can read cases 21% faster with the help of Enlitic. 

Caption Health

The medical imaging company provides guidance to healthcare professionals and inexperienced people to perform ultrasound examinations accurately and quickly. it also facilitates the work of healthcare professionals by providing automatic quality assessment and smart interpretation.

Behold.ai

Behold uses artificial intelligence technologies to help radiologists diagnose disease with radiology scans in a variety of cases. Behold.ai reduces the workload of medical professionals by fastening the process of diagnosis. 

Viz.ai

The company raised $50 M in late 2019 for detecting early signs of brain stroke.⁶ February 2020, Viz.ai released a new generation synchronized care platform for those who are in the post-acute care period. The platform sends a notification to healthcare professionals when there is a sign of a serious situation.

RetinAI

Retin AI‘s “Discovery Platform”  helps to collect, organize, and analyze health data from the eye in order to detect age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, etc. 

Subtle Medical

Subtle Medicals’ software improves the quality of noisy medical images and provides better interpretation. It is especially helpful for patients who have difficulty holding still for long periods of time.

BrainMiner

It is a UK-based company, and Brainminer’s software DIADEM provides an automated system for analyzing MR brain scans to help clinicians with an easily interpreted report. 

Lunit

Lunit has developed AI solutions for precision diagnostics and therapeutics. The company aims to optimize diagnosis and treatment matches by searching for the right diagnosis at the right cost, and the right medical treatment for the right patients.

In collaboration with Lunit and GE Healthcare launched an AI-powered chest X-ray imaging package designed to detect and highlight eight common conditions, such as tuberculosis and pneumonia, including those linked to COVID-19, using their algorithms.

3 main medical imaging technologies

Medical imaging helps healthcare providers see inside the human body to diagnose disease, monitor conditions, and guide medical treatment. Different imaging techniques provide unique insights into internal structures and functions. Three common imaging modalities are ultrasound, magnetic resonance imaging (MRI), and X-ray imaging.⁷

Ultrasound Imaging

Ultrasound imaging uses sound waves to generate images of internal organs, blood flow, and developing fetuses. A medical imaging device called a transducer sends high-frequency sound waves into the patient’s body. These waves bounce back, and a computer processes them into images. Ultrasound is widely used in obstetrics, cardiology, and soft tissue evaluations.

Advantages:

• No radiation exposure, making it safe for pregnancy.
• Provides real-time imaging for guiding invasive procedures.
• Portable imaging equipment allows use in clinical sites and emergency settings.

Limitations:

• Image quality depends on body composition and operator skill.
• Cannot effectively image bones or air-filled organs like the lungs.

Magnetic Resonance Imaging (MRI)

MRI uses strong magnetic fields and radio waves to create high-resolution images of soft tissues, organs, and the brain. Unlike X-ray imaging, MRI does not use ionizing radiation. A patient lies inside an MRI scanner while radio waves interact with hydrogen atoms in the body. This produces signals that are processed into detailed cross-sectional images.

Uses:

• Ideal for brain, spinal cord, and joint imaging.
• Helps detect tumors, nerve damage, and internal bleeding.
• Enhances medical image analysis with contrast agents for better visualization.

Considerations:

• Can be time-consuming (20–90 minutes per scan).
• Not suitable for patients with metal implants or severe claustrophobia.
• Requires strict quality assurance programs for accurate diagnosis.

X-ray Imaging and Computed Tomography (CT)

X-ray imaging is one of the oldest and most common imaging techniques. A controlled X-ray beam passes through the body, creating images of bones and certain internal structures. Medical X-ray imaging is widely used for diagnosing fractures, infections, and lung diseases.

CT scans, or computed tomography, take multiple X-ray images from different angles to generate cross-sectional images of the body. A CT scanner provides more detailed images than conventional X-rays, making it valuable for diagnosing complex conditions.

Benefits:

• Quick and effective for emergency diagnostics.
• CT scans offer detailed 3D images for precise clinical analysis.
• Used in radiation therapy planning and molecular imaging.

Risks:

• Ionizing radiation exposure, increasing the risk of developing cancer.
• Requires radiation protection measures for both patients and radiologic technologists.
• Contrast agents used in CT may cause allergic reactions in some cases.

How is AI used in medical imaging?

The aim of medical imaging is to capture abnormalities using image processing and machine learning techniques. Application areas can be divided into sub-branches such as the diagnosis of various diseases and medical operation planning. The top applications of AI-powered medical imaging are:

1. Revealing cardiovascular abnormalities

According to an article published by Frontiers in Cardiovascular Medicine Journal in 2019, the integration of AI into cardiac imaging will accelerate the process of the image analysis which is a repetitive task that can be automated, therefore healthcare professionals engaged in this work can focus on more important tasks.⁸

2. Prediction of Alzheimer’s disease

The Radiologic Society of America suggests that advances in AI can lead to predicting Alzheimer’s disease years before it occurs by the identification of metabolic brain changes.⁹

3. Cancer detection

In early 2020, the Google health team announced that they developed an AI-based imaging system that outperformed medical professionals in detecting breast cancer.¹⁰

4. Treatment revaluation

This is mostly used for cancer patients undergoing treatment to check if the treatment is working effectively and diminishing the size of the tumor.

5. Surgical Planning

Medical imaging also allows for the segmentation of the image related to the surgical area so that the algorithm can do the planning for healthcare professionals automatically. Surgical planning with the help of medical imaging can saves time in surgeries.

Check our comprehensive article on the use of AI in radiology.

How was AI-powered medical imaging technologies used during the COVID-19 outbreak?

Overall, the impact of AI solutions on the COVID outbreak was limited, but there are some examples where technology was demonstrated in a specific group of patients.(https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/)

Medical imaging is one of the AI-powered solutions that was frequently mentioned during the COVID-19 pandemic. Due to the rapid increase in the number of patients, the clinical practice and interpretation of patients’ chest scan results became a problem.

For example, a Chinese company, Huiying Medical claimed to have developed an AI-powered imaging diagnostic solution to detect the virus in the early stage with 96% accuracy.¹¹ solution to detect the virus in the early stage with 96% accuracy. 

Source: Intel¹²

Pneumonia is a serious complication of COVID-19 and results in patients requiring ventilator support. In a collaborative research by the University of California San Diego health department and AWS, a model was built to analyze chest images of patients at risk of pneumonia.¹³

The model was trained to identify patients infected with Covid-19 by using AI-powered medical imaging procedure. The algorithm was trained on 22,000 notations by human radiologists. The algorithm performs color-coded maps that indicate the probability of pneumonia.

External Links

• 1. Diagnostic Errors in Radiology - Healthcare AI | Aidoc Always-on AI. Healthcare AI | Aidoc Always-on AI
• 2. Brady A, Laoide RÓ, McCarthy P, McDermott R. Discrepancy and error in radiology: concepts, causes and consequences. Ulster Med J. 2012 Jan;81(1):3-9. PMID: 23536732; PMCID: PMC3609674.
• 3. Butterfly Announces FDA Clearance of its Next-Gen Handheld Ultrasound System, Butterfly iQ3.
• 4. fastcompany.com.
• 5. https://arterys.com/lung-ai/
• 6. Viz.ai raises $50 million for AI that detects early signs of stroke | VentureBeat. VentureBeat
• 7. Medical Imaging | FDA. Center for Devices and Radiological Health
• 8. Petersen SE, Abdulkareem M, Leiner T. Artificial Intelligence Will Transform Cardiac Imaging-Opportunities and Challenges. Front Cardiovasc Med. 2019 Sep 10;6:133. doi: 10.3389/fcvm.2019.00133. PMID: 31552275; PMCID: PMC6746883.
• 9. Artificial Intelligence Predicts Alzheimers Years Before Diagnosis | RSNA.
• 10. Google AI Beats Doctors at Breast Cancer Detection—Sometimes - WSJ. The Wall Street Journal
• 11. Huiying Medical claims its AI can detect coronavirus from CT scans with 96% accuracy | VentureBeat. VentureBeat
• 12. Huiying Medical: Helping Combat COVID-19 with AI Technology - Intel Community .
• 13. Artificial Intelligence Enables Rapid COVID-19 Lung Imaging Analysis at UC San Diego Health.