The healthcare industry is experiencing a major shift thanks to artificial intelligence (AI). Medical imaging, which involves creating visual representations of the body's interior for diagnosis and treatment, is at the center of this change. AI is transforming medical imaging in significant ways:
- Improving accuracy
- Saving time
- Personalizing care
- Reducing mistakes
- Predicting outcomes
These AI advancements are making healthcare safer and more effective. They're helping doctors provide better care while reducing the need for invasive procedures. As AI continues to develop, it's set to revolutionize how we detect and treat diseases, ultimately improving patient care across the healthcare system.
What Is The Need For AI In Medical Technologies?
AI is quickly transforming medical imaging across various techniques, including X-rays, ultrasounds, CT scans, MRIs, mammograms, and angiograms. This technology is streamlining clinical work by automating complex and repetitive tasks. Healthcare facilities are increasingly adopting AI-powered imaging solutions to diagnose diseases more quickly and accurately.
The benefits of AI in medical imaging are significant. It can automatically position equipment, speed up workflows, control radiation doses, improve image consistency, and reduce infection risks through automation. These advantages are making medical procedures safer and more efficient.
The market for AI-enabled medical imaging is experiencing rapid growth. According to research, the global AI in medical imaging market size was estimated at USD 1.01b in 2023 and is expected to grow at a CAGR of 34.8% from 2024 to 2030. This impressive expansion is fueled by several factors: the rising number of diagnostic imaging procedures, the increasing complexity of clinical decision-making, and the growing adoption of AI solutions to enhance accuracy and efficiency in clinical workflows.
This trend highlights the growing importance of AI in healthcare, particularly in medical imaging. It's helping healthcare professionals work more efficiently and make more informed decisions, ultimately leading to better patient care. As AI continues to evolve, it's likely to play an even more crucial role in shaping the future of medical imaging and healthcare as a whole.
TOP 10 AI Imaging Companies In 2024
1 Holdings Corporation
Fujifilm Holdings Corporation is a major player in the medical imaging industry with an annual revenue of USD 35 Billion. The company specializes in photography, optics, medical electronics, and biotechnology. Fujifilm's healthcare division offers advanced medical imaging and diagnostic equipment, pharmaceuticals, regenerative medicine, and biologics manufacturing. Their AI platforms enhance diagnostic imaging workflows using deep learning and advanced image processing. In 2021, Fujifilm launched new X-ray solutions integrated with Qure.ai's qXR computed-aided radiology software.
2 GE Healthcare
GE Healthcare is a leading manufacturer of diagnostic imaging solutions and radiopharmaceuticals, covering X-ray, ultrasound, CT scan, and MRI modalities. GE Healthcare also provides medical diagnostics, patient monitoring, and services in biopharmaceutical manufacturing and disease research. Their AI-enabled imaging solutions aim to improve diagnostic accuracy and clinical decision support for radiologists. With operations in over 100 countries, GE Healthcare has been a key player in the medical imaging field since its incorporation in 1994.
3 Aidoc
Founded in 2016, Aidoc is a rapidly growing AI solutions provider in the medical imaging sector, with an annual revenue of $5 Million. The company is known for its healthcare-grade AI-based decision support software that helps radiologists enhance patient treatment and improve care quality in real-time. Aidoc's AI platform offers a seamless cross-specialty experience for physicians, prioritizing urgent cases and reducing report turnaround times. Their solutions are currently used in over 500 medical centers worldwide, including major healthcare providers like Radiology Partners.
4 Butterfly Network
Butterfly seeks to provide a new viewpoint on medical imaging through hardware and software solutions. Butterfly IQ is a portable mobile gadget that leverages ultrasound-on-chip technology, making it the World's first handheld whole-body ultrasound framework. The technology also promises to be capable of detecting diseases in real time while scanning.
5 Arterys
Arterys has pioneered several innovations in medical imaging and AI. They created the first technology to visualize and quantify blood flow in the body using any MRI and received the first FDA approval for clinical cloud-based deep learning in healthcare. As a leader in 4D cloud-based imaging, Arterys won "Best New Radiology Vendor" and "Best New Software" at the 2016 Minnies Awards. FastCompany recognized Arterys as one of the World's 50 Most Innovative Companies in 2019. Their Lung-AI platform significantly reduces missed detections by 42 to 70%.
6 Lunit
Lunit focuses on AI solutions for precision diagnostics and therapeutics, aiming to optimize diagnosis and treatment matching. They collaborate with major healthcare companies like GE Healthcare to develop AI-powered diagnostic tools. One notable product is a chest X-ray analysis package that can detect eight common conditions, including those related to COVID-19.
7 Enlitic
Enlitic specializes in AI-powered diagnostic imaging solutions. Their Enlitic Curie™ platform integrates seamlessly into various stages of medical imaging workflows, automating data standardization. Enlitic also provides high-quality software-as-a-medical device products to help radiologists meet regulatory and quality requirements in patient care.
8 ContextVision AB
ContextVision is known for its AI-based image-enhancing solutions. They partner with global manufacturers of X-ray, ultrasound, and MRI equipment. ContextVision's AI-based software products are designed to help clinicians interpret medical images quickly and accurately, improving disease diagnosis and treatment. Founded in 1983, the company is listed on the Oslo Stock Exchange and continues to be a leader in medical image enhancement technology.
9 Siemens Healthineers
Siemens Healthineers is a leading player in AI-enabled medical devices, with 40 FDA-approved devices and a portfolio of over 80 AI-based solutions. Their AI-Rad Companion is a key product that uses AI algorithms to automate post-processing of imaging data. This tool improves radiologists' workflow and handling of high case volumes by automating routine tasks. It includes modules for various imaging modalities and body regions, such as chest CT for lung nodule detection and tumor burden estimation, brain MR for segmentation and volumetric measurements, prostate MR for volume estimation and PSA density calculation, and CT organ outlining, including lymph nodes. Siemens continues to expand the AI-Rad Companion with new modules for different imaging applications.
10 Philips
Philips has significantly expanded its AI capabilities in medical imaging through strategic acquisitions and development. The company acquired DiA Imaging for nearly $100 million, adding nine FDA-approved AI ultrasound products to its portfolio. Philips now offers AI software for various imaging modalities, including CT, MRI, PET, and ultrasound. These tools assist in scan planning, patient positioning, image enhancement, and result analysis. Philips also provides cloud-based systems to connect doctors with third-party AI diagnostic applications. Beyond imaging, their AI solutions extend to patient management, cognitive assessment, and sleep scoring. Philips is even integrating AI into consumer products for personal care, demonstrating the company's broad application of AI technology across its product lines.
Conclusion
AI has the potential to improve radiology workflows in a variety of ways. AI-assisted radiology can help with surgery and exam planning, image reconstruction, image analysis, and clinical decision-making. AI-assisted radiology is projected to provide radiologists with new and innovative tools. Such AI applications are expected to be pervasive and almost undetectable to physicians, slipping into the background of clinical decision-making processes.
