Highlights
- 155 BioTech companies that utilize AI in radiology and nuclear medicine
- Comprehensive overview of technologies AI companies utilize
- Mindmaps with companies applying AI in the radiology and nuclear medicine
- Overview of artificial intelligence methods that famous companies use
Report at a Glance
Nuclear Medicine is a field of medicine that uses radioactive substances which are either injected into or ingested by humans to diagnose or treat a disease.
For diagnostics, the patient consumes the radioactive substance, which is then distributed through the human body and concentrated in the disease area. The lesion takes it up, and any residue is naturally eliminated. Then the patient is transferred into the scanner, and radioactivity creates an image. An X-ray picture is given in the first pass, and the second pass gives the PET or SPECT image. The fusion of these images is PET/CT or SPECT/CT images that create a detailed static image of organs, bones, and tissues in high resolution. Such embodiments allow clear identification of the lesion location and make an accurate diagnosis.
There is a common misconception that nuclear medicine and radiology are the same. However, despite these fields being close, and in many cases, radiology complements the nuclear medicine approach, there is a crucial difference between the two. Radiology refers to generating energy that interacts with the body and produces an image using radiology equipment. Radiology professionals then interpret the image to identify abnormalities and diagnose disease. Nuclear medicine refers to the usage of nuclear compounds and radioactive materials and then tracking radiation sources in the body to develop a detailed image or video.
AI Applications in Nuclear Medicine
10 years + $2.6 bln = 1 new drug
Artificial Intelligence has numerous implementations in the field of nuclear medicine. It can be implemented for routine tasks such as planning of examinations, the detection of pathologies and their quantification, and manual research for additional information in medical records and textbooks, which takes too much time for physicians, as well as some special applications such as 'super diagnostics' and precision medicine.
A typical medical imaging workflow can be divided into four steps:
1) planning and optimisation,
2) scanning and reconstruction,
3) interpretation
4) reporting and clinical decision support.
Each step could be improved, accelerated, or completely automated with the help of AI.
Landscape of AI in Nuclear Medicine Companies
Artificial Intelligence for
Nuclear Medicine
Landscape Overview Q1 2023
This report aims to provide a comprehensive overview of the industry landscape regarding the adoption of AI in image processing, clinical research, and other aspects of radiology and nuclear medicine R&D. This overview highlights the trends and insights in the form of informative mind maps and infographics, and benchmarks the performance of key players that form the space and relations within the industry.