AI in Medical Imaging to Surge in Coming Years

Radiologists plan to apply machine learning to many other areas of medical imaging, and its use in cardiovascular imaging, pulmonary hypertension imaging and neural aneurysm imaging is likely to witness a steep and rapid adoption in the near future.

The survey found that only 16% of medical imaging professionals had no plans to adopt machine learning, whereas the majority of respondents viewed the technology as being either important or extremely important in medical imaging. Most radiology departments and imaging centers plan to begin using machine learning jump before 2020, while the remaining organizations are expected to follow a few years later. Interestingly, the survey found that there has been very little adoption of machine learning by imaging centers and all of the adopters are hospitals.

The research concluded that machine learning in medical imaging was not hype and the huge investments being made in the field were justified. However, given the cost pressures in radiology and in other areas of medical imaging, it still remains to be seen how AI solution vendors would profit from selling their AI and justify the additional expenses over the long-term. The current scenario also raises certain questions such as whether AI solutions would end up replacing, or radically altering, current imaging solutions like PACS or just be an add on.

The research found the rapid level of adoption and ability of AI to aid clinicians in their critical jobs to be encouraging, but notes that AI is unlikely to replace people and will only act as another valuable tool to help clinicians perform better, ultimately leading to improvement in patient care and control over costs in the long-term.

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