Spending for machine learning in radiology expected to soar
The market for machine learning in medical imaging will continue to grow, reaching about $2 billion in annual expenditures worldwide by 2023.
The market for machine learning in medical imaging will continue to grow, reaching about $2 billion in annual expenditures worldwide by 2023.
That’s the estimate of Signify Research, a research organization based in the United Kingdom.
Signify Research sees particularly rapid expansion of the market for algorithms using deep learning to support software used in image analysis. It estimates the market at less than $200 million this year, but growing to nearly $1.4 billion by 2023.
The consultancy defines machine learning applications as encompassing software for automated detection, quantification, decision support and diagnosis.
The use of such technology has emerged from early inflated expectations, with radiology leaders now expecting that its use will transform the diagnostic imaging industry, “both in terms of enhanced productivity, increased diagnostic accuracy, more personalized treatment planning and ultimately improved clinical outcomes,” the firm’s research report suggests.
The introduction of deep learning technology and affordable cloud compute and storage is accelerating the pace of product development for AI-based medical image analysis tools, says Simon Harris, a principal of the firm.
AI tools, he says, are gradually becoming more accurate and sophisticated, now capable of supporting added functionality, Harris adds.
Radiologists’ perceptions of AI have changed, too. They no longer view it as a threat to the profession, but in the last 12 to 18 months are looking at the possibilities for using it to augment radiologists.
“At the same time, there are emerging clinical applications where the use of AI has been shown to both improve clinical outcomes and deliver a return on investment for healthcare providers,” Harris adds. “Examples include software to detect and diagnose stroke and analysis tools to measure blood flow in non-invasive coronary exams.”
Barriers to growth in the use of AI in radiology include:
That’s the estimate of Signify Research, a research organization based in the United Kingdom.
Signify Research sees particularly rapid expansion of the market for algorithms using deep learning to support software used in image analysis. It estimates the market at less than $200 million this year, but growing to nearly $1.4 billion by 2023.
The consultancy defines machine learning applications as encompassing software for automated detection, quantification, decision support and diagnosis.
The use of such technology has emerged from early inflated expectations, with radiology leaders now expecting that its use will transform the diagnostic imaging industry, “both in terms of enhanced productivity, increased diagnostic accuracy, more personalized treatment planning and ultimately improved clinical outcomes,” the firm’s research report suggests.The introduction of deep learning technology and affordable cloud compute and storage is accelerating the pace of product development for AI-based medical image analysis tools, says Simon Harris, a principal of the firm.
AI tools, he says, are gradually becoming more accurate and sophisticated, now capable of supporting added functionality, Harris adds.
Radiologists’ perceptions of AI have changed, too. They no longer view it as a threat to the profession, but in the last 12 to 18 months are looking at the possibilities for using it to augment radiologists.
“At the same time, there are emerging clinical applications where the use of AI has been shown to both improve clinical outcomes and deliver a return on investment for healthcare providers,” Harris adds. “Examples include software to detect and diagnose stroke and analysis tools to measure blood flow in non-invasive coronary exams.”
Barriers to growth in the use of AI in radiology include:
- The regulatory process, with few approved and fully commercialized products on the market.
- A shortage of large-scale validation studies.
- Lack of integration of AI-based image analysis tools into radiologists’ workflows.
- Reluctance by providers to purchase tools from multiple software developers because of implementation and integration challenges.
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