Find out how artificial intelligence will impact the field of radiology, creating new opportunities for growth.
Top Artificial Intelligence Trends Influencing the Future of Radiology
Given the pace of technological advancements, artificial intelligence (AI) in radiology has moved beyond the nascent stage to maturity. Commercial adoption is on the rise, as the industry continues to see an increasing number of startups emerging to meet the needs of AI in radiology. However, the market is fundamentally different today than it was before the pandemic. Here are the key trends in AI in radiology that all market players should consider.
Complete end-to-end value-added solutions
Gone are the days of developing a simple reading room solution that helps radiologists identify areas of interest. Solutions that support the full continuum of care for current illnesses are more likely to take hold. These solutions will suggest the best imaging tests to perform based on the symptoms and the optimal scan settings to obtain the necessary images. They will guide clinicians in making appropriate diagnostic and treatment decisions.
RapidAI – Beyond stroke detection, this workflow solution facilitates in-app communication with stroke team members to build a response team at any time, reducing the time between detection and intervention.
VIDA – In pulmonology, VIDA Insights (formerly LungPrint) aids in early disease detection, optimizes time to interpret complex conditions such as COPD and MID, and aids in making the right treatment decisions.
Multiple anomaly detection solutions
Healthcare providers increasingly prefer solutions that can detect multiple abnormalities in a single scan, which can reduce image reading time and minimize human error. These solutions could be useful in emergency trauma cases, where a chance finding could help save a life. Although this is a significantly weaker trend, these solutions are perceived to provide a better return on investment. An example is Annalize.AI, which can detect more than 120 abnormalities in a chest X-ray.
Risk-Based Screening Stratification Solutions
Artificial intelligence solutions designed to help reduce exam volume by “eliminating” normal patients and flagging abnormal patients for review by the radiologist are becoming increasingly popular. Recent evidence presented at radiology conferences shows how these solutions can significantly reduce radiologist workload, especially with mammography screening (in developed countries) and tuberculosis screening (in developing countries) , which typically see large volumes of reviews. These solutions also help prevent, or at least reduce, unnecessary diagnostic tests such as biopsies and associated costs.
Request for AI solutions in radiology
APAC prepares for AI in radiology
There is a marked increase in the adoption of AI in radiology among emerging markets, especially in the APAC region. Healthcare providers purchase solutions from local vendors, such as Synapsica.AI and Rises.AI in
Self-developed AI solutions remain in demand
Despite numerous AI solution providers, the demand for hospitals to develop their own AI solutions persists. Some providers also help in this regard;
Platforms and marketplaces grow but face lukewarm response
Since 2017, several companies have created an AI marketplace offering a range of AI services from various vendors. Blackford analysis,
However, this approach still faces barriers to adoption, leading key players to shift to a platform model, which offers end users a wealth of value-added solutions to help manage administrative processes. and operational as well as their imaging workflows. These are in a better position to demonstrate higher value and return on investment, but only time will tell if this approach influences radiology departments and hospital CFOs in its favour. Philips (Health Suite) and
Interesting supplier changes and dynamics
New partnerships
Over the past five years, the evolution of the complexity of AI in radiology and its business ecosystem has shown that no solution provider can succeed alone. Emerging use cases and intense competition will force unprecedented partnerships among AI solution providers in their quest to capture market share. Partnerships can range from basic integrations (ScImage and DiA Imaging Analysis for viewer integration), to distribution (Fujifilm X-ray with Annalise.AI) and R&D (
Entry of new players somewhat balanced by consolidation
The competitive landscape is currently experiencing some interesting developments. For one, a host of new vendors like Artyra, Mireye, and Vinbrain have entered the fray, especially in emerging markets. On the other hand, some key players like
Interestingly, a few companies have left the space altogether. MaxQ.AI turned to other activities, while
Barriers to Growth: Refunds, Funding, and IPOs
The broader barrier to adoption of the reimbursement model has not been addressed, even in developed markets. The market hasn’t seen any major developments beyond “NTAP” payments for Stroke AI, which has caused some flutter.
The sector is also experiencing an uneven funding pattern and a significant drop in the number of closed deals, potentially fueled by the global economic slowdown. The pre-pandemic period saw a fair distribution of funding across all types and stages of AI startups. Now, only a few big players like Viz.AI and Aidoc land lucrative deals, while smaller vendors receive much lower amounts. However,
Conclusion
Massive advancements in computer technology have helped artificial intelligence make serious inroads in the field of radiology. These developments are noteworthy for radiology players and the broader healthcare space in general, as AI continues to redefine the delivery of care with more and more companies constantly pushing the boundaries. of what is possible. We believe the next three years will see some drastic changes in this space, and we will be watching closely.
To learn more about the latest trends in this market and find out how
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