Top Artificial Intelligence Trends Influencing the Future of Radiology

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.

Solution development

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 India and advanced capacity solutions in The Philippines. Solution providers in the region are also making inroads into other emerging markets by partnering with local vendors. For instance, lunit (South Korea) Between Indonesia in partnership with INFINITT, while Vuno (South Korea) entered Latin American markets in partnership with Visual Medica (PACS).

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; Gradient Health offers annotated images for training AI solutions, Encord offers DICOM annotation software and Paxera Health has partnered with Penn Medicine to develop its own AI solutions.

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, Health Nuance (now with Microsoft) and Envoy AI (now under the Symphony AI group, rebranded as Eureka) were early adopters of this approach. Over the years, several others have emerged, such as Arterys, IBMFovia.AI, Incepto (Europe), CARPL (Mahajan Imaging, India), Doctor Net (Japan)Vizyon (blockchain and teleradiology) and Wingspan (China). More recently, we have Enlitic (Curie platform) and QMenta, which focuses on neurology.

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 GE (Edison) are leading this transition, although their strategies go beyond radiology.

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 (Mayo Clinic with Vuno for precision oncology R&D), to provide end-to-end solutions (from AstraZeneca partnership with Oxipit.AI).

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 Radnet and Nanox have consolidated the market to some extent through a series of acquisitions.

Interestingly, a few companies have left the space altogether. MaxQ.AI turned to other activities, while IBM sold Watson Health to a private equity firm.

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, lunitwhich has done quite well for the past two years, has filed for an initial public offering (IPO).

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 Frost and Sullivan can help you promote your AI solutions in radiology, contact us.

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