The use of artificial intelligence (AI) in medical imaging has seen a significant increase in recent years, according to a report published by Klas Research. The report reveals that more than half of healthcare organizations have started to incorporate AI algorithms into at least one imaging use case, a substantial increase from just 17% in 2018.
With the healthcare industry facing challenges such as clinician shortages and burnout, AI has emerged as a promising solution to alleviate providers’ heavy workloads. The technology has been integrated into a growing number of medical devices, with the FDA authorizing 221 AI-backed medical devices last year alone, compared to just six in 2015. The majority of these AI-enabled devices are used in radiology, for tasks like identifying potential health conditions or planning radiation therapy.
Klas’ survey, which included input from over 200 health systems and imaging groups, indicates a surge in the adoption of AI imaging tools as more products receive FDA approval. Companies with large imaging volumes are more likely to have live AI products, but a significant number of midsize and small organizations are planning to adopt the technology in the near future.
The survey identified 65 different commercial AI products being used, including offerings from companies like RapidAI, Viz.ai, and Aidoc, as well as custom-built solutions. The most common use cases for AI in imaging include neurology and stroke imaging, lesion detection in mammograms, lung nodule detection, and automated report generation for radiologists.
Despite the increasing adoption of AI imaging tools, most organizations are only using the technology for a few specific use cases. Nearly three-quarters of respondents reported having one or two use cases, with less than 10% utilizing five or more. This suggests that while AI has great potential in medical imaging, its full capabilities are not yet being fully realized across the industry.
In conclusion, the use of AI in medical imaging is on the rise, with healthcare organizations increasingly turning to these tools to improve efficiency and patient care. As technology continues to advance and more AI products become available, the integration of AI into medical imaging workflows is likely to become even more widespread in the coming years.
