Abstract
ObjectivesMap the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence.MethodsWe created an online overview of CE-marked AI software products for clinical radiology based on vendor-supplied product specifications (www.aiforradiology.com). Characteristics such as modality, subspeciality, main task, regulatory information, deployment, and pricing model were retrieved. We conducted an extensive literature search on the available scientific evidence of these products. Articles were classified according to a hierarchical model of efficacy.ResultsThe overview included 100 CE-marked AI products from 54 different vendors. For 64/100 products, there was no peer-reviewed evidence of its efficacy. We observed a large heterogeneity in deployment methods, pricing models, and regulatory classes. The evidence of the remaining 36/100 products comprised 237 papers that predominantly (65%) focused on diagnostic accuracy (efficacy level 2). From the 100 products, 18 had evidence that regarded level 3 or higher, validating the (potential) impact on diagnostic thinking, patient outcome, or costs. Half of the available evidence (116/237) were independent and not (co-)funded or (co-)authored by the vendor.ConclusionsEven though the commercial supply of AI software in radiology already holds 100 CE-marked products, we conclude that the sector is still in its infancy. For 64/100 products, peer-reviewed evidence on its efficacy is lacking. Only 18/100 AI products have demonstrated (potential) clinical impact.Key Points• Artificial intelligence in radiology is still in its infancy even though already 100 CE-marked AI products are commercially available.• Only 36 out of 100 products have peer-reviewed evidence of which most studies demonstrate lower levels of efficacy.• There is a wide variety in deployment strategies, pricing models, and CE marking class of AI products for radiology.
Highlights
There is a wide variety in deployment strategies, pricing models, and CE marking class of artificial intelligence (AI) products for radiology
The number of artificial intelligence (AI) products for radiology has rapidly expanded over the past years
We focus on commercial products as these are the products that are currently available for clinical use
Summary
The number of artificial intelligence (AI) products for radiology has rapidly expanded over the past years. Even though the supply of AI software for radiology is vast and many radiologists seem willing to adopt and adapt AI tools, clinical implementation remains limited [3,4,5]. It has been shown that the extent in which research algorithms have been validated varies widely [6,7,8]. 6% of the recent papers on medical deep learning algorithms included a validation on independent external data [6]. The available evidence for commercially available AI software has not been studied yet. We focus on commercial products as these are the products that are currently available for clinical use
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