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Artificial Intelligence in Radiology: The Computer's Helping Hand Needs Guidance.

A intelligence (AI) is not entirely new to the medical field. We are accustomed to applying tools that have been developed with a varying degree of human and computer input in our clinical practice. Representative examples include handcrafted diagnostic algorithms to triage patients presenting with acute illness (1), statistically derived scores for osteoporotic fracture risk assessment (2), and decision trees for the differentiation of benign and malignant ovarian masses (3). Common to all these tools is that changes to input parameters lead to predictable changes in model output, making them easy to interrogate and understand. More sophisticated, deep learning (DL)–based models employed in decision support systems have been implemented in clinical practice for the automated interpretation of electrocardiograms (4) and detection and classification of lesions on mammography (5). Although DL-based models have exciting potential to solve complex problems, their black box approach faces skepticism despite advances in interpretability and explainability (6). The recent, widespread availability of hardware and software for the development of AI solutions for medicine has inspired an exponential increase in publications. Data-rich medical specialties such as radiology have become a particular focus of rapid development. However, there are ample scope and encouraging initiatives for AI to support the delivery of care from general practice, primary care, the emergency department, and specialist diagnostics to patient self-care (7). This study by Tadavarthi and colleagues (8) has examined the market of AI-enabled image analysis solutions for radiology and provides recommendations for the evaluation of AI tools before purchase. In their market study, the authors illustrate how most solutions are focused on highvolume conditions. Unsurprisingly, many solutions focus on support for lesion detection and quantification rather than decision support for diagnosis and recommendations for management where regulatory stakes and hurdles are higher. Yet only a minority of solutions advertised at the Radiological Society of North America and Society of Imaging Informatics in Medicine annual meetings between November 2016 and June 2019 have received approval for the American or European market. This finding is indicative of a rapidly developing field where, after years of purely scientific development, the first tools start undergoing consolidation, approval, and marketing. The sole focus on solutions advertised at North American conferences risks missing tools by smaller companies with lower marketing budgets and introducing a geographic bias. Indeed, several other solutions have achieved Conformité Européenne marking or U.S. Food and Drug Administration (FDA) approval. Such approval or their equivalent in other territories is a precondition for the implementation of products, but it is by no means sufficient to identify clinically beneficial and financially viable tools. Overall, the adoption of these algorithms into clinical practice is emerging, and further work is needed to transform the scientific enthusiasm for developing advanced AI tools with a broader scope into clinically workable solutions. For tracking the ongoing market, surveys like this study (dating from November 2019) date quickly, leaving a gap for a living review and other market watchers. Tadavarthi et al contribute to a growing number of recommendations for the acquisition and adoption of AI solutions in medicine with a particular focus on radiology (9). They raise important considerations to determine whether an AI tool is a viable solution for an individual service. In addition, one might want to consider the following criteria: Artificial Intelligence in Radiology: The Computer’s Helping Hand Needs Guidance