The potential of AI for workflow optimization is also being explored in use cases, but this has not been put into clinical practice.
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This is one of the first AI applications in radiology where the software functions autonomously and has taken over the task of the radiologist. This can be used as an autonomous pre-screening tool to reduce the use of microbiological tests, which are more time-consuming and costly (levels 2, 3, 6). AI-supported tuberculosis detection is especially useful in developing countries where staffing, expertise and financial resources are often limited. A use case that is already widespread is AI software for tuberculosis detection on chest radiographs. The workflow might also be optimized by changing the diagnostic process with AI. In spite of this, the availability and implementation of such software are limited, leaving room for growth of this industry. The applications, therefore, involve lower risk and have fewer rules and regulations to comply to before they can be implemented in clinical practice. The impact of most of these solutions is not necessarily aimed at the detection or diagnosis of the patient rather, these solutions address boundary conditions like patient management. These patients received a phone call reminder, decreasing the no-show rate from 19.3% to 15.9%. trained a model to predict which patients had the highest risk of missing their appointment. For example, even before a patient enters the radiology department, AI software might aid the scheduling of imaging appointments and predict no-shows for nudging or more efficient scheduling. AI could contribute to this in clinical, but also non-clinical, ways. With ever increasing health care costs worldwide, effective use of the limited resources is an important endeavor. We define these as the ultimate goals of AI in radiology, which can be supported by a variety of subgoals described as (1) making the workflow more efficient, (2) shortening the reading time, (3) reducing dose and contrast agents, (4) earlier detection of disease, (5) improved diagnostic accuracy and (6) more personalized diagnostics (Fig. Another recently published study systematically reviewed evidence on the economic impact of AI in health care (level 6) and found only six eligible articles, demonstrating the limited evidence of the impact of AI from a more global perspective.Ĭonsidering the framework of value-based health care and the corresponding value equation, value=outcome/cost, AI can create value when either reducing the costs or improving the health outcome. Most studies demonstrated the accuracy of the algorithm (level 2), but (prospective) research showing the benefits in clinical practice (level 3 and up) was limited and covered only 18 of the 100 products evaluated. Ultimately, level 6 evidence describes the impact of AI on a macro level, demonstrating the effects on costs and health.
Evaluations regarding higher levels (levels 3–5) describe the impact on the diagnosis, therapy and outcome of the patient. The lower levels describe the functioning and performance of the product (levels 1, 2). In a previous study this model was adapted (Table 1 ) to be applicable to assess evidence on AI. The scientific evidence on the efficacy can be classified according to the hierarchical model developed by Fryback and Thornbury back in 1991 to evaluate the contribution of diagnostic imaging to the patient management process. A study performed in 2020 showed that only 36 of 100 AI products analyzed had peer-reviewed evidence available on their efficacy.
Although the supply is large, the scientific evidence on the validation and impact of these products remains limited. These products have been cleared by the Food and Drug Administration (FDA) or are European Conformity (CE) marked to allow clinical use in the United States and Europe, respectively. More than 150 AI products for radiology are on the market. With health care expenses continuously rising through an increasingly older population and evolving technology, we should dare to be critical about what medical devices, including AI-based software, are actually improving health care or making it more efficient. The Da Vinci robot is a well-known example of innovative technology that became very popular very fast, but even today the cost-effectiveness and claim of improved patient outcomes are being debated.
To create a positive impact on health care with this technology, the clinical goal should be clearly defined. However, AI is a means, a tool, not the goal in itself. Artificial intelligence (AI) has the potential to change many aspects of health care.