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J Korean Med Assoc > Volume 61(12); 2018 > Article
Park, Do, Choi, Sim, Yang, Eo, Woo, Lee, Jung, and Oh: Principles for evaluating the clinical implementation of novel digital healthcare devices


With growing interest in novel digital healthcare devices, such as artificial intelligence (AI) software for medical diagnosis and prediction, and their potential impacts on healthcare, discussions have taken place regarding the regulatory approval, coverage, and clinical implementation of these devices. Despite their potential, ‘digital exceptionalism’ (i.e., skipping the rigorous clinical validation of such digital tools) is creating significant concerns for patients and healthcare stakeholders. This white paper presents the positions of the Korean Society of Radiology, a leader in medical imaging and digital medicine, on the clinical validation, regulatory approval, coverage decisions, and clinical implementation of novel digital healthcare devices, especially AI software for medical diagnosis and prediction, and explains the scientific principles underlying those positions. Mere regulatory approval by the Food and Drug Administration of Korea, the United States, or other countries should be distinguished from coverage decisions and widespread clinical implementation, as regulatory approval only indicates that a digital tool is allowed for use in patients, not that the device is beneficial or recommended for patient care. Coverage or widespread clinical adoption of AI software tools should require a thorough clinical validation of safety, high accuracy proven by robust external validation, documented benefits for patient outcomes, and cost-effectiveness. The Korean Society of Radiology puts patients first when considering novel digital healthcare tools, and as an impartial professional organization that follows scientific principles and evidence, strives to provide correct information to the public, make reasonable policy suggestions, and build collaborative partnerships with industry and government for the good of our patients.

Figure 1.
Hierarchy of artificial intelligence-related terms.
Figure 2.
Brief schematic summary of the processes for evaluating a novel health technology used by the Health Insurance Review and Assessment Service (HIRA) and the National Evidence-based Healthcare Collaborating Agency (NECA).
Table 1.
A checklist for robust clinical validation of the performance of a machine-learning algorithm
Characteristics of the dataset used for clinical validation
Is it representative of the target patients in real-world practice for which the algorithm will be used?
Was it obtained from other institutions than those that provided the data for algorithm development?
Was it derived from multiple institutions?
Was it captured with scanners different from those used to create the data
for algorithm development (e.g., a computed tomography scanner from a different vendor)?a)
Was it obtained using acquisition parameters different from those used to create the data for algorithm development (e.g., a different radiation
dose setting or image reconstruction method for computed tomography)?a)
Was it collected prospectively?

The more of these questions receive a “Yes” answer, the more generalizable the algorithm performance is. a)Applicable to imaging data.


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