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Due to the nature of their transparency, open-source tools have historically been lower in price compared to their proprietary counterparts [22]. Despite a projected US $6.6 billion investment by developers and investors [23], health care facilities have proven slow to adopt ML technologies [24]. Among other reasons, hesitancy by clinicians and administrators due to potential financial or value-based consequences of using such technology hold back its implementation [24,25].
JMIR Form Res 2022;6(4):e33970
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