By Suzan Slijpen, Mauritz Kop & I. Glenn Cohen
1. Introduction: A Fragmented AI in Healthcare Regulatory Landscape
In the past few years, we have witnessed a surge in artificial intelligence-related research and diagnostics in the medical field. It is possible that in some fields of medicine in the future AI tools used in diagnostics will generally perform far better than a human clinician. Prime examples of this can be found in radiology, particularly in the detection -and even the prediction- of malignant tumors.
Although the actual development of a clinically usable, deployable deep-learning algorithm is a challenge in and of itself, we have moved from an early period where there was not enough guidance as to ethical and other issues to an era where many guidelines have proliferated. While one might ordinarily say “let a thousand flowers bloom,” the fact that they partially overlap, sometimes diverge, and are often written at different levels of generality make it difficult for well-meaning companies to keep up. This is specifically the case for innovative firms who aim to bring their product into the European market.
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