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The World Health Organization issued its first global report on artificial intelligence in late June, highlighting concerns of algorithmic bias in health care applications of AI. It accompanies a growing number of news stories exposing AI’s shortfalls.

AI has come of age through the alchemy of cheap parallel (cloud) computing combined with the availability of big data and better algorithms. Problems that seemed unconquerable a few years ago are being solved, at times with startling gains — think instant language translation capabilities, self-driving cars, and human-like robots. AI’s arrival to health care, however, has been markedly slower. Perhaps the industry’s resistance to fundamental change is to blame, or its sluggish digital transformation. But the reason may be simpler: The stakes are much higher.


The complexity and criticality of health care’s issues far outweigh those of other applications. Nobody dies if an iPhone’s ad fails to inspire someone’s inner consumer. In fact, the arrival of AI may be less about health care being “late” and more about it finally being ready for health care.

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