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Expectations for AI in Healthcare Become More Modest

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In 2019, investors reportedly poured $4 billion into more than 90 healthcare artificial intelligence (AI) startups specializing in various technologies—from drug discovery to diagnostics—that may empower individual clinicians as well as entire healthcare systems. Although the technologies are promising, the enthusiasm they arouse should be tempered. In healthcare applications of AI, serious data challenges remain, and predictive modeling still exhibits shortcomings.

We are all familiar with the many promises of AI. For example, there are multiple claims that radiologists and dermatologists will soon be replaced by computers powered by AI to better discern the differences between malignant and benign masses and lesions. There are also projections that the technology will, in the near future, ensure that treatments are more precise, that patients are given access to customized engagement tools, and that operational challenges in healthcare are overcome.

These projections, however, may be overly optimistic. Why? First, it’s important to underscore that AI refers to computer algorithms that perform analyses on datasets to generate insights and predictions, which require well-curated and clean datasets. But despite the use of electronic medical records systems, healthcare data is far from “clean.” There are many different types of data that come in a variety of formats—making […]

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