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Health care companies are racing to incorporate generative AI tools into their product pipelines and IT systems after the technology displayed an ability to perform many tasks faster, cheaper — and sometimes better — than humans.

But the rush to harness the power of so-called large language models, which are trained on vast troves of data, is outpacing efforts to assess their value. AI experts are still trying to understand, and explain, how and why they work better than prior systems, and what blind spots might undermine their usefulness in medicine.

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It remains unclear, for example, how well these models will perform, and what privacy and ethical quandaries will arise, when they’re exposed to new types of data, such as genetic sequences, CT scans, and electronic health records. Even knowing exactly how much data must be fed into a model to achieve peak performance on a given task is still largely guesswork.

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