Have LLMs improved patient outcomes?
A review indicates that large language models have not yet led to measurable improvements in patient outcomes, despite widespread experimentation in healthcare settings.
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The article suggests that LLMs may rely on crystallized intelligence (learned knowledge) rather than fluid intelligence (genuine reasoning), meaning their apparent reasoning abilities could stem from memorized training data rather than true understanding.
The article argues that LLMs, unlike traditional programming abstractions, do not provide a reliable higher-level abstraction because they lack deterministic, verifiable behavior. While they can generate code or text, the results are probabilistic and require extensive verification, making them a fundamentally different tool rather than a genuine abstraction layer.