Even the most advanced AI can give you misleading results if the data behind it is unreliable.
Artificial intelligence is becoming increasingly important in healthcare.
But there's a fundamental problem that often gets overlooked.
AI systems depend heavily on the quality of the information they receive.
Marco Benitez (Digital Health Expert) explains why cleaning, organizing, and standardizing health data is essential before healthcare companies can use it effectively.
Medical records, laboratory results, and wearable devices often produce information in different formats.
One system might record data differently from another.
Some information may be duplicated, incomplete, or inconsistent.
And when unreliable information enters an AI system, the results can become unreliable too.
That's why healthcare data integration matters.
Better AI models alone won't solve every problem.
The quality of the data behind them is just as important.
Are we focusing too much on building smarter AI and not enough on improving the information it uses?
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