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Accelerating Healthcare With AI: Improving Diagnostic Accuracy
AI algorithms are proven to outperform traditional diagnostics. The average human diagnostic error rate is estimated to be 11.1% in the U.S., and as many as 98,000 deaths each year are attributed to medical mistakes under traditional diagnostics.Comparatively, an AI algorithm diagnosed cancer risk from mammograms 30 times faster than a human physician with 99% accuracy. As can be seen, integrating AI into clinical decision making can enhance diagnostic accuracy, reduce errors and optimize treatment planning.The rapid pace and volume of medical advancements make it challenging for providers to stay updated. AI can help support physicians with the latest in medical research. AI algorithms are able to swiftly process vast amounts of information and undergo an ongoing learning process as they accumulate and confront more data, improving their diagnostic accuracy.
Medigy Insights
AI’s ability to accelerate clinical decision making has the potential to shift healthcare from a reactive to a proactive model. With 63% of the world’s deaths attributable to preventable diseases like heart disease, cancer and diabetes, AI-driven early intervention could have a profound impact on public health.AI systems analyzing patient data in real-time can detect subtle changes in a patient’s biometrics that may indicate disease progression or potentially critical events. Diagnostic algorithms have already been shown to reduce readmissions by up to 20% and operational costs by 25%.
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