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What do doctors really need to know about AI? Here are several key points
By Sahika Betul Yayli  |  Jun 16, 2023
What do doctors really need to know about AI? Here are several key points
Image courtesy of and under license from Shutterstock.com
AI has greatly impacted the healthcare sector, though many physicians remain unaware of the extent of its capabilities. Doctors and other medical professionals do not necessarily need to become AI experts, though they should bear in mind a few key points.

ISTANBUL - One sector benefiting the most from the rise of AI is the health sector. Where do doctors stand amid this new and transformative technological change and, even more importantly, what do doctors actually need to know about AI?

Is AI an aid to doctors or a replacement?
Doctors must be aware that AI is hardly going to replace them. Sci-fi-like depictions of robots that replace doctors by taking patients’ medical histories, evaluating them, and organizing their treatments are not yet on the horizon. Today’s AI applications are assistants in clinical practice, and their purpose is to save time for doctors, increase diagnostic accuracy, and improve healthcare quality.

Before X-ray devices became widespread, the most important diagnostic tool for pneumonia was the stethoscope, though as technology advanced information from X-rays became far more helpful to doctors. However, no doctor would ever make a diagnosis based solely on a radiograph without examining the patient first. Instead, doctors use technology as an aid to complement examinations and provide insights, with physicians themselves compiling results. AI is similarly just an advanced assistant for doctors, helping in the following ways:

- Easily monitoring patients’ health: With the aid of AI, doctors can quickly detect a deteriorating condition or abnormality in a patient's vitals thanks to warnings that the software produces. This can also happen with data collected via wearable devices or monitoring in areas such as intensive care units (ICUs).

Current approaches by clinicians for the early prediction of patient deterioration can be estimated using manually calculated screening metrics called early warning scores (EWS). While EWS systems have excellent predictive values, they are limited by their intermittent nature. However, Alshwaheen et al. (2021) proposed a novel framework

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