Andy Pollen

More Myths About Healthcare AI Dismantled

After our post outlining three pervasive misperceptions about healthcare AI, we received an influx of comments via LinkedIn and our monthly newsletter with additional myths that need to be busted. Would you add any others to the list?

Myth: AI is only algorithms

The words artificial intelligence and algorithm are often used interchangeably, but there are important distinctions between the two. In essence, algorithms are the instructions that power artificial intelligence. Algorithms can be basic – like knowing what advertisement to show a person based on internet search history – or more complex. Most references to healthcare AI focus on complex algorithms that are intelligent, meaning they can learn, make decisions and problem solve.

Source: Aidoc

Myth: Clinical AI is unproven technology

AI has been used in clinical settings for decades, but clinical AI – intelligent solutions that help enable better diagnostic and treatment decisions – has seen slower adoption due to concerns over evidence, cost and regulatory barriers. Yet evidence has proven – in many cases multiple times and multiple different ways – the clinical, financial and operational benefits of AI.

Source: ResearchGate

Myth: AI implementation in a health system will be more trouble than it’s worth

This largely depends on your AI partner and its capabilities. With any integration into existing systems, there could be technical barriers. An AI operating system  offers clear benefits for a smooth implementation. This operational layer connects existing systems, creates a unified user experience, deploys algorithms and monitors performance – all automatically.

Source: Aidoc

A resource for you

Aidoc hosts a variety of healthcare AI education – from foundational information to clinical evidence and on-demand webinars – all intended to help build an understanding of the multi-faceted upstream and downstream benefits of clinical AI.

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Andy Pollen