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Nvidia’s AI Bot Outperforms Nurses: Here’s What It Means for You  

By ROBBIE PEARL

Soon after Apple released the original iPhone, my father, an unlikely early adopter, purchased one. His plan? “I’ll keep it in the trunk for emergencies,” he told me. He couldn’t foresee that this device would eventually replace maps, radar detectors, traffic reports on AM radio, CD players, and even coin-operated parking meters—not to mention the entire taxi industry.

His was a typical response to revolutionary technology. We view innovations through the lens of what already exists, fitting the new into the familiar context of the old.

Generative AI is on a similar trajectory.

As I planned the release of my new book in early April, “ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine,” I delved into the promise and perils of generative AI in medicine. Initially, I feared my optimism about AI’s potential might be too ambitious. I envisioned tools like ChatGPT transforming into hubs of medical expertise within five years. However, by the time the book hit the shelves, it was clear that these changes were unfolding even more quickly than I had anticipated.

Three weeks before “ChatGPT, MD” became number one on Amazon’s “Best New Books” list,  Nvidia stunned the tech and healthcare industries with a flurry of headline-grabbing announcements at its 2024 GTC AI conference. Most notably, Nvidia announced a collaboration with Hippocratic AI to develop generative AI “agents,” purported to outperform human nurses in various tasks at a significantly lower cost.

According to company-released data, the AI bots are 16% better than nurses at identifying a medication’s impact on lab values; 24% more accurate detecting toxic dosages of over-the-counter drugs, and 43% better at identifying condition-specific negative interactions from OTC meds. All that at $9 an hour compared to the $39.05 median hourly pay for U.S. nurses.

Although I don’t believe this technology will replace dedicated, skilled, and empathetic RNs, it will assist and support their work by identifying when problems unexpectedly arise. And for patients at home who today can’t obtain information, expertise and assistance for medical concerns, these AI nurse-bots will help. Although not yet available, they will be designed to make new diagnoses, manage chronic disease, and give patients a detailed but clear explanation of clinician’ advice.

These rapid developments suggest we are on the cusp of technology revolution, one that could reach global ubiquity far faster than the iPhone. Here are three major implications for patients and medical practitioners:  

1. GenAI In Healthcare Is Coming Faster Than You Can Imagine

The human brain can easily predict the rate of arithmetic growth (whereby numbers increase at a constant rate: 1, 2, 3, 4). And it does reasonably well at comprehending geometric growth (a pattern that increases at a constant ratio: 1, 3, 9, 27), as well.

But even the most astute minds struggle to grasp the implications of continuous, exponential growth. And that’s what we’re witnessing with generative AI.

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7 Ways We’re Screwing Up AI in Healthcare

The healthcare AI space is frothy.  Billions in venture capital are flowing, nearly every writer on the healthcare beat has at least an article or two on the topic, and there isn’t a medical conference that doesn’t at least have a panel if not a dedicated day to discuss. The promise and potential is very real.

And yet, we seem to be blowing it.

The latest example is an investigation in STAT News pointing out the stumbles of IBM Watson followed inevitably by the ‘is AI ready for prime time’ debate. If course, IBM isn’t the only one making things hard on itself. Their marketing budget and approach makes them a convenient target. Many of us – from vendors to journalists to consumers – are unintentionally adding degrees to an already uphill climb.

If our mistakes led to only to financial loss, no big deal. But the stakes are higher. Medical error is blamed for killing between 210,000 and 400,000 annually. These technologies are important because they help us learn from our data – something healthcare is notoriously bad at. Finally using our data to improve really is a matter of life and death.

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