This is what it means for you

Shortly after Apple released the original iPhone, my father, an unlikely early adopter, bought one. His plan? “I keep it in the trunk for emergencies,†he told me.

At the time, he couldn’t have foreseen that this device would eventually replace maps, radar detectors, AM radio traffic reports, CD players, and even coin-operated parking meters—not to mention the entire taxi industry.

Like most people, my father couldn’t imagine how a phone would completely change our personal and professional lives. He was a typical response to revolutionary technology. We view innovations through the lens of what already exists, placing the new in the familiar context of the old.

Generative AI is on a similar trajectory. As industries invest billions in this technology to streamline processes, improve operations and accelerate R&D, even those well-versed in its capabilities are amazed by AI’s lightning-fast advancements. I count myself among them.

Six months ago I started writingChatGPT, MD”, a book about the promise and the perils of generative AI in medicine. Initially, I feared that my optimism about the technology was too ambitious.

Based on the exponential growth projections…doubles in power every yearI foresaw that within the next five years it would become a center of medical expertise for doctors and patients (at which point it will be 32 times as capable).

And yet, by the time my book was published last week, I realized that my estimated timeline might have been too conservative.

In March, Nvidia stunned the technology and healthcare sectors with a flurry of announcements GTC AI Conference 2024. The California-based chipmaker, which owns 80% of the global semiconductor market, has released a series of high-profile products and partnerships that could revolutionize everything from diagnosis and healthcare delivery to drug discovery and healthcare economics.

The most notable announcement came when Nvidia and Hippocratic AI unveiled their partnership generative AI agents which are said to outperform human nurses in performing a variety of tasks, all at a significantly lower cost.

According to data released by the companythe AI ​​bots are 16% better than nurses at identifying the impact of a drug on laboratory values; 24% more accurate at detecting toxic doses of over-the-counter medications, and 43% better at identifying condition-specific negative interactions of over-the-counter medications. All that for $9 an hour, compared to the average hourly wage of $39.05 for American nurses. These AI nurse bots are designed to make new diagnoses, manage chronic diseases and provide patients with a detailed but clear explanation of the doctor’s advice.

These rapid developments suggest that we are on the cusp of a technological revolution, one that could achieve global ubiquity much faster than the iPhone. Here are three important implications for patients and doctors:

1. GenAI in healthcare is coming faster than you can imagine

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

But even the sharpest minds struggle to understand the implications of continued, exponential growth. And that’s what we’re seeing with generative AI.

For example, imagine a pond with only one lily pad. Assuming the number of lilies doubles every night, the entire pond will be covered in just 50 days. Yet on day 43 you would see only 1% of the pond surface covered. It seems hard to believe that just seven days later the water lilies will completely fill the pond.

Experts predict that AI computing advances will roughly double every year, if not faster. But even with conservative projections, ChatGPT and similar AI tools will be 32 times more powerful in five years and over a thousand times more powerful in ten years. That’s equivalent to your bicycle traveling as fast as a car and then, shortly after, a rocket.

This pace of progress is proving difficult for both healthcare providers and patients to understand, but it means now is the time to prepare for what’s to come.

2. GenAI will be different from previous AI models

When assessing the transformative potential of generative AI in healthcare, it is critical to acknowledge past failures such as Watson from IBM, cloud our expectations. IBM set ambitious goals for Watson, hoping it would revolutionize healthcare by helping with diagnoses, treatment planning and interpreting complex medical data for cancer patients.

I was very skeptical at the time, not because of the technology itself, but because Watson relied on data from electronic health records, which lacked the accuracy needed to make reliable “limited AI” diagnoses and recommendations.

Generative AI, on the other hand, uses a broader and more useful range of information sources. It not only uses published, peer-reviewed medical journals and textbooks, but will also be able to integrate real-time information from global health databases, ongoing clinical trials and medical conferences. Soon it will include continuous feedback loops from actual patient outcomes and physician input. This extensive data integration will enable generative AI to continuously stay at the forefront of medical knowledge, making it fundamentally different from its predecessors.

That said, generative AI will need a few more generations before it can be widely used without direct supervision from a doctor. But Nvidia’s bold entry into healthcare signals a long-awaited willingness among tech companies to sidestep healthcare’s legal and regulatory hurdles. Once an AI chatbot for doctors is available, several other companies will soon follow suit.

3. GenAI in healthcare will be ubiquitous (hospital, office and home)

Just as my father never imagined that his iPhone (stored in his trunk) would grow into an essential tool for navigating life, many Americans are struggling to imagine the transformative impact generative AI will have on healthcare.

The concept of having continuous access to medical advice and expertise – affordable, reliable and convenient, 24 hours a day – represents such a departure from current healthcare models that it is easy for us to dismiss it as far-fetched. Yet it is becoming increasingly clear that these possibilities are not only possible, but also probable.

Every day I receive feedback from both physicians and patients who have interacted with current generative AI tools. The majority report that responses, especially when prompted effectively, closely follow physicians’ recommendations. This is a testament to the evolving accuracy and reliability of generative AI in healthcare, and promises to revolutionize medical care delivery in the near future.

Ten years from now, we’ll look back on today’s skepticism the same way I think about my father’s initial underestimation of his iPhone. We are on the cusp of a major shift, where generative AI will become as integral to healthcare as smartphones have become to everyday life.