Thursday, October 10, 2024

Here’s how AI is set to disrupt healthcare — albeit slowly

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The third quarter just wrapped, and the pure AI trade of Nvidia (NVDA) and its chip cohorts suddenly finds itself a net drag on overall S&P 500 performance — which itself surprised investors with a solid 5.5% return.

Nevertheless, generative AI is still hot and slowly moving into real-world applications.

Abby Yoder, US equity strategist at JPMorgan Private Bank, highlighted that healthcare, which is notoriously slow-moving, may be an upcoming candidate for AI-driven innovation.

“Healthcare has underperformed seven of the last eight years,” Yoder pointed out in a recent episode of Stocks in Translation, despite the sector being the only one in the S&P 500 to boast positive annual earnings growth over the past 21 years.

But Yoder sees AI as a potential key to unlock a long-awaited shake-up that could bring long-term growth back to the sector.

Healthcare stocks have struggled in recent years, a trend Yoder attributes to legacy constraints of old systems and obstacles that make it difficult for new technologies to take root. The byzantine web of players, rules, regulators, and more has so far prevented significant AI adoption, which has the potential to address inefficiencies in insurance approvals, manual record-keeping, and claims management, all of which drag on productivity.

WUHAN, CHINA - APRIL 07: A Da Vinci robot-assisted surgical system is operated to play games during the 6th World Health Expo on April 7, 2024 in Wuhan, Hubei Province of China. (Photo by VCG/VCG via Getty Images)

A Da Vinci robot-assisted surgical system is operated to play games during the sixth World Health Expo on April 7, 2024 in Wuhan, Hubei Province of China. (VCG/VCG via Getty Images) (VCG via Getty Images)

There are some ripples of change, however. Companies like Google and Microsoft are diving into this space, partnering with hospitals and startups to create AI tools that lighten this burden.

On the diagnostic side, AI is being used to streamline medical imaging, cutting down time for tasks like identifying patterns in medical data, which improves both speed and accuracy in patient care. The goal is not to rid the world of human radiologists and technicians, but empower them with 21st-century tools that lighten their load and accelerate patient diagnoses and recoveries.

AI proponents say it’s not just about cutting costs; it’s about revolutionizing patient care. By using vast datasets of clinical information, the promise of AI is to someday help predict patient outcomes more effectively, modeling care before it happens to anticipate complications and select treatments.

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The other great hope for medical AI bulls is the immense promise for drug discovery.

According to Morgan Stanley’s healthcare forecast, the company’s head of US biopharma research Terence Flynn estimates that “[every] 2.5% improvement in preclinical development success rates could lead to an additional 30-plus new drug approvals over 10 years,” which would represent around $70 billion.

Before the long tail of AI promises in healthcare are realized, Yoder emphasized that there are strong areas of growth in healthcare right now, like the strong performance of GLP-1 drugs, which have put a few pharma stocks in the same league as their AI counterparts in the Magnificent Seven. Past diabetes treatment and obesity, there’s still lots of potential for broad preventative care applications — particularly with type 2 diabetes, Yoder noted.

The data from the trials looks promising. “[The] rate of stopping you from turning into a type 2 diabetic is north of 98%,” she said.

But despite AI’s potential, significant roadblocks remain — and trust is a big one.

Over 55% of healthcare professionals believe AI isn’t ready for primetime medical use, according to a GE Healthcare survey of 7,500 clinicians around the world. And only 26% of US responsdents think AI can be trusted — lagging the 42% of those who responded globally. With actual life-or-death stakes, this represents a major hurdle, along with the web of legacy technology that would need to be integrated. Much of the infrastructure, like electronic medical records, is outdated and not built for seamless AI integration.

Another area facing delays is AI-driven surgery. While AI-powered robots already assist in remote telesurgeries, humans are still driving the bus, and the concept of fully autonomous surgical procedures is still far from reality.

Despite the industry’s inertia and barriers to change, Yoder remains optimistic about healthcare’s growth potential as a long-term investment. And should something happen to the economy and the Fed’s soft landing, the sector has a key advantage: It’s a defensive large-cap space with low volatility. As Yoder noted, not only is healthcare spending ticking up as a percentage of the GDP, but when a recession hits, healthcare keeps chugging along.

On Yahoo Finance’s podcast Stocks in Translation, Yahoo Finance editor Jared Blikre cuts through the market mayhem, noisy numbers, and hyperbole to bring you essential conversations and insights from across the investing landscape, providing you with the critical context needed to make the right decisions for your portfolio. Find more episodes on our video hub or watch on your preferred streaming service.

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