Investing in top growth stocks is a great way to achieve strong returns and potentially outperform the market as a whole. The S&P 500 is an index of the leading companies on the U.S. markets, and historically, it has risen by 10% per year, though that’s an average including up and down years. That return is not guaranteed, but at such a high rate, an investment would double after a little more than seven years.

One artificial intelligence (AI) stock that has routinely outperformed the broad index is Broadcom (AVGO -1.12%).

The semiconductor and infrastructure company has benefited from the growth in tech in recent years, and that has allowed it to outperform the market on a consistent basis. With strong gains once again so fare this year, is Broadcom still a great buy, or could it be due for a pullback?

Broadcom has been a top growth stock over the past decade

Here’s a look at just how well Broadcom has performed over the previous 10 years, compared to the S&P 500.

Data source: YCharts.

What’s surprising is that the one year when the S&P 500 did better than Broadcom was 2019, when the index finished higher at nearly 29%, versus 24% gains for Broadcom.

The past doesn’t predict the future, but the tech stock’s terrific run can’t be ignored. In 10 years, shares of Broadcom have risen by more than 2,000%, while the S&P 500 has increased by around 200%.

Can Broadcom’s impressive gains continue?

As of the end of last week, Broadcom’s stock was up around 19% for the year, which was comfortably above the S&P 500’s returns of more than 6%. But with a valuation of around $1.3 trillion and Broadcom trading at 33 times its estimated future earnings (based on analyst estimates), it’s not a cheap stock to own.

The biggest risk is that the company relies heavily on demand from hyperscalers. These are big tech giants that have significant infrastructure needs related to tech and AI. If they scale back on their expenditures, that could significantly weigh on Broadcom’s results. The company estimates that its top five customers account for around 40% of its revenue.

The company’s revenue during the most recent reported period — which ended on May 4 — grew by a rate of 20% year over year, as its top line came in at just over $15 billion, while profits more than doubled, rising to nearly $5 billion.

If Broadcom can continue producing strong results such as these, it wouldn’t be surprising to see it outperform the market once again this year. Though that risk of hyperscalers cutting spending remains.

Is Broadcom stock a buy right now?

If you’re bullish on AI and expect there to be much more growth ahead, Broadcom can make for a compelling investment to simply buy and hold. But at the same time, it’s also important to consider the risks ahead, especially as tariffs and trade wars could impact growth in the tech sector in the near future.

Earlier this year, Broadcom’s stock was underperforming the S&P 500 due to the uncertainty in the markets. While that looks like a distant memory right now, investors should brace for a possible slowdown for the stock as it’s trading at an elevated valuation and it may be due for a decline. Its track record may be impressive, but that by no means guarantees it’ll always be a market-beating stock.

I’d hold off on buying shares of Broadcom only because the markets appear to be a bit too bullish right now, and with high expectations priced in, there’s a lot of downside risk that comes with owning the stock. Broadcom isn’t a bad buy, but I think there are better AI stocks to invest in today.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

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But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate diagnoses, racial bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

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But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Humans and AI are beginning to work together at this Florida hospital.

Data-sharing concerns, unrealistic expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

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But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

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Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

AI could rapidly accelerate the discovery of new medications.

Incremental change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

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Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.

This article is republished from The Conversation under a Creative Commons license. Read the original article.