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.

Palantir Technologies was the top-performing stock in the S&P 500 and Nasdaq-100 during the first half of 2025. With shares soaring by 80% through the first six months of the year — and by 427% over the last 12 months — Palantir has helped drive a lot of attention to the intersection of artificial intelligence (AI) and defense contracting.

Palantir is far from the only company seeking to disrupt defense tech. A little-known competitor to the company is BigBear.ai (BBAI -3.35%), whose shares are up by an impressive 357% over the last year.

Could BigBear.ai emerge as the next Palantir? Read on to find out.

BigBear.ai is an exciting company in the world of defense tech, but…

BigBear.ai’s share price volatility so far this year mimics the movements of a rollercoaster. Initially, shares rose considerably shortly following President Donald Trump’s inauguration and the subsequent announcement of Project Stargate — an infrastructure initiative that aims to invest $500 billion into AI projects through 2029.

BBAI data by YCharts

However, these early gains retreated following the Pentagon’s plans to reduce its budget by 8% annually.

While reduced spending from the Department of Defense (DOD) was initially seen as a major blow to contractors such as Palantir and BigBear.ai, the trends illustrated above suggest that shares rebounded sharply — implying that the sell-offs back in February may have been overblown. Why is that?

In my eyes, a major contributor to the recovery in defense stocks came after Defense Secretary Pete Hegseth announced his intentions to double down on a strategy dubbed the Software Acquisition Pathway (SWP).

In reality, the DOD’s budget cuts are focused on areas that are deemed non-essential or inefficient. For example, the Pentagon freed up billions in capital by reducing spend with consulting firms such as Booz Allen Hamilton, Accenture, and Deloitte. In addition, a contract revolving around an HR software system managed by Oracle was also cut.

Under the SWP, it appears that the DOD is actually looking to free up capital in order to double down on more tech-focused initiatives and identify vendors that can actually handle the Pentagon’s sophisticated workflows.

With so much opportunity up for grabs, it’s likely that optimistic investors saw this as a tailwind for BigBear.ai. This logic isn’t too far off base, either.

BigBear.ai’s CEO is Kevin McAleenan, a former government official with close ties to the Trump administration. McAleenan’s strategic relationships within the government combined with the DOD’s focus on working with leading software services providers likely has some investors buying into the idea that BigBear.ai won’t be flying under the radar much longer.

Image source: Getty Images.

…how does the company really stack up beside Palantir?

The graph below breaks down revenue, gross margin, and net income for BigBear.ai over the last year. With just $160 million in sales, the company tends to generate inconsistent gross margins — which top out at less than 30%. Moreover, with a fairly small sales base and unimpressive margin profile, it’s not surprising to see BigBear.ai’s losses continue to mount.

BBAI Revenue (TTM) data by YCharts

By comparison, Palantir generated $487 million in government revenue during the first quarter of 2025. In other words, Palantir’s government operation generates nearly triple the amount of revenue in a single quarter that BigBear.ai does in an entire year. On top of that, Palantir’s gross margins hover around 80%, while the company’s net income over the last 12 months was over $570 million.

Is BigBear.ai stock a buy right now?

Right now, BigBear.ai trades at a price-to-sales (P/S) ratio of around 11. While this may look “cheap” compared to Palantir’s P/S multiple of 120, there is a reason for the valuation disparity between the two AI defense contractors.

Palantir boasts large, fast-growing public and private sector businesses that command strong profit margins. By contrast, BigBear.ai is going to have a difficult time scaling so long as it keeps burning through heaps of cash.

Not only would I pass on BigBear.ai stock, but I also do not see the company becoming the next Palantir. Palantir is in a league of its own in the defense tech space, and I do not see BigBear.ai as a formidable challenger.