Private AI vs. Public AI in Healthcare: Which Side Are You On?

Privacy, cost, control. Healthcare leaders are facing a make-or-break choice. Here’s what you need to know.

Handoff #12 (Insider Edition) | Reading time: 8 minutes

Hospitals today are caught between two brutal choices: pay millions to build a fortress around sensitive patient data, or trust tech giants to keep it safe in the cloud.

Neither option is safe. Neither is simple.

The reality? AI isn’t just another tool in the healthcare toolbox. It’s becoming the nervous system of modern medicine.

McKinsey says AI could unlock $150 billion in annual healthcare savings. But don’t let the shiny numbers fool you. Implementing AI isn’t pocket change. Entry-level systems start around $40,000, advanced setups soar past $200,000, and that’s before you even glance at HIPAA compliance bills ranging from $10,000 to $150,000.

This decision will write the next chapter of your organisation's story. And it’ll either read like a success case study, or a cautionary tale.

Let's make sure it's the former.

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First, Understand the Battleground

Before you pick a side, know the terrain.

Public AI is your hyperscaler playground: AWS, Google Cloud, Microsoft Azure Health. Think fast deployment, pay-as-you-go flexibility, and access to the flashiest models money (or a subscription fee) can buy. Hyperscalers spend billions on security because their business depends on it.

Private AI is homegrown and hospital-hosted. It's about sovereignty. Total data ownership. Maximum control over workflows and regulatory alignment. It's costly, yes, but it gives you the kind of independence no public provider can guarantee.

In healthcare, where lives and litigation ride on data decisions, this isn’t academic theory. This is the core of your future operations.

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Private AI: Build Your Castle, But Bring Your Chequebook

If control is what keeps you up at night, private AI will help you sleep a little easier.

Valley Medical Center isn’t just a fan, they’re proof it works. After implementing Xsolis’ Dragonfly Utilize platform, case reviews skyrocketed from 60% to 100%. Discharged patient observation rates jumped from 4% to 13%. Not marginal gains. Game-changing ones.

University of Alabama at Birmingham Medicine? They took precision to a new level, using the Sickbay platform to synchronise patient data and personalise intraoperative blood pressure targets. Translation: safer surgeries, better outcomes, fewer surprises.

OSF HealthCare nailed both sides of the coin. Their AI assistant "Clare" slashed $1.2 million from contact centre costs while adding $1.2 million to annual patient revenue.

But let’s talk costs without flinching:

• Deep learning models: $60k–$100k

• Generative AI implementations: $200k+

• HIPAA compliance alone: up to $150k

• And don’t forget ongoing maintenance and the talent to run it.

Private AI is a fortress. Expensive to build, but once it stands, it’s yours.

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Public AI: Speed and Scale — For a Price

Public AI is a tempting fast lane.

You get instant scale. Advanced models at your fingertips. And cloud security that outclasses most internal IT defences.

Zauron Labs used Meta’s open-source Llama model to tackle a massive problem: 3 billion imaging exams per year, 3-5% error rate. They built "Guardian AI", a diagnostic spellchecker for radiologists. The result? Fewer diagnostic errors. Healthier patients.

Mendel's Hypercube reduced clinical trial matching time from hundreds of days to just one. One. That’s what happens when you leverage public models and fine-tune them for precision healthcare use.

Public AI feels like borrowing a Formula 1 car: blistering speed, high performance, but you don’t own the chassis, and the pit crew isn’t yours.

Risks?

• Vendor lock-in: you could become dependent on tech giants.

• Cross-border data flow issues.

• Black-box decision-making.

It’s fast. But it’s not free.

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What’s Really Driving These Decisions?

Spoiler: it’s not just the budget.