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03 Feb

Healthcare: Three Giants And Three Different Bets!

by Shailendra Gupta
in Blog
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Big Hospitals are thinking about whether they should wait for OpenAI, or go with Google, or maybe Anthropic through Microsoft?

 

That’s the wrong question to ask. Here’s why.

Three Giants, Three Completely Different Bets

  1. OpenAI launched ChatGPT for Healthcare on January 8.
  2. Anthropic launched Claude for Healthcare on January 11 (three days later).
  3. Google has been at this for years—Med-PaLM, AlphaFold, AMIE.

All impressive. Giants targeting healthcare.

 

But they’re playing three completely different games. And understanding which game they’re playing? That’s how others can figure out the real opportunity.

Let Me Show You What I Mean

I spent last weekend digging through their announcements. Not the press releases – the actual product pages, partnership lists, use cases.

 

And I realised something.

  • OpenAI is betting on doctors and patients.
  • Anthropic is betting on CFOs and payers.
  • Google is betting on researchers and radiologists.

Three different users. Three different problems. Three different go-to-market strategies. Let’s see what they are up to.

OpenAI: Bottom-Up (The Clinician Bet)

The Thesis: If you make AI useful for individual doctors and patients, they’ll demand it. And hospitals will have to adopt it.

 

What they’re actually doing:

  • Launched ChatGPT Health (patient-facing) on January 7
  • Launched ChatGPT for Healthcare (clinician workspace) on January 8
  • Powering patient-facing startups like Abridge (ambient listening), Ambience (clinical documentation), and EliseAI (appointment scheduling)

Who’s using it: Boston Children’s Hospital, Cedars-Sinai, Stanford Medicine, HCA Healthcare, UCSF.

 

The use cases:

  1. Clinical search (find the latest research in seconds)
  2. Patient education materials (translate discharge instructions into simple language)
  3. Referral letters (draft them in 2 minutes instead of 20)
  4. Clinical documentation (ambient listening while you talk to patients)

The strategy: Give it to individual clinicians. Let them fall in love with it. Then they’ll pressure their hospitals to adopt it org-wide.

 

This is bottom-up adoption. Doctors use it → Prove value → Hospital buys enterprise license.

Quote from Boston Children’s Hospital: Our early work with a custom OpenAI-powered solution allowed us to move quickly, prove value in a secure environment, and establish strong governance foundations. ChatGPT for Healthcare offers a path toward operational scale.”

See that? Prove value first. Then the operational scale. That’s bottom-up.

Anthropic: Top-Down (The CFO Bet)

The Thesis: If you solve expensive administrative problems, CFOs and CIOs will buy enterprise licenses. No need to convince individual doctors.

 

What they’re actually doing:

  • Launching through Microsoft Foundry (enterprise licensing model)
  • Building connectors to enterprise systems (CMS database, ICD-10 billing codes, National Provider Identifier Registry, FHIR)
  • Targeting payers (insurance companies), health systems (revenue cycle teams), and pharma (clinical trials, regulatory submissions)

The use cases:

  1. Prior authorisation (speed up insurance approvals that take hours)
  2. Claims appeals (reduce denials that cost billions)
  3. Care coordination (triage thousands of patient portal messages)
  4. Clinical trial management (pharma workflows)
  5. Regulatory submissions (FDA, EMA approvals)

The strategy: Sell to the CFO or CIO. Show clear ROI (cost savings, efficiency gains). They buy an enterprise license. Deploy org-wide.

 

This is top-down adoption. CFO buys it → Organisation deploys it → Measures ROI.

Quote from Anthropic: These tools can be used to speed up prior authorisation requests so that patients can get life-saving care more quickly, can help with patient care coordination to reduce the pressures on clinicians’ time, and help with regulatory submissions so that more life-saving drugs can come to market faster.

See the focus? Speed up (efficiency). Reduce pressures (cost savings). Faster submissions (operational ROI). That’s top-down.

Google: Research-First (The Validation Bet)

The Thesis: If you prove AI works through rigorous research and FDA clearances, academic medical centres and enterprises will trust it.

 

What they’re actually doing:

  • 260+ physicians across 60 countries evaluated 600,000+ model outputs over two years
  • Published in Nature and JAMA (peer-reviewed journals)
  • FDA clearances for dermatology AI and diabetic retinopathy screening
  • AlphaFold (revolutionised protein structure prediction—used globally)
  • AMIE (conversational diagnostic agent—research only, not deployed yet)
  • Med-PaLM 2 (extensive clinical benchmarking)

The use cases:

  1. Diagnostic imaging (X-rays, CT scans, MRI analysis)
  2. Medical question answering (Med-PaLM 2)
  3. Protein structure prediction (AlphaFold for drug discovery)
  4. Clinical record search (Vertex AI Search for Healthcare)
  5. Personalised wellness (wearables, sensor data from phones/watches)

The strategy: Do the research first. Validate clinically. Get FDA clearances. Publish in peer-reviewed journals. Then deploy through Google Cloud. This is research-first adoption.

 

Research proves it works → FDA clears it → Academic medical centres adopt → Enterprises deploy. That’s the Research First approach.

Quote from Google: We’ve been investing in AI for more than a decade—leading to breakthrough AI systems like AlphaFold, and our foundational model, Gemini… With a bold and responsible approach, we’re taking the next steps to make this technology even more helpful for everyone.

See the emphasis? Decade of investment. Breakthrough systems. Responsible approach. That’s research-first.

Here’s the Comparison:

So, What Does This Mean for You?

If you’re building in healthcare AI, here’s how to think about it:

 

Utilising OpenAI: Build patient-facing and clinician-facing tools. Use OpenAI API to power ambient listening, clinical documentation, and patient education. Focus on individual user experience (doctors, patients). GTM: Bottom-up (doctors demand it).

 

Utilising Anthropic: Build enterprise workflow automation. Use Claude to automate prior auth, claims appeals, and care coordination. Focus on ROI for CFOs (cost savings, efficiency).GTM: Top-down (sell to CIO/CFO).

 

Utilising Google: Build clinically validated diagnostic tools. Use Google Cloud/Vertex AI for imaging, diagnostics, and research. Focus on clinical accuracy and FDA clearances. GTM: Research-first (publish, validate, deploy).

Here is what we at Mind IT Systems think, and are building accordingly:

All three will coexist. Because they’re solving different problems for different users.

  • OpenAI will dominate clinical workflows and patient-facing apps. Doctors already love ChatGPT. They’ll use ChatGPT for Healthcare. Startups will build on the OpenAI API.

 

  • Anthropic will dominate enterprise operations and pharma. CFOs will buy Claude for prior auth and claims (clear ROI). Pharma will use Claude for trials and regulatory submissions.

 

  • Google will dominate diagnostics and research. Academic medical centres will use Google for imaging and research. Enterprises will use Vertex AI for clinical record search.

The Real Opportunity? It’s Not Competing with Them. Hospitals need not wait for OpenAI, Anthropic, or Google to solve their problems. They’re building intelligence. Hospitals need solutions.

 

Use their APIs. But build the integration layer, the workflow automation, the compliance framework, the training program, and the change management. That is where the value is. Last mile. Vertically deep dive, not horizontal anymore.

My Prediction for 2026

By the end of 2026:

  • All three Giants will have impressive case studies.
  • Adoption will still be under 20% in most hospitals.
  • The bottleneck will be implementation, not intelligence.
That's why we at Mind IT Systems are combining multiple data sources in a Hospital, providing insights, strategising actionable intelligence, and then doing the last-mile delivery.

Mind IT – Digital Healthcare Solutions impacting the last mile!

Final Thought

The opportunity isn’t in competing with OpenAI, Anthropic, or Google. The opportunity is in making their AI actually usable in real hospitals.

 

What’s your take? If you’re building in healthcare AI, which layer are you focusing on?

  • Intelligence (foundation models)
  • Implementation (integration, workflows)
  • Validation (compliance, monitoring)

I’d love to hear what others are working on.

Sources:

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About the Author

Shailendra

Shailendra Gupta
(Co-Founder and CEO of Mind IT Systems)

 

Shailendra is Co-Founder and CEO of Mind IT Systems and is responsible for strategy and business relations.

With around two decades of experience in getting things done in marketing, sales, strategy, delivery, or technology, he has a successful track record of leading startups and mid-size companies and being a prime contributor to stakeholder management, growth, and value creation. A thought leader in the geo-social space, he is highly respected for realizing new paradigms in marketing, solutions, and approaches.