I'm a neuroscientist and professor of neurobiology and ophthalmology and, by courtesy, psychiatry and behavioral sciences at Stanford School of Medicine. My work focuses on brain development, function, and neuroplasticity.

I host the Huberman Lab podcast, where we discuss science and science-based tools for everyday life.

Andrew Huberman

Harvey Castro, MD, MBA.

ChatGPT Healthcare Advisor • Physician with Executive MBA • AI in Healthcare Author Ex-CEO & Physician, aiding in AI's healthcare integration. Empowering physicians, patients, authoring the AI in healthcare revolution.

Confused on AI implementation? We help 👋 | Top AI Voice | Founder of Everyday AI, a Top 10 Tech Podcast | AI Strategist | AI Consultant | AI Keynote Speaker | Helping companies leverage GenAI

At 2 a.m. in the emergency room, seconds decide outcomes. Soon, those same decisions will be shaped by AI models, planetary-scale data, and infrastructure that lives beyond Earth.

I'm Dr. Harvey Castro, ER physician, Chief AI Officer at Phantom Space, and the person connecting those two worlds.

For over 20 years I treated crisis after it walked 
through the door. That experience burned one conviction into me: the future of healthcare isn't faster reactions. 

It's earlier signals.The problem health systems face today isn't lack of data. It's that the data lives in silos, the compute lives in buildings that flood and fail, and the AI lives in research papers that never reach the bedside.

I'm working to fix that from orbit.

At Phantom Space I lead AI strategy for space-based health infrastructure: orbital compute networks, satellite data pipelines, and edge inference tools that bring predictive intelligence to clinicians who need it most whether they're in Dallas or Djibouti.

What this looks like in practice:

Space-based compute that survives disasters and serves communities hospitals can't reach.

AI that anticipates illness before it becomes crisis reducing burnout, protecting patient safety, and giving physicians back their judgment.

Human-centered design that ensures the technology serves the clinician, not the other way around.

I also advise health systems, governments, and 
regulators on how to deploy AI safely, ethically, 
and at scale without losing the humanity 
that medicine requires.

The bottom line is simple.

Compute in orbit. Care on Earth.

If you are building the future of healthcare 
or trying to understand it. I would like to 
connect.

Chief Executive Officer

Medical Intelligence Ops, where we are pioneering the integration of Large Language Models (LLMs) into healthcare. Our physician-led team is committed to transforming patient care and hospital operations through intelligent solutions.

Open ai ChatGPT Health, Anthropic AI Health and Now Microsoft Copilot Health

View newsletter: Apple Vision + AI  Healthcare 

The 2026 Shift Toward Integrated Personal Wellness Read my newsletter here ( see report) Healthcare didn’t just evolve in 2026. It crossed a line we m

Open article: The Architecture of Medical Superintelligence by Harvey Castro, MD, MBA. • 3 min read

The Architecture of Medical Superintelligence

AI is making smart doctors slower.

Not hypothetically.

Actually slower.

A randomized trial found experienced developers using AI tools were 19% slower than those without them  .

I’m watching the same thing happen in healthcare.

AI scribes were supposed to reduce burnout.

Instead, many physicians are paying a new price:

The correction tax.

Reviewing.
Editing.
Double-checking.
Second-guessing.

The problem isn’t the AI.

It’s that we bolted it onto broken workflows.

We’re running a new engine on an old transmission  .

We kept all the old layers:
• Manual approvals
• Redundant documentation
• Billing reviews
• Endless clicks

Those systems were built for human limitations.

AI doesn’t need them.

But we never deleted them.

So productivity dips.

And burnout shifts instead of disappearing.

Here’s the shift healthcare leaders need to make:
	1.	Stop deploying AI like an intern.
	2.	Redesign workflows, not just tools.
	3.	Delete unnecessary review rituals.
	4.	Elevate clinicians to judgment not typing.

AI won’t fix burnout by making documentation faster.

It fixes burnout when documentation becomes invisible.

The real question isn’t:

How do we make doctors faster?

It’s this:

Are we brave enough to redesign healthcare around the technology we’re deploying?
Dwight Pond (based on your comment I wrote this post.) 

#HealthcareAI #PhysicianBurnout #DigitalHealth

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Physician Burnout & AI: How Technology Can Support Provider Well-being in Modern Healthcare

The future of AI in healthcare won’t be decided by intelligence alone.
It will be decided by AQ.

Most people know that IQ is the Intelligence Quotient, which is your cognitive ability.

Some know EQ, Emotional Quotient, your ability to understand and manage emotions.

But few talk about the AQ (Adversity Quotient).

AQ is your ability to respond to setbacks, pressure, uncertainty, and failure.
And in healthcare AI… adversity is constant.

I recently revisited Adversity Quotient: Turning Obstacles into Opportunities by Paul G. Stoltz

Here’s the equation that changed how I think about leadership:

Performance ≈ (IQ + EQ) × AQ

Let me define that clearly:

• IQ (Intelligence Quotient) = analytical ability, knowledge, technical skill

 • EQ (Emotional Quotient) = empathy, self-awareness, interpersonal regulation

 • AQ (Adversity Quotient) = resilience under stress, persistence through setbacks

Now here’s the key:
If AQ = 0…

Everything else gets multiplied by zero.

You can be brilliant.
 You can be emotionally intelligent.

But if you collapse under pressure, innovation collapses with you.
Stoltz breaks AQ into four components: the CORE model.

C — Control
 How much influence do you believe you have in a difficult situation?
 In AI adoption, you can’t control regulation or headlines.
 But you can control governance, training, and safeguards.

O — Ownership
 Taking responsibility for improving the outcome — even if you didn’t cause the problem.
 When an algorithm misfires, blame slows progress.
 Ownership improves systems.

R — Reach
 How far does adversity spread in your thinking?
 One failed AI pilot does not mean “AI doesn’t work.”
 It means that the pilot failed.

E — Endurance
 How long do you believe adversity will last?
 AI transformation is not a quarterly project.
 It’s a decade-long climb.

In healthcare AI, adversity is guaranteed:

• Algorithms will make errors
 • Clinicians will resist change
 • Regulators will evolve policies
 • Implementations will stall

The hospitals that win won’t be the smartest.
They’ll be the ones with the highest AQ.

Because in medicine:
AI doesn’t replace doctors.
 It stress-tests them.

And the real formula becomes:
Resilient Physician + Responsible AI = Sustainable Innovation
So here’s what I’m reflecting on:

As AI accelerates…
 Are we training physicians in intelligence 

But not in adversity intelligence?

🚨 The Real AI Risk No One Talks About in Medicine

AI isn’t the threat our decisions about it are.

In my latest work, AI Lessons from Movies, one theme is undeniable: the most catastrophic failures aren’t technical they’re human decisions surrounding AI use.

🎯 What future physicians must understand:

🧠 Decision > Technology
AI systems like HAL 9000 or WarGames didn’t fail from code they failed from unchecked decisions and misplaced trust.

⚖️ Trust Must Be Earned, Not Assumed
Accuracy alone is insufficient. In medicine, blind reliance on AI in triage, diagnostics, or risk scoring can quietly amplify harm.

🔍 Oversight Is Non-Negotiable
From Minority Report to Ex Machina, the lesson is clear: when humans step back, consequences accelerate.

The pattern is undeniable AI doesn’t need intent to cause harm; it only needs unchallenged authority.

I’ve written this as a directive: decisions must be scrutinized, governance must be intentional, and human judgment must remain central. The future of medicine will not be shaped by algorithms alone, but by the discipline with which they are questioned, constrained, and overridden when necessary.

The defining question isn’t “Can AI do this?”
It’s “Should we allow it to?”

What decisions are you delegating to AI today and are you fully prepared to defend them?

#AI #Healthcare #MedicalEducation #Leadership #Ethics #FutureOfMedicine #DigitalHealth #Innovation #DRGPT

Follow @HarveyCastroMD

Most AI failures are not technical.

They’re organizational.
The model worked.
The system failed.

Roles were unclear.
Oversight was fragmented.
Responsibility was diluted.

Healthcare learned this early.

When everyone owns a system,
no one is accountable for outcomes.

AI doesn’t fail where it’s built.
It fails where it’s deployed.

Leaders often ask,
Is the AI accurate?

The better question is,
Who is responsible when it’s wrong?

Best practices to prevent organizational failure:

Define single-point accountability
Clarify escalation paths before deployment

Train leaders, not just users
Align incentives with safe outcomes
Review failures without blame, but with ownership

AI maturity isn’t technical excellence.
It’s organizational clarity.

Harvey Castro, MD, MBA.  
Follow me and repost

#AI #ArtificialIntelligence #Leadership #AIGovernance #ResponsibleAI
#DigitalTransformation #RiskManagement #HealthcareAI #DrGPT

3:47 AM. The Algorithm Said Low Risk.


3:47 AM.

Full ER.

Fatigue setting in.

A patient comes in with vague abdominal pain.

Vitals stable.
Labs borderline.
AI triage model: “Low Risk.”

Green dashboard.

Reassuring probability score.

But medicine isn’t practiced on color-coded screens.

It’s practiced in rooms.

Here’s what moments like this teach us about AI in healthcare:

• Risk scores don’t feel anxiety.
Patients do.

• Probabilities don’t capture trajectory.
People change faster than models update.

• Low risk doesn’t mean no risk.
It means “unlikely.” Not impossible.

• The final decision is still human.
Technology informs. Physicians decide.

AI is powerful.

But its greatest strength isn’t certainty.

It’s tension.

It forces us to pause.

To double-check.

To question.

The best use of AI in medicine isn’t blind trust.

It’s informed friction.

That’s how safety improves.

Not by replacing judgment but by sharpening it.

Clinicians 

Have you ever overridden an algorithm?

What made you do it?

#EmergencyMedicine
#HealthcareAI
#ClinicalDecisionMaking
#PatientSafety
#DrGPT

In high-stakes systems,
explainability matters more than accuracy.

A perfect answer you can’t understand
is a dangerous one.

In medicine,
a correct diagnosis without reasoning
is not enough.

#Trust requires understanding.

If you can’t explain it, you can’t defend it.
Accuracy tells you what happened.
Explainability tells you whether you should trust it.

Leaders should ask:

Can my team understand why this decision was made?

Best practices for explainable AI:

Prioritize interpretable outputs
Expose reasoning, not just results

Match explanations to user expertise
Require justification for critical decisions

Reject black boxes in high-risk domains
AI that cannot explain itself
will eventually lose trust.

Harvey Castro, MD, MBA.  
Follow me and repost
#AI #ArtificialIntelligence #ExplainableAI #ResponsibleAI #Leadership
#AIGovernance #RiskManagement #HealthcareAI #DrGPT

Everyone talks about AI making cities smarter.

Fewer people talk about what happens when those systems fail.

As AI becomes embedded across transport, energy, healthcare, and public services, something new emerges:

Interconnected intelligence.

But interconnected systems have a hidden property.

When one breaks…
others can fall with it.

This is one of the central questions we explored in this new whitepaper on AI governance for digital public services and smart cities.

Proud to contribute alongside an incredible group of global experts.

One insight stood out:

Smart city systems are becoming critical infrastructure.
And critical infrastructure must be designed for resilience not just efficiency.  

That means thinking about:

• interoperability across vendors
• transparency and explainability
• fallback modes when systems fail
• human oversight in high-risk decisions

From my perspective working on AI infrastructure at Phantom Space, resilience may also require rethinking where compute lives.

Edge.
Cloud.
And eventually space-based infrastructure designed to keep AI systems operating even when terrestrial systems fail.

Because the future of AI isn’t just smarter software.

It’s resilient intelligence infrastructure.

Curious what others think:

What is the biggest risk we face as cities become AI-driven?


#AI #SmartCities #AIgovernance #TrustworthyAI #Infrastructure #DrGPT

Harvey Castro

At 2 a.m. in the emergency room, seconds decide outcomes.
Soon, those same decisions will be shaped by AI models, planetary-scale data, and infrastructure that lives beyond Earth.

I’m Dr. Harvey Castro, an ER physician and Chief AI Officer at Phantom Space, working at the intersection of medicine, artificial intelligence, and space-based computing to help healthcare move from reactive to predictive without losing its humanity.

For over 20 years, I treated crisis after it walked through the door.
That experience shaped my conviction: the future of healthcare isn’t faster reactions, it’s earlier signals.

Today, I focus on how:

AI + clinical data

Edge computing, satellites, and orbital data centers

Human-centered design

can work together to anticipate illness, reduce burnout, and protect patient safety at scale.

What I’m working on now

Serving as Chief AI Officer at Phantom Space, helping design AI strategies that leverage space-based data pipelines and resilient orbital infrastructure for real-world health impact

Advising health systems, governments, and regulators on safe, ethical, deployable AI

Translating frontier technologies generative AI, ambient sensing, edge inference into tools clinicians can actually trust and use

📢 𝐖𝐇𝐈𝐓𝐄𝐏𝐀𝐏𝐄𝐑: 𝐂𝐚𝐧 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐬𝐦𝐚𝐫𝐭 𝐜𝐢𝐭𝐢𝐞𝐬 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐡𝐚𝐫𝐦𝐢𝐧𝐠 𝐑𝐄𝐒𝐈𝐋𝐈𝐄𝐍𝐂𝐄 [𝐇𝐢𝐧𝐭: 𝐢𝐧𝐭𝐞𝐫𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐫𝐞𝐚𝐭𝐞 𝐜𝐚𝐬𝐜𝐚𝐝𝐢𝐧𝐠 𝐁𝐈𝐆 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬.]

📜 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐏𝐮𝐛𝐥𝐢𝐜 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬: GOVERNANCE Challenges in AI-Enabled Smart Cities examines how Europe can build interoperable, trustworthy urban infrastructure as AI becomes core to transport, energy, HEALTH, and public administration - linked-high-risk sectors under European Commission's EU AI Act. 

As municipalities rapidly deploy AI-driven services—𝘧𝘳𝘰𝘮 𝘵𝘳𝘢𝘧𝘧𝘪𝘤 𝘮𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵 𝘵𝘰 𝘴𝘮𝘢𝘳𝘵 𝘮𝘦𝘵𝘦𝘳𝘪𝘯𝘨 𝘵𝘰 𝘥𝘪𝘨𝘪𝘵𝘢𝘭 𝘩𝘦𝘢𝘭𝘵𝘩 𝘳𝘦𝘤𝘰𝘳𝘥𝘴—the challenges extend beyond individual systems. Tightly coupled platforms, cross-border data flows, and multi-vendor service chains create systemic RISKS that can cascade across sectors and jurisdictions, affecting millions of citizens simultaneously.

Developed by AI & Partners with a global network of legal, technical, policy, and civil-society experts, the whitepaper explores how regulatory frameworks can operationalise fairness, transparency, and operational resilience in smart city deployments—while enabling the efficiency and inclusion benefits of digital transformation.

𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐑𝐞𝐩𝐨𝐫𝐭
+++ 𝑰𝒏𝒕𝒆𝒓𝒐𝒑𝒆𝒓𝒂𝒃𝒊𝒍𝒊𝒕𝒚 𝒂𝒔 𝑰𝒏𝒇𝒓𝒂𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆 – Why open standards, data portability, and cross-border coordination are essential to prevent vendor lock-in and enable seamless service delivery.
+++ 𝑹𝒆𝒔𝒊𝒍𝒊𝒆𝒏𝒄𝒆 𝒃𝒚 𝑫𝒆𝒔𝒊𝒈𝒏 – How AI systems supporting critical urban functions must include fallback modes, incident reporting, and graceful degradation to prevent cascading failures.
+++ 𝑷𝒓𝒊𝒗𝒂𝒄𝒚 𝒊𝒏 𝑴𝒖𝒍𝒕𝒊-𝑺𝒆𝒏𝒔𝒐𝒓 𝑬𝒏𝒗𝒊𝒓𝒐𝒏𝒎𝒆𝒏𝒕𝒔 – Why extensive sensor networks and data fusion require privacy-by-design and explicit safeguards for marginalised communities.

🙏 Special thanks to our contributors:
• Sean Musch, AI & Partners
• Michael Charles Borrelli, AI & Partners
• Dr. Don Liyanage
• Dr. Benedikt Kohn
• Harvey Castro, MD, MBA.
• Lisa Ventura MBE FCIIS MBCS
• Helen Yu
• Mariia Shcherbakova 
• Patrick Orsos 
• Shreyas Karanjkar
• Edward Cole 
• Neil Oschlag-Michael 
• Mitko Karushkov 
• Vibhav Mithal, FHCA
• Mateo Jarrin Cuvi
• Michael Boevink 布 纷 奇 , 마이클 보빈크, مايكل بوفينك
• Ina Schöne
• Richard Chiumento 
• Carolyn Harrison
• And the many global experts and partners shaping responsible, human-centric AI governance

—

💬 For policymakers, public authorities, and system designers: Where do you see the hardest trade-off today?

#Law #Policy #TrustworthyAI #government #publicservices

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