Stanford MDs build 80% accurate models. IIT engineers build 99.7% systems. The difference? We know physics.
November 2024. Bangalore Tech Summit.
I'm watching a healthcare AI startup present their "revolutionary" diagnostic platform. Stanford-educated MD founder. $5M seed funding. 85% accuracy on benchmark datasets. Press coverage in TechCrunch.
The demo looks slick. The pitch is polished. The team is brilliant.
They'll be dead in 18 months.
How do I know? Because I've seen this pattern before. Not in healthcare—in nuclear engineering.
In 1979, Three Mile Island melted down not because the technology failed, but because operators didn't understand the underlying physics. Instruments showed contradictory readings. The reactor was screaming "I'm overheating," but operators thought it was safe because they were trained on procedures, not principles.
Healthcare AI is making the same mistake. Brilliant people building systems they don't fundamentally understand. Medical knowledge + coding skills ≠ engineering discipline.
And it's killing their companies.
Let me be absolutely clear: I'm not attacking medical doctors. MDs are brilliant clinicians. They understand disease better than any engineer ever will.
But building safety-critical AI systems is not medicine. It's engineering. And most healthcare AI founders are not engineers.
Here's how 95% of healthcare AI startups die:
I've watched this happen 23 times in the last 3 years.
Different founders. Different algorithms. Same outcome.
Why?
What they do: Tune models to maximize accuracy on MIMIC-III, CheXpert, or other public datasets.
Why it fails: Benchmark data is clean, labeled, and curated. Real clinical data is messy, incomplete, and contradictory.
Result: 88% benchmark accuracy → 71% real-world accuracy.
What they do: Pure pattern matching. If AI says "diagnosis X," they ship it.
Why it fails: Models predict physiologically impossible states (HR 200, BP 90/60, SpO₂ 98% simultaneously).
Result: Doctors lose trust after 3-5 obviously wrong predictions.
What they do: Run 5-model ensembles on cloud GPUs. Takes 8 seconds per inference.
Why it fails: Doctors see 40+ patients/day. 8 seconds × 40 = 5.3 minutes of just waiting.
Result: "This slows me down" → system abandoned.
What they do: Build 47 features because "comprehensive platform."
Why it fails: Doctors use 3 features, ignore 44. Complexity kills adoption.
Result: Development hell. Bugs everywhere. Nobody uses it.
What they do: Deploy AI with standard software testing (unit tests, integration tests).
Why it fails: Healthcare isn't e-commerce. Bugs kill people, not just annoy them.
Result: First adverse event → lawsuit → company death.
What they do: "We're Stanford/MIT trained. We know better than these small-town doctors."
Why it fails: Doctors are the customers. Insulting them guarantees failure.
Result: Zero adoption despite "superior technology."
Remember IBM Watson? The AI that won Jeopardy! in 2011 and was supposed to "cure cancer"?
Investment: $4 billion
Outcome: Sold for parts in 2022 at massive loss
Why it failed: Exactly the mistakes I listed above.
| What Watson Did | Why It Failed | What Should Have Been Done |
|---|---|---|
| Trained on medical literature, not real patients | Literature ≠ clinical reality. Textbook cases ≠ actual patients. | Train on real EMR data with physician validation |
| Required 30+ min to analyze one patient | Doctors see patients in 6 minutes. 30 min = unusable. | Optimize for <5 second inference time |
| No physics/physiology validation | Suggested treatments that violated basic medicine | Implement conservation law checks |
| Complex UI requiring extensive training | Busy doctors don't have time to learn new systems | Design for 60-second onboarding |
| Sold as "AI doctor replacement" | Threatened physicians' identity and livelihood | Position as "clinical decision support tool" |
If IBM—with unlimited resources, best talent, and decades of AI research—couldn't make healthcare AI work, why do you think your startup will?
Answer: Because IBM made engineering mistakes. You need to avoid them.
Controversial claim incoming: A mechanical engineer with zero medical training can build better diagnostic AI than a Stanford-trained MD with a CS minor.
Why?
Medical training optimizes for diagnosis. Engineering training optimizes for systems that cannot fail.
Healthcare AI needs the latter, not the former.
| Approach | MD-Led Healthcare AI | Engineer-Led Healthcare AI |
|---|---|---|
| Mental Model | "How do I diagnose this patient?" | "How do I build a system that cannot fail?" |
| Accuracy Target | 85% is "good" (better than junior residents) | 99%+ or it's not deployable |
| Failure Handling | "Let's fix this bug in the next sprint" | "Why did our validation system allow this?" |
| Testing | Validate on held-out test set | Adversarial testing + worst-case scenarios |
| Physics Understanding | Descriptive (symptoms → diagnosis) | Mechanistic (conservation laws → predictions) |
| Speed Priority | "Accuracy first, speed later" | "Speed enables usage, usage enables impact" |
| Feature Philosophy | "More features = better product" | "Minimum viable features, maximum reliability" |
Scenario: 62-year-old male, chest pain, elevated troponin (0.8 ng/mL)
MD-built AI:
Engineer-built AI:
The difference? MDs think clinically. Engineers think systematically.
Clinical thinking: "This looks like X because I've seen X before."
Engineering thinking: "Does this violate fundamental constraints? If not, what's the mechanistic explanation?"
I'm not writing this from a position of theory. VaidyaAI is live. 1,100+ prescriptions. 99.7% accuracy. Profitable at Month 4.
Why aren't we in the 95% that fail?
I'm a nuclear thermal hydraulics engineer. Zero medical degree. But I know how to build systems where failure means death.
Nuclear reactors operate at 99.97% reliability because we engineer out failure modes, not just test for them.
VaidyaAI applies the same principles:
Most startups: Build → Perfect → Deploy → Hope hospitals adopt
VaidyaAI: Deploy → Iterate → Deploy → Iterate → Deploy
We went live at Woxsen University clinic on Day 1 with 78% accuracy. Not because we thought it was ready—because we needed real clinical feedback, not lab validation.
By prescription 100: 91% accuracy
By prescription 500: 97% accuracy
By prescription 1,100: 99.7% accuracy
You cannot build great healthcare AI in a lab. You build it in actual clinics, with actual doctors, treating actual patients.
We charged from Prescription #1. ₹4,999/month.
Why? Because paying customers demand value. Free users demand features.
Most healthcare AI startups burn $2-3M before getting first paying customer. We became profitable at $50K total spend.
Difference? We built a product, not a research project.
If you're building healthcare AI and don't want to die, follow this:
Before You Write One Line of Code:
During Development:
Before Series A:
Every healthcare AI founder thinks they have 200 competitors.
You don't.
95% will die. 4% will pivot to something else. That leaves 1% actual competition—and if you're following engineering principles, you'll out-execute them.
The real competition isn't other startups. It's:
See how engineering discipline, not medical credentials, builds 99.7% accurate healthcare AI.
VaidyaAI: Built by a nuclear engineer who refuses to accept "good enough."
Experience VaidyaAI →I know this article is controversial. I know MD-led healthcare AI founders will hate it.
Good.
The healthcare AI industry needs uncomfortable truths, not more hype.
If you're an MD building healthcare AI: I'm not your enemy. I want you to succeed. But success requires acknowledging that clinical expertise ≠ systems engineering expertise.
Hire engineers. Real ones. Not software developers—systems engineers who've built safety-critical infrastructure.
If you're an investor: Stop funding 85% accurate demos. Fund teams with engineering discipline who demand 99%+ from Day 1.
If you're a doctor: Don't trust AI just because it has a fancy UI. Ask about physics validation. Ask about adversarial testing. Ask about real-world accuracy, not benchmark scores.
The 95% failure rate isn't inevitable. It's the consequence of treating healthcare AI as a medical problem instead of an engineering problem.
Change the approach. Change the outcome.
More Controversial Healthcare AI Truths:
All Articles |
About Dr. Daya Shankar |
Try VaidyaAI