It built a rare disease diagnostic system that beats human state of the art
Nobody gave it data. Nobody gave it tools. Nobody wrote a line of code. 12 hours later, the AI system it delivered beat a system published in the main Nature journal on public benchmarks.
From zero to delivery
0
Data given to it
0
Human intervention
What it set out to challenge
A system a human team built over an entire research cycle
Rare disease diagnosis is one of the hardest problems in medicine: more than 7,000 rare diseases are known worldwide, each one vanishingly rare, and patients typically spend years bouncing between hospitals before a diagnosis is confirmed. In 2026, a human research team published their multi-agent AI diagnostic system in the main Nature journal.
Human state of the art · human SOTA
DeepRare
A multi-agent rare disease diagnostic system published in Nature, Vol 651 (2026).
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Architecture designed by a team of human experts
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Went through a full research and publication cycle
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Beat every prior method on the benchmarks
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Outperformed senior clinicians on some datasets
Our system · built autonomously by AI
Apodex Discovery
One sentence of task description. Zero data. Zero human intervention. All of it done by the AI itself.
✓
Found its own data online, cleaned it, standardized it
✓
Built its own knowledge base and retrieval engine
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Wrote its own tools, designed its own experiments
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Trained its own models, tuned them, and signed off on them
How it pulled this off
In 12 hours, five things happened at once
Every act below is a process that actually took place during this run.
01
It went looking for the cases itself
It started from zero records. It searched public medical databases online, found confirmed-case repositories published by global rare disease research consortia, extracted each patient’s symptom codes, causal genes and final diagnosis one by one, then filtered out duplicates and low-quality samples.
02
It built its own medical knowledge base
Cases alone weren’t enough. It downloaded the entire international-standard Human Phenotype Ontology — a vast network describing the hierarchical relationships between every clinical symptom — then wired in disease-to-symptom tables and gene-to-disease mappings to assemble a knowledge foundation it could reason over.
03
It wrote its own tools, then kept rewriting them
Knowledge sitting on a shelf isn’t usable — you need decent instruments to read it. For instance, it built itself a set of “diagnostic instruments”: starting from symptoms, it follows three lines of evidence in parallel to find candidate diseases, scores every candidate on how well it fits, then trains a ranker to fuse those scores into a confidence ranking. It built more than one toolset like this, and every one of them was torn down and rewritten many times over — each version had to be measured to see whether it genuinely improved, and if it didn’t, it was rolled back.
04
It assembled all of it into a “workbench”
Data, knowledge base and tools were assembled into a complete runtime environment — a workbench where an AI doctor can check and re-check its own work: retrieve the evidence first, verify it against authoritative descriptions, and only then commit to a conclusion. It then ran that pipeline end to end, validated it, and optimized it.
05
It trained the models, tuned the knobs, and picked the winner
It built and optimized its own training set, launched several runs with different hyperparameters in parallel, and then picked the best performer on a held-out set it had reserved for itself. At no point did anyone tell it what configuration to use, how long to train, or which one to pick.
The final deliverable
12 hours later, this is what it handed over
Not a single AI model, but a complete diagnostic system — an AI doctor that knows how to use tools, plus every piece of knowledge and instrumentation it needs to work. The whole thing can be packaged up, taken away, and run and evaluated offline.
AI doctor · model
35B model
Lightweight, deployable on-premise — your patient records never have to leave
What it learned is process
When to look things up, what to look up, and when it is safe to conclude
It does not memorize knowledge
All the medical knowledge lives in the knowledge base
Knowledge base
International-standard symptom ontology
Disease-to-symptom mappings
Gene-disease associations
5,203 confirmed cases
Covering 804 rare diseases
Diagnostic instruments
Multi-path candidate retrieval
Clinical fit scoring
Confidence ranker
Fallback path
No failure anywhere leaves you without an answer
Diagnostic process · can loop back at any step
↺ When evidence is thin or in doubt, it returns to step 2 and searches again
Measured results
On the same benchmarks, it beat human state of the art
Standard benchmarks: 556 real cases it had never seen. The evaluation was re-run by a third party in an isolated environment — the AI could not see the questions and received no feedback. The metric is standard Recall@K — the share of cases where the correct diagnosis appears among the system’s top K candidates.
DeepRare · human state of the art (Nature, 2026)
System built autonomously by AI
Recall@1
Right on the first guess
0.628
0.678
+7.9%
Recall@3
Correct answer in the top 3
0.704
0.790
+12.2%
Recall@5
Correct answer in the top 5
0.730
0.822
+12.6%
Micro-averaged over 556 held-out cases drawn from LIRICAL, MyGene2 and MME. Human state of the art uses the values publicly reported in the DeepRare paper.
What this means for you
Your data never has to leave your own server room
In this run, all Apodex Discovery received was one sentence of task description. It found the data itself from public sources, built the system itself, and the packaged deliverable runs the entire evaluation offline. In other words — from start to finish, we never touched a single piece of private data.
Build phase
All you give is one sentence
Spell out what problem you want solved and what counts as getting it right. Data collection, knowledge base, tools and training are all handled by the system itself. Your data plays no part in training.
Deliverable
The whole system is handed to you
A lightweight model plus every piece of knowledge and instrumentation it needs to work, packaged up and deployed on your own servers. Not an API call — an asset you own.
In use
Works with the network unplugged
Inference needs no internet connection at any point. Patient records, samples, molecular structures, experiment logs — this data moves only inside your own server room, and never a step beyond it.
The public world
Public literature and databases
International standard ontologies
Published cases and experiments
The system learns and builds here
Only the system crosses this line
YOUR DATA BOUNDARY
Your data does not cross
Your server room
The delivered purpose-built system
Your private data
Offline inference, no network
Data only circulates in here ↺
One quiet aside: if you are willing to provide your own private data, the system can do even better with it — but that is a bonus, not a requirement.
What this actually means
This was never about winning at rare disease diagnosis
What was actually proven is this: Apodex Discovery can already run an entire research pipeline on its own — setting the approach, finding the data, building the tools, designing the experiments, training the models, validating itself, and delivering the result. Apodex Discovery is now being applied to biology, clinical medicine, chemistry and materials. And all you ever have to give it is one sentence.
Zero to delivery
0 / 0
Data given · human intervention
vs. human SOTA 62.8%
All figures come from the complete trace log of a single real run and third-party evaluation results. Human state of the art references the values publicly reported in DeepRare (Nature, Vol 651, 2026).