You have 2 options.

Option 1.

Wait patiently for the brains and bodies of you and everyone you’ve ever loved to deteriorate until it ends in catastrophe.

Option 2.

Give everyone a free robot doctor that can automate and radically accelerate clinical research.

Here’s why

2 Billion People are SUFFERING

from chronic diseases like depression, fibromyalgia, Crone’s disease, and multiple sclerosis. There are over 7000 diseases that we still don’t have cures for.

Since the 1990’s, we’ve seen a continual rise in the incidence Chronic

Diseases

These have been strongly linked to the with increase in the quantity of pesticides and

chemicals in your food

But we only have long-term toxicology data on 2 of the over 7000 preservatives, flavorings, emulsifiers, sweeteners, pesticides, contaminants, and herbicides.

The

Good News!

There could be billions of cures we don’t even know about yet!

There are over 166 billion possible medicinal molecules, and we’ve only tested 0.00001% so far.

Unfortunately, Clinical Research is SLOW, EXPENSIVE, and IMPRECISE

It costs about $2.6 billion and takes about 12 years to bring a new drug to market. We only approve around 30 drugs a year. It would take over 350 years to find all the cures at this rate! So, you’ll be long dead by then.

The cost of clinical research has grown exponentially since Congress passed the 1962 Kefauver-Harris Drug Amendments.

It costs $48k per patient in a Phase III clinical trial. Trials often spend over $30 million simply recruiting patients.

80% of Clinical Trials Fail Due to Insufficient Participants

Yet

Less than 1% of People with Chronic Diseases Participate in Clinical Research

So what’s the solution?

Should you just wait patiently for the sweet release of death?

No!

We Can Defeat Chronic Disease

With the Power of
Robots!

Some robots can invent new drugs!

And some robots can actually make drugs!

But imagine if we could automate clinical research!

The cost savings from automating clinical research could make the next part of that chart look like this:

How it Works

1. Automated Data Collection

Import from Wearables and Apps

It should be effortless for patients to import their data from lots of apps and wearable devices like physical activity, sleep, environmental factors, and vital signs.

Browser AI Agents

Patients should be able to use browser-based autonomous AI agents to collect their diet data from services like Instacart, supplement purchases from Amazon, CVS prescriptions, Quest lab results, etc.

Manual Data Collection

Patients should be able to easily record any symptom severities, foods, treatments, or anything in a simple reminder inbox.

Data from Speech

Patients who prefer talking over messing around with apps should be able to talk to the FDAi. This would also allow passive inference of cognitive and emotional data.

Images to Data

Patients should be able to easily record any symptom severities, foods, treatments, or anything in a simple reminder inbox.

Notifications

Patients should be able to use web and mobile notifications with action buttons can be used to track symptoms and factors in a fraction of a second.

2. Automated Data

Analysis

After the FDAi gets a few months of data, it should provide patients with Root Cause Analyses for their conditions, telling them how much different medications, supplements, or foods might improve or worsen their symptoms:

Automated Causal

Inference

As any obnoxious college graduate will tell you, correlation does not necessarily imply causation.

Just because you took a drug and got better, it doesn’t mean that’s really why your symptoms went away. Even in randomized controlled trials hundreds of other things are changing in your life and diet.

So, our FDAi must apply Hill’s 6 Criteria for Causality to infer if something causes a symptom to worsen or improve instead of just seeing what correlates with the change.

Personal

Studies

For instance, when gluten-sensitive people eat delicious gluten, it usually takes about a 2-day onset delay before they start having symptoms. Then, when they stop eating it, there’s usually a 10-day duration of action before their gut heals and their symptoms improve.

Onset Delays

One example of causal inference involves applying forward and reverse lagging of the depression and exercise data. The result suggests a causal relationship based on the temporal precedence of physical activity.

Discovering

Cumulative Effects

The FDAi should also compare the outcome over various durations following the exposure to see if there is a long-term cumulative effect or if it’s just a short-term acute effect. Acute effects are probably obvious already. This analysis suggests that the mood benefits of regular exercise may continue to accumulate after at least a month of above-average exercise.

Real-Time Decision Support

This data will then make it possible to provide real time decision support to tell us the most important we can do at any given time to treat or prevent disease.

3. Effortless Trial

Participation

through automated:

1. Trial Search

If you’re one of the inadequately treated 2 billion chronically ill and nothing else has worked for you, it should find you the most promising new treatment available.

2. Trial Enrollment

If you want to participate, it should automatically enroll you and get the treatment shipped to you or whatever.

3. Data Collection

The FDAi should automatically schedule all the tests and collect all the data to determine the safety and efficacy of the treatment.

4. Data Analysis and Publishing

The FDAi should then analyze and anonymize the data and publish the results for everyone.

Citizen Science

Anyone can create a study, become a prestigious scientist, get a link, and invite all their friends to join!

Create Study

Global Scale Studies

should be published in a Wikipedia for clinical research based on everyone’s data listing the likely effects of every food and drug.

Current Studies

Mega-Studies

allow anyone to look up their condition and see how different foods, drugs and supplements tend to improve or worsen their condition for the average person.

Check out a Mega-Study