In this post I am about to potentially save you millions or years or both.
Because I’m going to give you the best advice I know to avoid being taken for a ride.
The fact that AI is exploding ias a more advanced way of doing medical science has a good thing and a bad thing.
The good is obvious… at least if you’re on this list.
The bad thing is that the list of mockselleres just keeps growing and growing.
And it will continue to do so.
If someone talks to you about machine learning, neural networks, analytics… it is increasingly difficult to distinguish the wheat from the chaff.
That’s why, in the end, I recommend ignoring:
– Whatever the website of the day says
– Financial stuff like “they raised a hundred billion euros”
– High-flown names of big tech companies (who know as much about health as I know about marine biology)
– Winners of competitions in accelerators, incubators, greenhouses and botanical gardens
– Medical research companies that have spent 20 years writing things down in notebooks (digital or not) and now it turns out that they are more modern than Tesla
It’s ALL tall tales until proven otherwise.
I’m telling you this from the experience of being in the digital health startup circus for almost 10 years.
Pay attention because I’m making it very easy for you.
You have to look at two things and only two things. But you have to look at these two things very carefully:
– Testimonials from users, who say that they used it and it helped them in something specific. Be careful with this, because sometimes testimonials are given for the sake of being nice. They have to talk about specific things. It is not valid “I believe that Bill’s AI technology has great potential and I say this as a gastroenterology specialist at the Hospital of the Seventh Day”
– Clinical publications or at least in the Journal of Medical Informatics (not publications in machine learning journals or anything like that).
A group of medical oncologists led by Dr. Andres Muñoz, who works at one of the top hospitals in the South of Europe, is exhibiting two posters this week at the American Congress of Medical Oncology.
This double paper explains the generation of an AI algorithm that predicts thrombosis and major bleeding in cancer.
It fills an evidence gap where there was nothing.
And it has been created with information from thousands of patients.
And if you go to the Savana website you can see it.
And if you want to do one, let me know
and we will assess whether it is possible or not.
Have a great day.