ABOUT THE GUEST
This episode features an interview with Navid Alipour, founder and CEO of AI Med Global. AI Med Global is focused on Artificial Intelligence Technology that improves healthcare and helps save lives. It’s made up of two companies Navid co-founded: CureMetrix and CureMatch.
Navid is a long-time entrepreneur in the AI space with an emphasis on combining AI and the life sciences, known as Wellness Science. He seeks to identify scientists and domain experts that solve massive pain problems to take to market by building brand new companies.
In this episode, Navid describes AI’s role in medical imaging to detect cancer with more certainty, and using AI in precision medicine to match patients to personalized cancer therapies. Navid also talks about responsible use of data and how doctors can save valuable time with new technologies.
“We can never have enough data. And there’s that saying that data is the 21st century oil.”
“It’s important to train your data sets, because at the end of the day, artificial intelligence–as much as some will say the robots are going to take over the world–we have a ways to go before that happens. You still have to train it. Machine learning is: you feed it the data, you clean it, you process it, and that’s why we say garbage in, garbage out. You have to clean the data and collect it in an elegant manner to then train the algorithms to detect what you wanted to detect… You still have to train it to specifically detect what you want to detect, recommend, predict, or forecast.”
“If a doctor wants to recommend a three drug combination, there’s literally over four and a half million combinations. So it’s beyond human cognition to process that. That’s what we do, based on the person’s lab work, their next generation sequencing lab work. That’s our input. And we’ll say, out of the millions of combinations, here’s the recommended three drug, two drug, one drug combination.”
“We have data, we have papers that have been published and we can show definitively that the recommendations we made helped, as oncologists say, increase the progression free survival or the overall survival… That speaks volumes to be able to show that we can detect the cancer, we can detect the heart disease, and of course you get them on medication earlier, that reduces the risk of a cardiac event, a heart attack.”
“AI’s not going to replace the doctor, but the doctor using AI will replace the doctor that is not.”
“No two snowflakes ever look the same. No two cancers molecularly are ever, ever the same. So we define cancer by the part of the body, the organ it’s found in. But really it should be defined by the molecular makeup. And no two will ever be the same. So if no two are ever the same, how can you train a machine learning algorithm? You can’t. And so that’s where machine learning will never be the AI tool that’ll get the best treatment recommendation for that specific patient.”
“I’ll say that lab work of that cancer, it’s like the 23 And Me of that cancer. And so we’ll say, based on this person’s specific cancer, there’s no other kind in the world. And all the drugs available, the algorithm will match. Hence why we named it Cure Match. And we’ll say, here’s a recommended combination. It’s for that person. It’s true precision medicine.”
“But I’ve heard of examples of a doctor having ChatGPT write a prescription or send a letter to insurance in 90 seconds instead of 30 minutes, right? So if you can give that time back to the doctor at the primary care level to then take care of their patients, which is their true passion, not filling out paperwork in clicks and clicks and clicks. That’s huge. That time is priceless.”
“Doctors see this and they see the results. And the fact is that an AI algorithm doesn’t need a coffee break. It doesn’t get distracted. It doesn’t have a food coma after lunch. It doesn’t get tired. And so that’s where you marry the HI, the human intelligence, and the AI, the artificial intelligence, and that’s gonna help deliver better care. We have a shortage of doctors, by the way. I wouldn’t be concerned about doctors losing their jobs. They’re going to use this technology to do their job better and more efficiently.”
[00:16] Navid’s background
[02:14] Data’s role today
[03:13] Why it’s important to train data sets
[05:08] How can we trust AI technologies?
[06:34] Using AI for precision medicine
[08:54] Data frameworks
[11:12] How Navid uses augmentative AI
[15:17] How Navid uses generative AI
[18:07] Measuring health and quality of data
[19:26] Regulatory confines affecting healthcare AI
[21:44] Main advantages of AI
[22:59] Building tech literacy
[25:29] Navid’s challenges as CEO
[26:51] How to build a great team
[28:26] The best advice Navid’s received
[30:21] Navid’s advice for people in his field
Connect with Navid on LinkedIn
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