Just in the last few days, we've seen:
- A guy in Australian using AI to design a custom mRNA vaccine
- Microsoft+Providence building AI models predicting spatial proteomics from pathology slides
We are so close to making cancer fully, truly personalized. Most patients would be surprised how much more we can do to select the right treatment.
Even in cancer, drugs are usually given on a basis of "well, it works on average with patients of your [age/tumor size/other general characteristic]", instead of predicting if someone will benefit from the drug or not.
This problem requires superhuman skills, which is why the medical world hasn't solved it yet.
With AI, we are reaching the point where you can (1) predict response to all available cancer therapies, (2) do it on an individual patient level, not population level.
What you're seeing in the news with these examples are still mostly research applications. Productizing this and bringing it to the clinic requires clinical validation and regulatory approvals. Good news is that there are already first tools used being used.
Our AI research lab @Ataraxis_AI has built tools like that for breast cancer and they are actually used by oncologists and other physicians. Similar to the Microsoft/Providence story, these tools use very standard, widely available information, primarily pathology slide imaging.
This shift takes more time than we'd all like, because new technology, whether it's a new drug, medical device, or an AI tool, requires a lot of physician and patient trust. Luckily, just like in other areas of AI applications, it has been accelerating. You'll start seeing a lot of this for all cancers, and ultimately, all diseases.
Séb Krier@sebkrier
This is wild. theaustralian.com.au/business/techn…
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