This week at ASCO, we will present clinical validation pf prognostic predictive capabilities of Ataraxis Breast: recurrence risk, adjuvant chemotherapy benefit, and neoadjuvant therapy response. This is also the first data from Phase III RCTs (and certainly not the last!)

This will be the main way we build predictive tools at Ataraxis. Tau enables learning treatment response from real-world data and then predicting its benefit on individual basis, so you don't have to rely on just classifying patients as low/high risk

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.

The Providence team has been doing fantastic AI pathology work for many years now. If you liked this, at @Ataraxis_AI we've built tools for breast cancer that are used every day to predict treatment response and distinguish low/high risk tumors.

Most prognostic/predictive tools in breast cancer were built 20 y ago, never improved, and now limit the clinical utility. We don’t need to rely on modestly prognostic tests restricted to postmenopausal eBC ER HER2- IDC anymore. Can't wait to share our AI tools results #SABCS25

Ataraxis will continue to show evidence that our AI-native tools for oncology are not just more accurate than the standard of care, but that we can answer the most difficult problems in treatment selection

Grok 4 (Heavy and base) has been a disappointment--mostly the product, not model. Somehow it fails to maintain context even after 2-3 messages, fails to work consistently with files, randomly decides to searches the web even with the option disabled.. o3(-pro) still wins so far.

Congrats to our team!! What a week 😎

We are at #ASCO25 this weekend! @JanWitowski will be presenting the results from our Ataraxis Breast clinical validation study--stop by to learn how our multi-modal AI and foundation models help personalize breast cancer treatment.

We are looking for a biostatistician to join us: jobs.ashbyhq.com/ataraxis-ai… This an amazing position for someone with strong skills in biostatistics, interested in optimizing cancer treatment with AI. Please help me spread the word!

Big personal update! As of 3/1, I’ve joined @Ataraxis_AI — a startup I co-founded in 2022 — full-time as Chief Scientific Officer. We recently raised a $20.4M Series A, and are now building a frontier AI lab to push the boundaries of personalized oncology treatment. 1/

Ataraxis has a really unique combination of world-class AI researchers and medical/commercial leaders who brought to market new products used by millions of patients every year. We have only a few more open roles left:

We raised a $20.4M Series A for Ataraxis AI, the frontier AI lab for precision medicine. We're accelerating the development of Ataraxis Breast and other tools to transform cancer. If you want join the fastest-growing startup in the space, we're hiring: jobs.ashbyhq.com/ataraxis-ai

I will be at the San Antonio Breast Cancer Symposium #SABCS24 next week sharing amazing clinical validation results of Ataraxis Breast--the first AI-native prognostic/predictive test for breast cancer patients

We are coming to NeurIPS! @joecappadona will present results of our novel approach to training foundation models for digital pathology. This research goes beyond simply scaling model/data size, and led us to develop Kestrel, the model behind Ataraxis Breast. Come see us!

We are hiring at Ataraxis! Jobs: jobs.ashbyhq.com/ataraxis-ai The mission: apply advanced AI to one of the hardest problems in healthcare—optimizing treatment and predicting patient outcomes Looking for problem-solvers across all roles who want to build something meaningful

“If you don’t benefit from chemotherapy, you want to avoid it.” Our CEO @JanWitowski shared with Forbes' @TheAlexKnapp how Ataraxis Breast enables providers to recommend more precise, effective treatments—helping patients avoid unnecessary chemotherapy—and more.

It’s time to use AI to help cancer patients get truly personalized treatment! To solve this, I founded @Ataraxis_AI with @kjgeras. Ataraxis is an AI precision medicine company building a new era of tools for better treatment selection, starting with Ataraxis Breast:

I-JEPA: Efficient method for Self-Supervised Learning of image features. No need for data augmentation, just masking. Joint embedding predictive architecture, not generative. And it's open source, of course. Blog: ai.facebook.com/blog/yann-le… Paper: arxiv.org/abs/2301.08243 Code & models: github.com/facebookresearch/…

SAM: Segment Anything Model from FAIR. Foundation model for image segmentation. Demo: segment-anything.com/demo Blog: ai.facebook.com/blog/segment… Paper: ai.facebook.com/research/pub… Code: github.com/facebookresearch/… Dataset: SA-1B , 11 million image, 1 billion masks ai.facebook.com/datasets/seg…