Causal multi-modal AI for personalized chemosensitivity prediction
Quick summary
arXiv:2609.13567v1 Announce Type: new Abstract: Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scores as a proxy for treatment benefit, which may contribute to the overprescription of chemotherapy. Here we present a causal multi-modal AI model that predicts personalized chemosensitivity using routinely collected pathology and clinical information. We developed our model on a multi-national dataset of 9,141 patients (twelve cohorts, nine countries) and evaluated it on another 1,994 patients (fiv
Key takeaways
- arXiv:2609.13567v1 Announce Type: new Abstract: Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who.
- Current guidelines rely on recurrence scores as a proxy for treatment benefit, which may contribute to the overprescription of chemotherapy.
- Here we present a causal multi-modal AI model that predicts personalized chemosensitivity using routinely collected pathology and clinical information.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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