Discover the newest research about AI innovations in π Oncology.
By tracking entire patient histories instead of clinical snapshots, a new AI model outperforms traditional cancer staging systems across three countries.
A new ensemble AI model predicts positive surgical margins before breast-conserving surgery, but its performance drop in external testing highlights the ongoing struggle with clinical generalization.
A new multi-center model uses basic clinical data to flag brain metastasis before symptoms appear, challenging the need for expensive, complex biomarkers.
A new foundation model uses baseline CT scans to flag which lung cancer patients are likely to develop life-threatening lung inflammation from immunotherapy.
Deploying AI to halve diagnostic wait times is a massive win, but it risks creating a dangerous bottleneck further down the care pathway.
A massive consolidation in cancer diagnostics reveals the steep financial price of dominating the liquid biopsy market.
A new multimodal AI model helps solve the agonizing clinical dilemma of whether breast cancer survivors should endure five extra years of hormone therapy.
A new study shows AI-generated synthetic patients can replicate cancer trial results, potentially shrinking the need for human control groups.
Averaging tumor data has long blinded oncologists to the specific, high-risk cells that drive patient mortality.
A new machine learning model uses simple blood and nutrition markers to predict which uterine cancer patients can safely skip aggressive surgery to preserve their fertility.
