Oncology AI Research Summary

Summarised evidence on detection sensitivity, false-positive burden and reader workload effects.

Why harmonisation and test-retest stability determine whether a radiomic signature travels.

Concordance with expert panels, and where disagreement clusters by grade group.

Extraction accuracy for stage, histology and treatment intent, with failure modes.

Adoption barriers observed when prediction outputs enter tumour board discussion.

Time saved versus time spent correcting, by organ at risk.

Merck and Moderna announced positive topline results from the Phase 3 INTerpath-001 trial for an individualized mRNA-based cancer therapy in melanoma.

Discover how AI-powered clinical trial matching can help breast cancer patients and oncologists identify potentially relevant clinical trials faster—and understand its benefits and limitations.

A new AI model analyzes routine H&E pathology slides to predict TP53 mutations, RNA expression, tumor type and survival outcomes across 32 cancer types.

GenBio AI's AIDO Cell aims to simulate human cells from DNA and RNA to proteins and whole-cell behavior. Explore what virtual cells could mean for drug…

What if biology could be designed, tested and optimized in much the same way that engineers develop a new technology? That idea sits at the heart…