Unified research intake
New publications are collected from PubMed and arXiv, normalized, enriched, and stored in a single searchable dataset.
Automated ingestion
Metadata extraction
OpenAI summaries
Version 2.0 is live
Research analytics that move oncology teams forward
Explore AI-driven cancer diagnostics research in one elegant workspace. Track new papers, surface trends, compare methods, and ask grounded questions against your research corpus.
Papers tracked
1189
+ live indexed research
PubMed share
417
+ clinical evidence
arXiv share
772
+ frontier models
Research momentum
Features
Everything your team needs. Nothing you do not.
The platform combines ingestion, tagging, search, analytics, and evidence-aware AI answers in one interface so researchers and clinicians can move faster.
Semantic exploration
Search by concept instead of just keywords. The site uses embeddings to rank papers by meaning and research relevance.
Vector search
Cancer tags
AI method tags
Evidence-grounded AI workspace
Ask high-level questions and get concise answers based only on retrieved papers already stored in the portal.
RAG workflow
Paper citations
Safe responses
Visual research monitoring
Dashboards show publication trends, leading cancer types, top AI methods, and the newest additions to the corpus.
Trend charts
Recent activity
RSS feed
Total papers
1189
Curated oncology AI papers in the database.
PubMed
417
Clinical and peer-reviewed sources.
arXiv
772
Fast-moving preprint and ML research.
Top categories
5
Leading cancer areas currently indexed.
Top cancer types
- general cancer
- unknown
- breast cancer
- brain tumor
- colorectal cancer
Latest papers
- Molecular alterations prediction in gliomas via an interpretable deep learning model: a multicentre and retrospective study.
- Gut microbiome and metabolic health: mechanisms and precision interventions.
- Real-world efficacy of Bruton tyrosine kinase inhibitor based maintenance therapy in diffuse large B-cell lymphoma: a multicenter retrospective cohort study with external historical control.
- Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.
- Deep learning based on CD3 histological slides for prediction of colon cancer outcome: analysis of three international stage III colon cancer cohorts.