# AI insights

##### Corpus analysis (last 30 days)

Papers analyzed

### 162

With summaries & mining.

Topic clusters

### 8

Identified from keywords.

Citation overlap

### 12

Foundational papers shared.

Quality score

### 87%

Summary coherence (sample).

##### Narrative insight (sample)

"Your corpus is dominated by NLP and transformer-related papers, with a strong secondary focus on healthcare applications. Keyword extraction shows consistent themes: deep learning, BERT, and summarization methods appear across collections.

Citation analysis suggests 12 papers are cited frequently across different collections — these may be foundational references worth reviewing. Topic clustering reveals 8 distinct themes, with the largest clusters around 'transformer architectures' and 'clinical NLP'.

Consider adding more papers from underrepresented domains (e.g. multilingual, low-resource) to balance the corpus. Summary quality scores are highest for extractive summaries on technical papers."

##### Suggested actions

- Review the 12 shared foundational papers for citation graph insights.
- Expand keyword taxonomy for healthcare-specific terms.
- Run abstractive summarization on survey papers for better coverage.

This page is UI-only; in a real setup the AI would analyze your corpus and generate these insights.

##### Topic clusters (sample)

- Transformer architectures: 45 papers
- Clinical NLP: 28 papers
- Summarization methods: 24 papers
- Pre-trained models: 22 papers
- Entity recognition: 18 papers

##### Model feedback

What you might feed back into training.

- Log when users edit AI-generated summaries.
- Track keyword corrections and entity adjustments.
- Collect ratings for summary usefulness per paper type.

All numbers and bullets here are placeholders; this prototype doesn't run any real AI models.