AI Research Abstract & Paper Structure Assistant
Supercharge research workflows: automatically organize abstracts, paper structures, and key takeaways for submissions and briefings.
AI Text Tools
Project Details
Separate multiple formats with commas, semicolons, or line breaks.
Research Copy
The AI-generated research copy will appear here once ready.
What you can do with this tool
- - Draft a structured abstract (problem → method → results → conclusion).
- - Generate a paper outline with sections, bullets, and logical flow.
- - Rewrite key contributions in clearer, submission-friendly language.
- - Tailor phrasing for a target venue or audience (reviewers, practitioners).
Example input & output
Example input
- Research Topic & Keywords
- Federated learning for privacy-preserving medical imaging diagnostics (MRI).
- Problem Statement & Motivation
- Centralized training requires sharing sensitive patient data; hospitals need strong privacy while maintaining model accuracy.
- Methodology & Experimental Setup
- We train a CNN across 6 hospitals using federated averaging with differential privacy. Baselines: centralized, local-only, vanilla FedAvg.
- Experimental Data & Metrics
- AUC improved from 0.86 (vanilla FedAvg) to 0.90 (DP-FedAvg) with ε=3.0; communication rounds reduced by 20%.
- Key Findings & Contributions
- Privacy-preserving FL matches centralized accuracy on MRI classification while reducing data-sharing risk.
- Implications & Future Work
- Enables cross-hospital collaboration for imaging diagnostics; future work includes robustness to non-IID data.
- Target Venue / Audience
- Medical Imaging / ML conference reviewers.
- Deliverable Formats
- Structured abstract + paper outline.
- Preferred Tone
- Academic & concise
Example output
Structured abstract (example)
Background: Training medical imaging models often requires aggregating sensitive patient data across institutions.
Objective: Enable cross-hospital collaboration while preserving privacy without sacrificing diagnostic performance.
Methods: We apply federated learning across six hospitals using federated averaging with differential privacy. We compare against centralized training, local-only models, and vanilla FedAvg.
Results: Our DP‑FedAvg approach improves AUC from 0.86 to 0.90 at ε=3.0 and reduces communication rounds by 20% relative to vanilla FedAvg.
Conclusion: Privacy-preserving federated training can achieve near-centralized performance for MRI diagnostics while reducing data-sharing risk.
Paper outline (high-level)
- Introduction + motivation
- Related work (FL, DP, medical imaging)
- Method (DP‑FedAvg)
- Experimental setup (sites, datasets, baselines)
- Results + ablations
- Limitations + future work
Examples are illustrative. Your results will vary based on your inputs.
Tips for better results
- - Include representative experiment data (tables or headline metrics) to keep the abstract evidence-based.
- - Clarify the target audience (journal reviewers, executives, investors) so the AI calibrates tone and detail.
- - Specify any language preferences within the inputs if you need the copy produced in a different language.
- - List the desired formats (abstract, outline, conclusion, keywords, citations) to generate tailored sections in one pass; add draft text under 'Existing Draft Notes' for refinement.
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