AI-Augmented Oncology: Leveraging Deep Learning for Earlier Cancer Detection
Term Project 1 · full report
Sixteen-page technical report with executive summary, peer-review appendix, and IEEE references. Opens in a reader when you choose to view it.
- Explained why the piece was chosen
I chose it because it is the only piece where I can show my work being wrong and then getting better. The draft went to three classmates for review, and their feedback is documented alongside what I changed in response. Most portfolio pieces show a finished object. This one shows the process that produced it.
- Explained how the piece reflects development as a communicator
A twelve-page technical report on whether AI can help doctors catch cancer earlier than they currently do. It walks through the evidence, a 2026 trial where AI-supported mammogram reading found 29% more cancers, and colonoscopy studies where AI raised detection rates from around 33% to 41%, and then argues for a specific position: that these tools should support radiologists rather than replace them.
What it taught me is that I write long. One reviewer said my sentences in two sections leaned so heavily on semicolons and dashes that the text became heavy to read. She was right, and fixing it did not lose a single technical point, it just made the same argument easier to follow.
- Piece showcases communication skills
Structured written communication at report scale: letter of transmittal, executive summary for readers who stop early, argument body with IEEE references, limitations section for credibility, and Appendix A documenting peer feedback and revision. It demonstrates completeness, coherence, conciseness, and correctness under a formal engineering format.
- Piece was effectively communicated
The report is navigable at scale: generated table of contents, labelled sections, figure captions, and an executive summary for readers who stop early. Peer feedback is documented in Appendix A with the revisions that followed. One reviewer flagged heavy sentence density in two sections; I shortened those without dropping technical content. A reader can follow the full evidence chain or stop after the summary — and the revision trail shows the draft was tested on real readers.

