Beyond Just Finding Papers: Research with AI Today

Mario Mahecha, Renata Tarraf Fernandes, Santiago Guzman

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We have already talked about how AI makes literature searches in neuroradiology quicker and often more insightful. But there is a lot more to it. AI is now touching nearly every step of research, from coming up with ideas to digging into data, writing paper, and even exploring references. Here is how you can actually use these tools.

1. Turning paper networks into good questions

These tools help you develop sharper, more meaningful research questions by showing how papers connect and where gaps or debates exist.

ResearchRabbit and Connected Papers let you upload a key paper (PDF or DOI) and generate interactive networks of related studies. Clicking around the nodes, reading abstracts, and spotting clusters often reveal areas that haven’t been deeply explored, which can shape your next project.

Scite.ai lets you enter a topic or paper to see who cites it, labeling citations as supporting, contrasting, or just mentioning. Filtering by type makes it easy to spot where the field agrees, disagrees, or where debates might give you a new angle.

2. Digging into data and finding patterns

The aim is to spend less time manually cleaning and testing data, and more time understanding what it reveals, trends, relationships, or unexpected outliers.

With Pandas, matplotlib, and scikit-learn, you write short Python scripts to handle your data directly: df.dropna() to remove missing values, plt.hist() or sns.boxplot() to explore distributions, and model.fit() for regressions or classifiers.

DataRobot and H2O.ai take this further by letting you upload a CSV file, then automatically test multiple machine learning models, rank them by accuracy, and suggest the best features. They even generate Python scripts you can download and replicate locally.

Sweetviz and AutoViz are quick ways to scan your data. After installing with pip, running sweetviz.analyze(df) creates full HTML reports with distributions, relationships, and alerts for anomalies, helping you quickly diagnose and explore your dataset.

3. Writing it all up

AI tools make it easier to produce clearer, more structured drafts, so you can focus on your findings instead of getting stuck rewriting awkward sections.

With ChatGPT, Claude, or Gemini, you paste bullet points or rough drafts into the chat and ask for a formal introduction or a cleaner results section. They generate structured, clearer versions you can refine.

Grammarly and WordTune help by analyzing your text in their editors (or through plugins in Google Docs or Word), highlighting grammar issues and suggesting rephrasing or more concise alternatives.

4. Smarter ways to handle references

It’s not just about formatting citations correctly, these tools help you uncover papers you might have missed and see how the literature groups together.

Zotero and Mendeley let you drag PDFs or DOIs into your library. They automatically pull metadata and recommend related articles based on what you already have, broadening your perspective beyond your initial search.

EndNote goes a step further by scanning your manuscript and suggesting papers to cite based on your text. It can also group articles by topic or citation frequency, highlighting foundational or overlooked work.

5. Keep your human filter on

Even with these efficiencies, it’s crucial to make sure your work remains rigorous and accurate. Always manually verify references, some might not exist. Inspect plots and automated analyses to confirm they actually make sense in the context of your research.