AI guide for science: Key tools for producing novel research papers

João Brainer, a clinical researcher at Einstein, explains the potential and limitations of the AI technologies transforming academic output

Juliana Protásio

A person is working on a laptop with an open spreadsheet displayed, while consulting printed documents and a cell phone on the desk. The scene suggests a typical environment for research, data analysis, or the organization of academic information.

The rise of artificial intelligence (AI) tools designed for academia has pushed the debate beyond whether or not they should be used. “No one is asking whether or not we will use AI anymore; the question now is how to use it ethically,” said neurologist João Brainer, a clinical researcher at Einstein Hospital Israelita and a professor at the Federal University of São Paulo (UNIFESP), at a virtual meeting hosted by Science Arena.

According to Brainer, the biggest challenge faced by scientists today is strategic: understanding which tool is best suited to each stage of the research process and how to get the most out of them without compromising scientific integrity.

To help researchers navigate this rapidly expanding ecosystem, Science Arena compiled a list of the main tools recommended by Brainer, including details of their main functions, strengths, and weaknesses. See below:

CONSENSUS

Link: https://consensus.app/

Key function: Literature searches, focusing on direct, accurate, evidence-based results.

Strengths: The platform has partnered with major scientific publishers (such as Wiley, Sage, ACS, and others). As a result, it can extract data from papers in their entirety, rather than just the abstracts. It also provides the exact page number and a link to the source article, significantly reducing the risk of hallucinations or data manipulation.

Weakness: Because it depends on publishing partnerships, Consensus’s search is limited to specific databases, potentially excluding important titles.

CORE

Link: https://core.ac.uk/

Key function: Indexing open access scientific literature.

Strengths: CORE is a free platform that aggregates data from repositories around the world. It is particularly useful for conducting broad literature searches, focusing on the latest developments in open-access publications.

Weakness: By design, CORE does not search paid-access journals or articles protected by paywalls.

OPEN EVIDENCE

Link: https://www.openevidence.com/

Key function: Searching for high-impact medical literature.

Strengths: The tool is extremely thorough and reliable for the medical and biomedical fields, targeting the databases of established journals, such as The New England Journal of Medicine (NEJM) and The Journal of the American Medical Association (JAMA).

Weakness: By focusing on specialized databases, the scope of the results is limited, potentially excluding important journals or fields.

SCISPACE

Link: https://scispace.com/

Key function: Supporting academic writing with citation optimization and reference management.

Strengths: Authors can select a section of their text and ask the tool for a source within its database that supports the claim. The platform has a direct interface with Zotero and Mendeley, two popular reference management tools, and can automatically format tables and citations in more than 2,600 styles, including Vancouver and AMA. It can also be used to adjust the tone of a text, to make it more persuasive, pragmatic, or conversational, for example.

Weakness: Over-reliance on rewriting features can weaken the text’s originality and the author’s voice.

PERPLEXITY AI

Link: https://www.perplexity.ai/

Key function: Connecting ideas and cross-referencing data from different scientific articles.

Strengths: Perplexity performs advanced methodological correlations by cross-referencing author data and multiple articles, quickly creating complex theoretical overviews.

Weakness: João Brainer issues a strong warning about data privacy. The platform’s integrated browser collects user reading and browsing data in real time. For scientists working with patents, business ideas, or unpublished theses, this can create risks related to information leaks and loss of intellectual property before publication.

Popular Large Language Models (LLMs)

Key function: Preliminary screening, organizing ideas, and refining manuscripts.

Strengths: Each of the most popular large language models (LLMs) offers its own advantage in the research process: Brainer highlights Claude as the most rigorous and refined model for dealing with the density of purely academic texts; Gemini excels at searching for references online and providing links for fact-checking; and ChatGPT is especially useful for identifying trends and gaps in the literature.

Weaknesses: Because they are designed for general purposes, LLMs function based on linguistic probability. This means that when asked to process data without contextual limitations, there is a greater risk of hallucinations. They require detailed and exhaustive input; poorly worded or superficial prompts tend to lead to inaccurate and irrelevant responses.

Number one tip: combine tools and build a PDF database

Brainer’s main practical recommendation is not to expect any single tool to solve every aspect of a research project. It takes some effort to achieve consistent results.

“You can combine the broad searches of PubMed AI CORE with the more in-depth refinement provided by Consensus and the reference management of SciSpace,” says the expert.

“You should also create your own archive of PDFs you have collected,” advises the researcher.

Brainer predicts that in response to the current flood of AI-generated papers, major journals will begin requiring authors to submit the original source files they consulted, both for auditing purposes and to ensure scientific integrity.

Watch the full discussion with João Brainer below:

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