New SciSpace Literature review || SciSpace AI for Systematic Meta-Analysis || Hindi || 2023

Systematic literature review and meta-analysis are getting faster because SciSpace AI can automatically pull relevant papers, generate structured summaries, and help users write sections of a review without manually reading and extracting everything. The workflow described centers on searching with keywords, selecting the most relevant top papers, and then using built-in summarization to produce long-form abstract-style text (hundreds of words) that can be directly used in a research write-up.

After creating a free account (with the option to explore without upgrading to premium), the process starts by signing in and running a query. The interface then returns a small set of highly relevant papers—initially framed as “top five” based on the keyword search. Users can add more papers to expand the set, and each added paper generates “insights” and a “summarize abstract” output. The summary output is positioned as a practical time-saver: instead of manually condensing multiple sources, the tool can condense content into a few lines or longer abstract-like text (the transcript mentions ranges such as 300–500 words and an example around 5200 words being summarized into shorter form).

Beyond summarization, the workflow includes editing and organizing what will be used in the final review. Users can add specific sections such as “add conclusion,” “add contribution,” and “practical implications,” and then remove papers that don’t fit the current focus. A slider-style view helps manage the selected set, and a “remove” option lets users filter out irrelevant papers (including options tied to recency and general/conference categories). The platform also provides access to PDFs when available, plus citation-related information so users can bookmark and later retrieve sources for writing.

The transcript also highlights discovery features that help broaden research beyond the initial search. For any selected paper, the system surfaces “related papers” and “similar papers,” along with citation context such as which authors or works cite the selected item. Clicking into an author or related topic branches into additional papers in the same field, enabling a snowball effect: start with one relevant study, then expand outward through related and citation-linked literature.

A key differentiator is “Pilot” functionality tied to reading and comprehension, especially for math-heavy content. When a PDF is available, Pilot can generate explanations in simpler language for equations and tables, including variable definitions and step-by-step interpretation. If a paper’s PDF isn’t available on the platform, the transcript describes importing or uploading a PDF to enable the same Pilot-based explanation. This is presented as a way to avoid getting stuck on technical sections and to keep the literature review moving.

Finally, the transcript notes that while some features may be paid, core literature review tasks—searching, selecting papers, summarizing, organizing, and using Pilot for comprehension when PDFs are accessible—are available for free during exploration. The overall takeaway is a streamlined end-to-end workflow: search → select → summarize → organize → expand via related/citation links → understand PDFs (including equations) → compile into a systematic review faster than manual methods.