MedAI Verdict
Research tools

Medical Research

ResearchRabbit

by ResearchRabbit

Last verified

Finds related papers from seed articles and shows them as citation and similarity maps.

At a glance

Pricing
from USD 12.5/mo
HIPAA
Not disclosed
SOC 2
Not disclosed
EHRs
—
Founded
—
On this page

Independent score  ·  By our public rubric

50/100Solid choice
How it’s computed →
  • Regulatory & Compliance
    0/11

    No FDA clearance listed

  • Clinical Integration
    0/7.8

    No EHR integrations listed

  • Evidence Strength
    27/27

    5 peer-reviewed papers

  • Vendor & Market
    6/18

    Early-stage company

  • Sentiment & Transparency
    6.5/15.5

    Prices listed publicly

▸ Show all 11 dimensions

Regulatory & Compliance

  • FDA clearance0/6

    No FDA clearance listed

  • HIPAA / SOC2 / BAA0/5

    No public HIPAA/SOC2/BAA attestation

Clinical Integration

  • EHR integrations (count)0/4

    No EHR integrations listed

  • Top-3 EHR coverage (Epic / Oracle / Athena)0/2

    None of the top-3 EHRs covered

  • Bidirectional write-back0/1

    No bidirectional write-back documented

Evidence Strength

  • Peer-reviewed papers21/21

    5 peer-reviewed papers

  • RCT / meta-analysis / systematic review6/6

    3 RCT/Meta-Analysis/Systematic Review

Vendor & Market

  • Funding & adoption signal6/12

    Early-stage company

  • Years in market0/6

    Founded year not recorded

Sentiment & Transparency

  • Clinician sentiment (Reddit)0/9

    No clinician sentiment data available

  • Pricing transparency7/7

    Prices listed publicly

Scored October 15, 2026 · Public scoring method v1.1.0

Bottom line

Finds related papers from seed articles and shows them as citation and similarity maps.

Pricing from from USD 12.5/mo.

Editorial review  ·  By MedAI Verdict

Bottom line

ResearchRabbit helps you build a collection of papers by starting from a few you trust, then suggests related work through citations, references, authors and semantic similarity. A free plan exists, and a paid ResearchRabbit+ tier arrived in late 2025. It suits a researcher doing a narrow literature search who wants alerts and maps.

What changed in 2025?

ResearchRabbit launched paid tiers and revamped the product in late 2025. Librarian Aaron Tay called the update the biggest in years, with a cleaner iterative flow and a configurable citation map where axes and node size can be set. Litmaps is connected to the company. PitchBook says Litmaps acquired ResearchRabbit on May 8, 2025. Tay says the October 2025 announcement described a partnership and cites reports of an acquisition without confirming one. Founding year, headquarters and funding are not documented.

What does it do?

You add seed papers and the tool returns related work. The vendor says more than 1 million researchers use it and that it covers over 310 million academic papers, neither independently verified. Collections can be shared. The homepage describes the tool as analyzing "papers you love" and finding "the research you've been missing." Paid plans add Signals alerts that the vendor says protect research integrity, and the Institution plan adds LibKey integration, user management and usage statistics.

What do the plans cost?

As of October 2026 the free plan is listed at $0 and allows up to 50 seed articles, unlimited searches and an unlimited library. ResearchRabbit+ is $10 per month on an annual plan or $12.50 on a monthly plan, with 300 seed articles, advanced search controls, multiple projects and Signals alerts. Country discount codes are available. Institution pricing is quote only. The pricing page shows no trial or refund terms.

Where does it fall short?

Aaron Tay's November 2025 review found free accounts limited to fewer than 50 input papers and fewer than 5 authors with one project, advanced filters for keywords, date, journal quartile, h-index and retraction exclusion behind the paywall, and two-way Zotero sync missing after the revamp. He called the interface not as intuitive as it could be. The free limits he described differ slightly from today's pricing page, which allows up to 50 seed articles, and one secondary source says ResearchRabbit+ restores Zotero sync. A researcher who depends on that sync should test it before paying. Homepage testimonials are curated by the vendor.

What does the evidence say?

No peer-reviewed study evaluates ResearchRabbit. Systematic reviews use it as a search aid, among them a 2026 International Journal of Clinical Pharmacy review of prescribing interventions in the Middle East and North Africa, a 2025 ecancermedicalscience review of cancer stem cells and a 2024 Acta Oto-Laryngologica meta-analysis of thyroid disease and hearing loss, which names it explicitly. A 2026 Antioxidants & Redox Signaling overview covers literature-search tools generally.

How does it compare?

Connected Papers draws a graph from one origin paper, and its pricing page lists five free graphs a month. Litmaps and Inciteful are the other names in seed-paper discovery. ResearchRabbit's own pitch is a persistent library built from papers you add.

Who is it for?

A PhD student keeping a rolling library for a thesis benefits from the alerts. A clinician-researcher assembling a reading list before a grant application can stay on the free plan, since 50 seed articles covers a narrow topic. A group that needs user management and usage statistics has to go through the Institution plan.

The verdict

The free plan costs $0 with no time limit, so start there. ResearchRabbit+ costs $10 a month on an annual plan or $12.50 month to month, and it adds 300 seed articles and advanced search controls. Trial and refund terms are not stated on the pricing page.

Editorial review last generated October 15, 2026. Synthesized from clinician sentiment, peer-reviewed coverage, and our editorial silo picks. Refined by hand where vendor facts change.

Pricing

What it costs

Single paid tier at USD 12.5/month per provider.

TierMonthlyAnnualNotes
Free——Up to 50 seed articles, unlimited searches across 310M+ articles, unlimited library and shared collections.
ResearchRabbit+, annual—USD 120Up to 300 seed articles, advanced search controls, multiple projects and Signals alerts. Country discount codes are available.
ResearchRabbit+, monthlyUSD 12.5—Same features as the annual plan.
Institution——Contact for pricing. Adds LibKey integration, user management and usage statistics.

Source: vendor pricing page. Last checked October 15, 2026.

Peer-reviewed coverage

What the literature says

5 peer-reviewed studies indexed on PubMed evaluate ResearchRabbit in clinical contexts. The most relevant are shown below, ranked by editorial relevance score combining title match, study design, recency, and journal tier.

The association between thyroid disease and hearing loss: a meta-analysis.
Gu L, Guo W, Wang X, et al.· Acta Otolaryngol· 2024Meta-Analysis
It has been shown that there is a link between thyroid-related diseases and hearing loss. The purpose of this study is to investigate the relationship between thyroid-related diseases and hearing loss by conducting a meta-analysis. A thorough search was carried out in the following electronic databases: PubMed, Cochrane Library, Embase, Web of Science, Google Scholar, Semantic Scholar, and ResearchRabbit. The chi-square test and theindex examined the research's heterogeneity. A funnel plot and the Eger test were used to examine publication-biased effects. A total of 48,507 individuals (6482 h…
Cancer stem cells and post-therapy tumour recurrence: a systematic review of mechanistic pathways and translational gaps.
Barjij I, Meliani M· Ecancermedicalscience· 2025Systematic Review
Cancer stem cells (CSCs) are increasingly recognised as pivotal drivers of tumour recurrence and treatment resistance across multiple malignancies. Despite extensive preclinical investigations, the mechanisms by which CSCs mediate relapse after therapy remain insufficiently integrated and poorly translated into clinical frameworks. This systematic review aimed to synthesise current mechanistic evidence linking CSC biology to post-therapeutic recurrence in solid and hematologic tumours, highlighting recurrent molecular pathways, experimental models and translational gaps. Following Preferred R…
Multifaceted and educational interventions to improve prescribing indicators in the Middle East and North Africa Region: a systematic review and meta-analysis.
Ilyas M, Chivese T, Hadi MA, et al.· Int J Clin Pharm· 2026Meta-Analysis
Rational prescribing is challenging due to global antibiotic resistance and widespread polypharmacy. Evidence on effective interventions to improve prescribing practices in MENA is limited. This systematic review and meta-analysis evaluated the effectiveness of multifaceted and educational interventions in improving WHO/INRUD prescribing indicators in the Middle East and North Africa (MENA). We searched PubMed, Scopus, and CINHAL up to June 10, 2025, for experimental studies evaluating the effectiveness of multifaceted interventions on WHO/INRUD prescribing indicators. Searches were supplemen…
Artificial Intelligence Tools in Biomedical Research: Part 1-Literature Search and Knowledge Mining.
Sen CK· Antioxid Redox Signal· 2026Editorial
The exponential growth of biomedical literature has rendered traditional search methods inadequate. Artificial intelligence (AI) tools have emerged and are developing as transformative solutions for literature search and knowledge mining. This first article of a series, intended to address different components of biomedical research, provides a comprehensive analysis of recent advancements, practical applications, and challenges in deploying AI for biomedical research. The objective of this work is to synthesize the evolution, capabilities, and limitations of AI-driven tools for literature di…
Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.
Meneses JCBC, Santos Neto ATD, Domingos MAF, et al.· Comput Inform Nurs· 2026
Artificial intelligence, particularly machine learning, has great potential to improve health outcomes, including predicting adverse conditions. In breast cancer, machine learning models can help personalize prevention strategies for radiation-induced cutaneous toxicity. This scoping review aimed to explore machine learning models for predicting radiation dermatitis in women with breast cancer. Data collection was conducted in November 2023 from 7 electronic databases and gray literature, with no restrictions on publication year. Publication selection was supported by the RAYYAN reference man…

See all on PubMed