ScholarIQ is an AI-powered literature review and evidence synthesis platform designed to help researchers search, screen, organize, and extract scientific evidence faster while maintaining transparency, rigor, and human oversight.
Built for academics, HEOR teams, CROs, medical affairs professionals, and clinical researchers, ScholarIQ combines AI-assisted workflows with fully traceable evidence review to support modern research demands without compromising methodological integrity.
As scientific literature continues to grow exponentially, researchers face increasing pressure to generate high-quality insights under compressed timelines. Traditional systematic review platforms often prioritize structure and compliance but can be rigid, operationally heavy, and slow to adapt to iterative research workflows. At the same time, many generative AI tools offer speed but operate as opaque “black boxes,” producing outputs that are difficult to validate, reproduce, or defend in scientific and regulatory settings.
ScholarIQ was created to remove this tradeoff.
The platform is built around a simple philosophy:
AI should accelerate research execution without replacing human judgment.
Rather than functioning as a one-click summarization tool, ScholarIQ serves as a transparent, researcher-guided evidence workspace. AI is embedded throughout the platform to reduce manual effort, streamline repetitive tasks, and accelerate literature workflows while keeping researchers fully in control of decisions and interpretation.
ScholarIQ supports flexible and non-linear research workflows that reflect how scientific investigation actually occurs. Researchers can search literature using natural-language queries, upload records or PDFs at any stage, refine search strategies iteratively, screen studies, extract structured data, and interact directly with full-text articles using AskAI — all within a unified interface.
Unlike many AI research tools that rely primarily on abstracts, ScholarIQ’s AskAI functionality engages directly with full-text articles to help users extract PICO elements, study characteristics, outcomes, inclusion criteria, and other structured insights. AI-generated responses remain citation-linked and traceable back to the underlying source material, allowing researchers to inspect supporting evidence and validate conclusions directly within the workflow.
The platform also supports living and continuous evidence workflows. New records can be uploaded at any stage, automatically deduplicated against existing libraries, and prioritized using AI-assisted relevance ranking while preserving previous screening decisions. This makes ScholarIQ especially valuable for rapid reviews, scoping reviews, evidence surveillance, and evolving research domains where literature changes continuously.
ScholarIQ is intentionally designed with a streamlined and lightweight user experience that minimizes operational friction and reduces the administrative burden often associated with traditional review platforms. Features such as embedded PDF review, split-screen extraction workflows, AI-assisted querying, conversational AskAI interactions, tagging, and evidence mapping help researchers focus more on scientific interpretation and less on software management.
While ScholarIQ emphasizes speed, flexibility, and AI-assisted exploration, it does not replace systematic review methodology. Instead, it modernizes how researchers interact with evidence.
Every aspect of the platform is built around three core principles:
- Transparency over black-box AI
- Flexibility over workflow rigidity
- Human judgment remains central
Researchers remain accountable for scientific interpretation, validation, and decision-making.
As part of the broader PICO Portal ecosystem, ScholarIQ also provides a seamless pathway from rapid exploratory research into enterprise-grade systematic review workflows, enabling organizations to scale from lightweight evidence discovery to structured, submission-ready review processes without losing continuity.
ScholarIQ is designed not simply to make literature reviews faster, but to make modern evidence synthesis more transparent, adaptable, and usable in the age of AI.
