Accelerate with MadeAi-LR

Literature review (LR) is a comprehensive, crucial, and structured summary and synthesis of existing published research on a specific topic or research question. Its goal is to deliver unbiased, high-quality evidence while minimizing duplication of prior work.

MadeAi-LR is an AI-powered platform built for life science literature reviews, combining advanced GenAI technology with expert human support. Unlike standalone tools, it offers both automation and professional guidance, ensuring speed, accuracy, and scientific rigor in every review. MadeAi-LR is the most comprehensive, end-to-end GenAI-enabled literature review platform, supporting life science professionals from protocol creation through to submission reports. It’s an AI-native solution, built in partnership with three of the top 15 pharma companies.

Based on our customer results, MadeAi-LR delivers 40-60% time savings on literature review projects. The platform automates time-intensive tasks like deduplication, screening, and data extraction while maintaining 90% accuracy rates across key processes. This means your team can focus on high-value analytical work rather than manual article processing, significantly accelerating your research timelines without compromising quality. See our Return-on-Investment overview for specific details on the time savings of MadeAi.

The “AI as Reviewer” mode pairs one human reviewer with AI as the second reviewer. The AI classifies articles as relevant or irrelevant, providing explanations. This keeps the dual-review process intact while reducing manual work by half. MadeAI ensures expert validation at important stages, aiming to improve human judgment intelligently.

MadeAi-LR integrates multiple major databases into a single platform, including PubMed, Embase, Google Scholar, and the Cochrane Library, among others. This centralized approach eliminates the need to jump between different interfaces and import/export files repeatedly. You get comprehensive search coverage while working from one unified workspace.

Deduplication is handled automatically. MadeAi-LR identifies and categorizes unique versus duplicate records separately, so you’re not wasting time reviewing the same study multiple times. The system streamlines your screening process by ensuring only unique studies move forward in your review pipeline.

MadeAI-LR includes integrated full-text purchasing and management through RightFind. You can purchase and access full-text articles directly within the platform without leaving your workflow to navigate multiple external repositories or document delivery services. Everything stays centralized and trackable.

Traceability is built into every AI-driven function. When MadeAi-LR makes screening recommendations or extracts data, it highlights the rationale behind each prediction and pinpoints the exact source within the article PDF. This transparency supports expert verification, helps you understand why the AI made specific decisions, and makes it easier to validate findings for regulatory or publication purposes.

Yes, the Quality Appraisal feature helps reviewers assess study quality directly from full-text PDFs by answering domain-specific questions and providing overall risk ratings. The platform generates visual outputs that let you quickly interpret study quality across your entire review, significantly reducing manual effort while enhancing transparency and standardization.

MadeAi-LR offers two levels of AI-aided extraction. Summary-level extraction automatically generates structured summary tables capturing key details aligned with your review objectives. Arm-level extraction goes deeper, pulling detailed statistical and outcome data at the treatment arm or subgroup level. Both approaches provide full contextual traceability and dramatically reduce manual data handling effort.

The platform serves as a centralized collaboration hub where team members can leave comments, add tags, change roles, and track progress in real-time. Everyone works from the same up-to-date information, eliminating version control nightmares and ensuring seamless communication and coordination throughout your review process.

Living LR is an optional feature that keeps your completed literature review continuously up to date. Since relevant articles constantly emerge in medical journals, Living LR automatically monitors for and incorporates new publications. This is particularly valuable for evidence that informs ongoing clinical decisions, regulatory updates, or long-term research programs where staying current is critical.

Absolutely. The platform includes built-in templates for generating PRISMA charts and facilitates the creation of comprehensive reports for publication or regulatory purposes. You can export screening decisions, data extraction results, and article summaries in various formats. The traceability features also support audit trails and validation documentation that regulators and journals expect.

Report generation is highly customizable to match your specific needs. Whether you’re preparing submissions for regulatory agencies, academic journals, or internal stakeholders, MadeAi-LR can adapt output formats and content to meet different requirements. The platform gives you flexibility while maintaining consistency and compliance with industry standards.

MadeAi-LR isn’t just software, it’s delivered through AI-Powered Expert Services. You get access to a team experienced in optimizing AI efficiencies specifically for life science literature reviews. They understand that applying AI requires validation points where subject matter expertise is critical, and they help you rethink workflows to maximize the benefits of AI innovation. This combination of proven platform plus expert guidance is what makes MadeAI’s approach distinctive.

Yes, the platform is designed with publication-grade rigor in mind. The 90% accuracy rates, built-in PRISMA compliance, comprehensive traceability, and detailed documentation capabilities all support the standards expected by high-impact journals and regulatory bodies. The expert services team also understands how to adapt AI-accelerated workflows while maintaining methodological integrity.