Accelerate with MadeAi-LR
Understanding how challenging, time-consuming, and draining literature review work can be for busy life science professionals, we created MadeAi-LR. MadeAi-LR provides the strength of a proven, award-winning, end-to-end platform and the knowledge of support experts experienced in optimizing AI efficiencies for life science literature review. MadeAi-LR gives life science literature reviews a centralized collaboration platform, automated efficiency, and transparency for workflow validation.
Customer Result
1
40% - 60%
90%
90%
90%
MadeAi-LR Features
Delivered through our AI-Powered Expert Services Team, the MadeAi Platform brings significant value to literature review customers enjoying its rich set of features.
End-to-End Collaboration Platform
MadeAi-LR provides a centralized collaboration platform to conduct end-to-end literature review work. MadeAi-LR facilitates communication and coordination among research team members. Users can leave comments, tags, change roles, and track progress in real-time, enhancing collaboration, workflow efficiency, and teamwork.
Smart Search
MadeAi-LR integrates multiple databases into a single platform (e.g., PubMed, Embase, Google Scholar, Cochrane Library), providing researchers with a centralized and comprehensive search experience, which allows for efficient and thorough literature searches across a wide range of sources.
Deduplication
MadeAi-LR automatically identifies and categorizes unique and duplicate records separately, streamlining the screening process and ensuring that only unique studies are included in the review.
GenAI-aided Screening
MadeAi-LR uses GenAI to screen studies using a research-oriented framework, helping researchers quickly identify relevant studies that meet their criteria.
Full-Text Purchase & Management
MadeAi-LR offers the ability to purchase and manage full-text articles directly within the platform through an integration with RightFind, eliminating the need for reviewers to access multiple external sources.
Quality Appraisal
MadeAi-LR helps reviewers to assess the quality of studies from full-text PDFs by answering domain-specific questions, along with an overall risk rating. It provides visual outputs to help researchers quickly interpret study quality across the review, significantly reducing manual effort and enhancing transparency.
AI-Aided Extraction
Summary level Extraction:
MadeAi-LR helps reviewers by automatically generating a clear, structured summary table for each study, capturing all key details in line with the review’s objectives. It provides full contextual traceability, minimizes manual workload, and enhances the reliability and efficiency of evidence synthesis.
Arm Level Extraction:
MadeAi-LR enables detailed extraction of statistical and outcome data at the treatment arm or subgroup level from an article and facilitates comparative analysis. It enhances transparency, supports robust subgroup analyses, and reduces manual data handling effort during systematic reviews.
Single- and Multiple-Article Summaries
MadeAi-LR generates concise single- and multiple-article summaries, allowing researchers to quickly extract key findings and insights.
Reports & Documentation
MadeAi-LR facilitates the generation of comprehensive reports and documentation for publication or regulatory purposes. Researchers can export screening decisions, data extraction results, and single-article summary and multiple-article summary, among other relevant information in various formats for reporting and dissemination. MadeAi-LR also offers customizable report generation.
Traceability to Assist Expert Verification
MadeAI-LR enables traceability in AI-driven screening and extraction by highlighting the rationale behind each prediction and pinpoints the exact source within the article PDF during data extraction. This supports expert verification, improves transparency, and streamlines the review process.
Verification on Compliance Checking
MadeAi-LR includes built-in templates for generating PRISMA charts, ensuring compliance with industry standards and guidelines. This helps ensure that the review process adheres to best practices and enhances the credibility and trustworthiness of the findings.
Living LR
Even after completing a comprehensive literature review, new, relevant articles continually emerge in medical journals that need to be incorporated to keep the study up to date. MadeAi-LR offers an optional Living LR, which keeps the completed LR up to date by automatically monitoring for and incorporating article.
AI-Integrated Review
AI-aided Review:
AI-aided Review can be used when reviewing Title & Abstract Screening and Full-text Screening. For the AI-aided Review mode, AI-generated suggestions are shared with two blinded human reviewers to facilitate faster, more informed decisions.
AI as Reviewer:
AI as Reviewer mode enables one human reviewer to work in parallel to the AI, which serves as the second reviewer—reducing manual workload without compromising review quality. MadeAi will sift through the articles, tag them as likely relevant or likely irrelevant, and provide an explanation for each decision.
AI as Reviewer
AI as Reviewer mode enables one human reviewer to work in parallel to the AI, which serves as the second reviewer—reducing manual workload without compromising review quality. MadeAi will sift through the articles, tag them as likely relevant or likely irrelevant, and provide an explanation for each decision.
MadeAi’s AI-Powered Expert Services Distinction
The value of MadeAi’s GenAI-enabled, end-to-end literature review solution comes from the MadeAi’s AI-Powered Expert Services, and the team’s understanding of the complexities in using AI:
- Optimizing AI for literature review requires a nuanced approach with an experienced partner leveraging a proven platform.
- Applying AI saves time in many manual areas of literature review, while requiring validation points throughout the process, where subject matter expertise is critical.
- Rethinking the AI-accelerated workflow is a necessary component of achieving the benefits of AI innovation, one that requires deep AI experience to support.
- Adapting traditional formats, updating methodologies, and embracing innovation and greater efficiency are all part of MadeAi’s unique platform and service offering.
Contact Info
Connect with a MadeAi representative to see how we can support your content synthesis needs.