Evidence Generation for AMCP Dossiers Evidence generation for AMCP (Academy of Managed Care Pharmacy) dossiers sits at the center of successful U.S. market access. Healthcare decision-makers rely on these dossiers to evaluate clinical value, economic impact, and real-world performance before making formulary and coverage decisions. Yet traditional manual processes struggle to keep pace with expanding…

What Is Abstract Research with AI? Abstract Research with AI converts a plain-language research question into a summary of published evidence, with each statement linked back to its source record. There is no search string to build and no stack of abstracts to screen by hand. A researcher asks a question and receives an answer…

Living Review: Evidence in Real-Time Living Systematic Reviews exist to solve a timing problem: the evidence base moves while the review is still being written. A review is a photograph. Clinical practice is a film. Between the last search date and the day a decision gets made, new trials report, guidelines shift, and regulators act. …

The Need for AI-Powered Quick Research  AI-Powered Quick Research gives life sciences teams a faster way to explore and synthesize evidence while maintaining structure, traceability, and scientific rigor. Traditional systematic literature reviews (SLRs) remain the gold standard for high-stakes evidence in life sciences. However, they often take a long time, consume substantial resources, and struggle…

Data Extraction from Charts and Graphs: An Overview While extracting structured data from published text has become increasingly mature, extracting equivalent precision from visual data remains a significant challenge. Extracting the same precision from figures is not. In systematic literature reviews (SLRs), critical numbers often live only inside forest plots, Kaplan-Meier curves, Sankey diagrams, or…

AI-Native Network Meta-Analysis: An Overview AI-Native Network Meta-Analysis is changing how life sciences teams generate and update comparative evidence. Network meta-analysis (NMA) lets decision-makers compare multiple treatments simultaneously, even when head-to-head trials are missing. Traditional NMA remains rigorous but slow, labor-intensive, and difficult to keep current. An AI-Native approach embeds machine learning and large language…