Transforming Clinical Development with AI: Quantiphi  Driving Innovation With NVIDIA AI Enterprise Software in Life Sciences

Patient recruitment remains one of the biggest hurdles in clinical trials, with 19% of trials failing due to low enrollment. The process of manually reviewing Electronic Health Records (EHRs) against protocol inclusion and exclusion criteria is slow, inconsistent, and prone to bias, often leading to patient misclassification or delays in enrollment. This not only impacts trial timelines but also contributes to rising costs and underrepresentation of diverse populations in research.

Quantiphi’s Clinical Trial Patient Cohort Generation solution automates patient screening by analyzing EHRs and cross-referencing them with trial eligibility criteria. By leveraging LLMs to interpret clinical data, the solution ranks eligible patients into cohorts, enabling faster and more informed recruitment decisions. It seamlessly integrates with Clinical Trial Management Systems (CTMS) ensuring real-time updates, while AI-driven cohort ranking optimizes patient selection. This results in accelerated recruitment, improved trial diversity, and reduced operational burden on clinical teams.

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Drafting a clinical trial protocol is a complex and time-intensive process that requires inputs from multiple stakeholders. On average, protocol development takes several months, with frequent revisions due to incomplete or inconsistent data. This leads to significant delays in trial initiation and increases the administrative burden on clinical operations teams. Moreover, ensuring compliance with global regulatory standards adds another layer of complexity, further slowing down the approval process.

Quantiphi’s Clinical Protocol Writer automates protocol generation by extracting and synthesizing information from investigator brochures, trial designs, historical protocols and from external systems including PubMed and clinicaltrilas.gov. Utilizing fine-tuned LLMs for clinical text generation, the solution creates structured, pre-filled drafts with contextualized content for each section, while significantly eliminating manual efforts for medical writers. By reducing protocol development time, the solution enables faster trial initiation and minimizes errors, improving overall operational efficiency.

Clinical trials generate vast volumes of regulatory and operational documents, from investigator brochures to site reports and compliance filings. Managing these documents manually is inefficient, with studies showing that 50% of trial data contains inconsistencies, increasing regulatory risks and delaying approvals. Ensuring audit readiness and maintaining compliance across multiple jurisdictions further compounds the challenge, leading to bottlenecks in document workflows.

Quantiphi’s Clinical Document Processing solution automates document classification, metadata extraction, and quality validation using LLMs and multi-modal AI. The platform intelligently categorizes documents, extracts key metadata, and flags inconsistencies for human review. Automated quality checks and human-in-the-loop workflows enhance validation, while a real-time dashboard provides complete visibility into document status. This streamlines document processing, ensures compliance, and improves audit readiness, reducing manual workloads and expediting regulatory submissions.

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Pharmacovigilance teams are responsible for monitoring and reporting adverse events (AEs) to ensure patient safety, but manual case processing leads to delays and underreporting. With millions of scientific articles, case reports, and patient records published annually, extracting meaningful insights and ensuring timely reporting is a significant challenge. Regulatory agencies require stringent reporting timelines, and delays in AE detection can result in non-compliance, financial penalties, or even patient harm.

Quantiphi’s Pharmacovigilance Platform (QSafe) leverages LLMs to automate AE identification, causality assessment, and regulatory reporting. The platform scans medical literature, case reports, and safety databases to detect potential AEs, classifies their severity, and generates structured reports aligned with regulatory requirements. Built-in workflows for case assignment, review, and approval ensure seamless pharmacovigilance operations, while a centralized dashboard provides real-time tracking of AE cases. This solution enhances AE reporting accuracy, accelerates regulatory submissions, and improves overall patient safety.

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