Basil Systems, the leading AI-powered product lifecycle intelligence platform for the life sciences industry, announced the launch of its groundbreaking MedTech Safety Signaling solution, a first-of-its-kind technology that transforms post-market surveillance into a proactive risk detection and prevention engine.

Medical device manufacturers operate in one of the most complex and high-stakes environments in healthcare. Every year, companies must monitor millions of siloed and disconnected data points, from adverse event reports and global recalls to competitor filings and internal quality data. Traditionally, safety and product quality teams have had to manually sift through this information, a reactive and resource-intensive process that often identifies risk signals too late, after patient harm or costly recalls have already occurred.

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Basil’s Safety Signaling module is designed to close this gap. By leveraging its proprietary platform with the world’s most comprehensive indexed dataset of regulatory filings, submissions, labels, clinical trials, adverse event reports, recalls, and guidances, Basil enables commercial large language models (LLMs) to uncover subtle, predictive correlations that would otherwise be invisible to human reviewers.

The platform begins with a post-market event selected by the user, whether a recall, warning letter or other event of interest. Basil’s enriched AI then queries millions of adverse event narratives, inspection reports, citations and other resources, scanning for contextual and semantic similarities to the recall’s root cause. Unlike simple keyword searches, Safety Signaling interprets the meaning of these narratives, grouping events with percentage-based confidence scores, root-cause explanations, and direct links back to the original reports for full traceability.

By surfacing elusive patterns such as device design flaws, process control failures, faulty components, or usage environments, Basil’s Safety Signaling equips safety, quality and regulatory teams with actionable intelligence. Insights can be exported directly into existing workflows, quality management systems, and business intelligence tools, making them easy to operationalize.

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The solution is already being deployed by several of the world’s largest medical device manufacturers, where it is helping to:

• Identify potential recall risks by extracting adverse event patterns earlier.
• Accelerate CAPA and field correction strategies by linking safety issues to specific design, supply chain, or manufacturing elements.
• Benchmark safety profiles against competitors and anticipate where regulators may focus next.
• Create a feedback loop for R&D, informing human factors engineering and product design improvements that safeguard patients.

“Medical device companies are drowning in post-market data but starving for predictive analytics and intelligence,” said Anthony Cirurgiao, CEO & Founder, Basil Systems. “Our AI-powered Safety Signaling delivers those insights. We directly address the challenges of early risk detection and root cause analysis by using verifiable data to ignite actionable insights. This isn’t about finding more data; it’s about finding the critical connections in the data that already exist. We help our customers predict and prevent issues, improve product design, and solidify market leadership, while championing patient safety.”

Setting a New Standard for Post-Market Surveillance

With Safety Signaling, Basil Systems is redefining what’s possible in post-market intelligence. For manufacturers, the ability to detect early warning signals is not only a matter of regulatory compliance and potential cost avoidance, but also one of patient trust, brand protection, and long-term business sustainability.

By combining the industry’s most complete dataset with a purpose-built AI enrichment platform, Basil delivers the repeatable, defensible insights that manufacturers need to act with confidence. The proprietary system ensures accuracy by grounding its outputs in verifiable source data and avoiding the “hallucinations” often seen in generic AI tools, while maintaining a design that is intuitive and easy to implement at scale.