ConcertAI has announced enhancements to its CancerLinQ Suite, incorporating AI-driven trial matching and contextualised patient insights.
These updates aim to support oncologists and care teams in processing structured and unstructured data, delivering timely intelligence for trial enrolment and treatment decisions within existing workflows.
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CancerLinQ Suite uses AI to convert unstructured data into actionable insights and unifies quality governance, clinical support, and trial optimisation to help improve patient outcomes.
ConcertAI oncology chief medical officer Shaalan Beg said: “The promise of technology in oncology isn’t measured by how sophisticated the platform is, but instead by whether it meaningfully supports the clinicians delivering care and positively impacts patient outcomes.
“Our newest CancerLinQ enhancements were built to put trial matching and real-world cohort comparisons directly in the workflow, where they can actually inform treatment decisions. That’s the standard we hold ourselves to: AI that earns its place at the bedside.”
The TriaLinQ module within the CancerLinQ Suite offers enhanced patient eligibility matching, using AI to identify clinical trial eligibility factors, including line of therapy, disease progression, recurrence, and detailed biomarker profiles.
The upgraded platform automates eligibility screening, delivering results 3.3 times faster than manual methods.
By integrating insights into clinical workflows, it can support up to 50% higher clinical trial enrolment, thus helping increase patient access to current therapies.
The suite’s PatientLinQ module offers broad patient care insights, especially using biomarker data.
It supports clinicians in identifying trials with at least 90% patient eligibility, comparing patients with real-world cohorts, closing care gaps, and improving decision-making during critical treatment stages.
Powered by ConcertAI’s Cara AI platform, the CancerLinQ Suite integrates data from more than 900 sites and focuses on oncology applications, supporting shared learning and research advancement.
