BEKhealth AI Outperforms Google, Amazon, and Other Leading Medical AI

When clinical trial recruitment hinges on precision and speed, the quality of AI matters more than ever. In our white paper titled BEKhealth AI Outperforms Google, Amazon, and Other Leading Medical AI, BEKhealth introduces a specialized suite of medical language models designed specifically for clinical trial patient matching—and demonstrates how those models consistently surpass general-purpose NLP tools from AWS, Google, Spark NLP, and medspaCy.

Built upon rich EMR datasets and validated by domain experts, BEKhealth’s LLMs were benchmarked across multiple critical categories—labs and biomarkers, medical procedures, medication history, and diagnoses. In rigorous head-to-head comparisons, BEKhealth AI delivered accuracy scores up to 80%+, including at high-certainty thresholds, outperforming all competitors in every category.

Why does this matter? Because in clinical research, misidentifying a protocol-eligible patient can delay enrollment, skew data integrity, and introduce trial inefficiencies. BEKhealth’s platform is trained to understand trial-specific eligibility criteria directly from both structured and unstructured EMR sources, effectively translating vast amounts of raw clinical data into real-time matching insights for sponsors, research sites, and CROs.

In addition to raw accuracy improvements, BEKhealth’s system also offers predictive confidence scores—only predictions with over 80% confidence are surfaced—which historically align with over 90% clinician validation. This human-in-the-loop approach ensures not just faster identification, but safer, more reliable outcomes for patients and trials.

Download the white paper to explore key findings including:

• Comparative performance across leading NLP solutions

• BEKhealth’s specialized approach to EMR data

• Implications for enrollment acceleration, site selection, and trial efficiency

Whether you’re a sponsor looking to improve site screening accuracy, a research site seeking better feasibility tools, or a CRO aiming to reduce patient matching time, the insights in this report offer a new benchmark for AI-powered trial readiness.