Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching

Dimensions overall score 6.0

• SatIR is a scalable constraint-satisfaction method for clinical trial matching that uses LLMs to improve recall and interpretability over existing techniques. Existing retrieval techniques, largely based on keyword and embedding-similarity matching between patient profiles and eligibility criteria, often struggle with low recall, low precision, and limited interpretability due to complex constraints.problem 35%
  Evidencepartial

  SatIR is a scalable constraint-satisfaction method for clinical trial matching that uses LLMs to improve recall and interpretability over existing techniques. Existing retrieval techniques, largely based on keyword and embedding-similarity matching between patient profiles and eligibility criteria, often struggle with low recall, low precision, and limited interpretability due to complex constraints.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• Clinical trials are central to evidence-based medicine, yet many struggle to meet enrollment targets, despite the availability of over half a million trials listed on ClinicalTrials.gov, which attracts approximately two million users monthly. Existing retrieval techniques, largely based on keyword and embedding-similarity matching between patient profiles and eligibility criteria, often struggle with low recall, low precision, and limited interpretability due to complex constraints.method 35%
  Evidencepartial

  Clinical trials are central to evidence-based medicine, yet many struggle to meet enrollment targets, despite the availability of over half a million trials listed on ClinicalTrials.gov, which attracts approximately two million users monthly. Existing retrieval techniques, largely based on keyword and embedding-similarity matching between patient profiles and eligibility criteria, often struggle with low recall, low precision, and limited interpretability due to complex constraints.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• ScienceToStartup currently rates this 6.0/10 on the public viability pass. It retrieves 32%-72% more relevant-and-eligible trials per patient, improves recall over the union of useful trials by 22-38 points, and serves more patients with at least one useful trial.result 35%
  Evidencepartial

  ScienceToStartup currently rates this 6.0/10 on the public viability pass. It retrieves 32%-72% more relevant-and-eligible trials per patient, improves recall over the union of useful trials by 22-38 points, and serves more patients with at least one useful trial.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• Clinical Trial Matching moved forward this cycle; last verified April 2026. Public score 6.0/10.why now 35%
  Evidencepartial

  Clinical Trial Matching moved forward this cycle; last verified April 2026. Public score 6.0/10.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

Competitive landscape

SatIR is a scalable constraint-satisfaction method for clinical trial matching that uses LLMs to improve recall and interpretability over existing techniques.