Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an explainable AI system using Natural Language Processing (NLP) to match patients with phase 1 oncology clinical trials, improving recruitment efficiency and drug development. The AI shows promising results for high-quality patient-trial matching.
Area Of Science
- Oncology
- Artificial Intelligence
- Clinical Trial Recruitment
Background
- Patient recruitment for phase 1 oncology clinical trials faces significant challenges, impacting drug development efficiency.
- Existing methods for matching patients to trials often lack transparency and efficiency.
Purpose Of The Study
- To develop and evaluate an explainable AI system using Natural Language Processing (NLP) for matching patients with phase 1 oncology clinical trials.
- To improve the efficiency and quality of patient-trial matching in early-phase oncology research.
Main Methods
- A prototype AI system was developed utilizing modern NLP techniques to match patient records with phase 1 oncology clinical trial protocols.
- Matching considered four key criteria: cancer type, performance status, genetic mutation, and measurable disease.
- The system provides a matching score with explanations, evaluated against domain expert ground truth using synthesized data.
Main Results
- The AI system achieved a precision of 73.68%, recall of 56%, accuracy of 77.78%, and specificity of 89.36%.
- Key error sources identified include abbreviation ambiguity and contextual misunderstanding.
- The system demonstrated no false positive matches when evidence of no match was found.
Conclusions
- Explainable AI offers a promising approach to enhance patient-trial matching efficiency and quality in phase 1 oncology.
- This NLP-based system represents a novel, publicly available tool for optimizing patient selection for early-phase oncology trials.
- Further development is warranted to address identified error sources and improve system performance.
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