The Role of Quantum Computing and AI in Enhancing Clinical Trial Matching Efficiency
Abstract
Matching eligible patients to appropriate clinical trials remains a major challenge in
healthcare due to large, heterogeneous datasets and complex eligibility criteria. This paper proposes
a novel Quantum Machine Learning (QML) algorithm integrated with Natural Language Processing
(NLP) techniques to optimize clinical trial matching. Unlike traditional methods, the proposed
framework introduces a hybrid quantum–classical pseudocode workflow that combines
quantum feature mapping with NLP-based data extraction to improve accuracy and efficiency in
patient–trial matching. To strengthen the foundation of the proposed approach, key patented technologies,
such as US20200005906A1 (intelligent patient–trial data matching) and
US20220068443A1 (NLP-based eligibility interpretation), have been considered, demonstrating
the growing feasibility of automated matching systems. The novelty of our work lies in unifying
these advances within a scalable QML–NLP framework that reduces manual workload, shortens
trial recruitment times, and improves overall research efficiency. By integrating quantum computing
with NLP, this study moves beyond existing reviews and provides a concrete, implementable
methodology to accelerate patient recruitment and advance personalized medicine.
By leveraging the computational power of quantum computing, current limitations in managing big data for clinical trials can be overcome. The synergy of quantum machine learning and NLP enhances trial efficiency, reduces…
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- In silico toxicology protocols
- Automated in Silico Design of Homogeneous Catalysts
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.