arXiv:2502.18531v1 Announce Type: new
Abstract: Background: Recruitment for cohorts involving complex liver diseases, such as hepatocellular carcinoma and liver cirrhosis, often requires interpreting semantically complex criteria. Traditional manual screening methods are time-consuming and prone to errors. While AI-powered pre-screening offers potential solutions, challenges remain regarding accuracy, efficiency, and data privacy. Methods: We developed a novel patient pre-screening pipeline that leverages clinical expertise to guide the precise, safe, and efficient application of large language models. The pipeline breaks down complex criteria into a series of composite questions and then employs two strategies to perform semantic question-answering through electronic health records – (1) Pathway A, Anthropomorphized Experts’ Chain of Thought strategy, and (2) Pathway B, Preset Stances within an Agent Collaboration strategy, particularly in managing complex clinical reasoning scenarios. The pipeline is evaluated on three key metrics-precision, time consumption, and counterfactual inference – at both the question and criterion levels. Results: Our pipeline achieved high precision (0.921, in criteria level) and efficiency (0.44s per task). Pathway B excelled in complex reasoning, while Pathway A was effective in precise data extraction with faster processing times. Both pathways achieved comparable precision. The pipeline showed promising results in hepatocellular carcinoma (0.878) and cirrhosis trials (0.843). Conclusions: This data-secure and time-efficient pipeline shows high precision in hepatopathy trials, providing promising solutions for streamlining clinical trial workflows. Its efficiency and adaptability make it suitable for improving patient recruitment. And its capability to function in resource-constrained environments further enhances its utility in clinical settings.
Expert Commentary: Streamlining Clinical Trial Workflows with AI-Powered Patient Pre-Screening
In the field of clinical research, patient recruitment for complex liver diseases such as hepatocellular carcinoma and liver cirrhosis can be a challenging task. The traditional manual screening methods are not only time-consuming but also prone to human errors. However, the advent of AI-powered pre-screening offers potential solutions to these challenges.
This article introduces a novel patient pre-screening pipeline that leverages clinical expertise to guide the precise, safe, and efficient application of large language models. The pipeline breaks down complex criteria into a series of composite questions and then applies two strategies to perform semantic question-answering through electronic health records.
Multi-disciplinary Nature of the Concepts
This research effort combines expertise from multiple disciplines, including clinical medicine, artificial intelligence, and natural language processing. It demonstrates the integration of clinical knowledge and technological advancements to address the specific challenges associated with patient recruitment in complex liver disease trials.
The pipeline’s approach to breaking down complex criteria shows the influence of clinical expertise in designing effective questions that extract the relevant information from electronic health records. At the same time, the utilization of large language models powered by AI demonstrates the significance of cutting-edge technology in achieving precise and efficient results.
Pathway A: Anthropomorphized Experts’ Chain of Thought Strategy
This strategy employed in the pipeline focuses on mimicking the reasoning process of human experts. By breaking down complex clinical reasoning scenarios into a series of questions, it facilitates precise data extraction from electronic health records. Pathway A shows the potential to assist in automating the understanding and interpretation of complex medical information, reducing the burden on human experts and improving the efficiency of patient pre-screening.
Pathway B: Preset Stances within an Agent Collaboration Strategy
Pathway B, on the other hand, utilizes the collaboration between an agent and the clinical experts to tackle complex reasoning scenarios. This strategy acknowledges the limitations of fully automated approaches and emphasizes the importance of human input in handling intricate clinical situations. By combining the insights and expertise of both machine and human, Pathway B enhances the accuracy of semantic question-answering and provides a valuable approach for managing complex clinical reasoning.
Evaluation Metrics and Results
The pipeline’s evaluation metrics include precision, time consumption, and counterfactual inference at both the question and criterion levels. The results indicate high precision (0.921 at the criterion level) and efficiency (0.44 seconds per task) of the pipeline. This suggests that the pipeline is capable of accurately extracting relevant information from electronic health records and processing it in a timely manner.
Importantly, the pipeline’s promising results in the specific contexts of hepatocellular carcinoma and cirrhosis trials (achieving precision rates of 0.878 and 0.843, respectively) highlight its potential in advancing the recruitment process for these complex liver diseases. The ability of the pipeline to handle different diseases showcases its adaptability and generalizability, making it a suitable tool for improving patient recruitment in various clinical trial workflows.
Promising Solutions for Streamlining Clinical Trial Workflows
This data-secure and time-efficient patient pre-screening pipeline holds great promise for streamlining clinical trial workflows. By automating the screening process and reducing the manual effort required, the pipeline can expedite patient recruitment and enhance the efficiency of clinical trials. Its precision and adaptability further contribute to its utility in diverse clinical settings.
The multi-disciplinary nature of this research effort highlights the importance of collaboration between clinical experts and technology specialists. Moving forward, further research could focus on refining the pipeline’s accuracy, exploring its potential in other disease areas, and addressing any data privacy concerns. Overall, the integration of AI-powered patient pre-screening in clinical trials opens new avenues for improving healthcare outcomes and advancing medical research.