AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows

• URL: http://arxiv.org/abs/2602.00052v1
• Date: Mon, 19 Jan 2026 18:38:36 GMT
• Title: AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows
• Authors: Ramtin Babaeipour, François Charest, Madison Wright,
• Abstract summary: Structuring protocol content into standard formats has the potential to improve efficiency, support documentation quality, and strengthen compliance.<n>We evaluate an Artificial Intelligence (AI) system using generative LLMs with RetrievalAugmented Generation (RAG) for automated clinical trial protocol information extraction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Increasing clinical trial protocol complexity, amendments, and challenges around knowledge management create significant burden for trial teams. Structuring protocol content into standard formats has the potential to improve efficiency, support documentation quality, and strengthen compliance. We evaluate an Artificial Intelligence (AI) system using generative LLMs with Retrieval-Augmented Generation (RAG) for automated clinical trial protocol information extraction. We compare the extraction accuracy of our clinical-trial-specific RAG process against that of publicly available (standalone) LLMs. We also assess the operational impact of AI-assistance on simulated extraction CRC workflows. Our RAG process was measured as more accurate (87.8%) than standalone LLMs with fine-tuned prompts (62.6%) against expert-supported reference annotations. In the simulated extraction workflows, AI-assisted tasks were completed 40% faster, rated as less cognitively demanding and strongly preferred by users. While expert oversight remains essential, this suggests that AI-assisted extraction can enable protocol intelligence at scale, motivating the integration of similar methodologies into real world clinical workflows to further validate its impact on feasibility, study start-up, and post-activation monitoring.

Related papers

• Timely Clinical Diagnosis through Active Test Selection [49.091903570068155]
  We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design) to better emulate real-world diagnostic reasoning.<n>LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data.<n>We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use.
  arXiv  Detail & Related papers  (2025-10-21T18:10:45Z)
• Leveraging AI to Accelerate Medical Data Cleaning: A Comparative Study of AI-Assisted vs. Traditional Methods [3.2666593942117688]
  Octozi is an artificial intelligence-assisted platform that combines large language models with domain-specifics to transform medical data review.<n>Economic analysis of a representative Phase III oncology trial reveals potential cost savings of $5.1 million.
  arXiv  Detail & Related papers  (2025-08-07T15:49:32Z)
• TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching [0.0]
  We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching.<n>Built on fine-tuned, open-source large language models, TrialMatchAI ensures transparency and maintains a lightweight deployment footprint.<n>In real-world validation, 92 percent of oncology patients had at least one relevant trial retrieved within the top 20 recommendations.
  arXiv  Detail & Related papers  (2025-05-13T12:39:06Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• Towards Conditioning Clinical Text Generation for User Control [2.009205898486993]
  This paper explores automated dataset augmentation using Large Language Models (LLMs) as human proxies to condition LLMs for clinician control without increasing cognitive workload.<n>We achieve new state-of-the-art results with simpler methods than prior submissions through more efficient training, yielding a 9% relative improvement without augmented training and up to 34% with dataset augmentation.
  arXiv  Detail & Related papers  (2025-02-24T19:00:13Z)
• Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care [0.0]
  Deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-time monitoring.<n>This study explores the application of deep learning techniques, such as convolutional neural networks [CNNs] and transformerbased models, to stratify patients.<n>Predictive modelling approaches, including survival analysis and time-series forecasting, are employed to predict trial outcomes, enhancing efficiency and reducing trial failure rates.
  arXiv  Detail & Related papers  (2024-12-09T23:20:08Z)
• Advancing Healthcare Automation: Multi-Agent System for Medical Necessity Justification [0.0]
  This paper explores the application of Multi-Agent System (MAS) that utilize specialized LLM agents to automate Prior Authorization task. We demonstrate that GPT-4 checklist achieves an accuracy of 86.2% in predicting item-level judgments with evidence, and 95.6% in determining overall checklist judgment.
  arXiv  Detail & Related papers  (2024-04-27T18:40:05Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)
• Active learning for medical code assignment [55.99831806138029]
  We demonstrate the effectiveness of Active Learning (AL) in multi-label text classification in the clinical domain. We apply a set of well-known AL methods to help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results show that the selection of informative instances provides satisfactory classification with a significantly reduced training set.
  arXiv  Detail & Related papers  (2021-04-12T18:11:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.