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Insights from Applied AI Scientists: eIQ Review Use Case Spotlight | Part 4

Interest in artificial intelligence and machine learning (AI/ML) has increased significantly across the life sciences industry over the last year as reflected in eClinical’s 2024 Industry Outlook Survey, where over 50% of respondents selected “AI/ML” as the industry trend that will have the greatest impact on efficiency and outcomes over the next year. In comparison, only 11% of respondents selected AI/ML as their top trend in the 2023 Industry Outlook Survey.

This shift is driven by growing data volumes and complexity, causing many to look proactively to technology for solutions to navigate the evolving clinical trial landscape. However, while interest in innovative approaches and AI/ML is high, there is still uncertainty in how to go about implementing leading edge technologies.

 eIQ Review – part of the elluminate Clinical Data Cloud® – is a simple way to begin incorporating AI/ML in your trials while delivering significant value. Summarized below is an example of an AI/ML-enabled use case deployed within eIQ Review and highlights the data management efficiency gains as a result of leveraging these capabilities.

Concomitant Medications Indication (CMIND)

Traditionally, medical reviewers examine and authenticate concomitant medications (CM) and their paired indications for accuracy, verifying that every medication is correctly matched with its designated use as per the guidelines of the study protocols.

Concomitant Medications Indication (CMIND) within eIQ Review provides an AI system that not only evaluates the correctness of a medication-indication pair, but also provides a nuanced confidence score, indicating the level of certainty in the alignment between the prescribed medication and its specified indication.

To support this, a comprehensive medication-indication dictionary was constructed by leveraging an information extraction technique, fine-tined on open-source data, to associate each medication with a meticulously curated list of correct indications. Building on this foundation, a sophisticated clinical similarity scoring system was implemented, harnessing the capabilities of Large Language Models further.

As a result, the volume of medication-indication pairs requiring manual review was reduced by 60%, all while maintaining a remarkable sensitivity rate of over 90%. Simply put, this translates to a fast and effective streamlined workflow for medical monitors, paving the way for a more efficient and resource-conscious future in clinical trial data management.

In addition to Concomitant Medications Indication (CMIND), several other AI-enabled use cases are available within eIQ Review including: Concomitant Medication Abnormal Duration (CMAD), Automated Adverse Event Duration (AAED), Domain Classification (DMCL), Univariate Outlier Detection (UOD) and Concomitant Medication Consistency (CMCON)

For data management teams, the integration of innovative technologies such as AI and ML can address the limitations of traditional approaches that are simply not scalable enough to support the pace at which clinical trials are evolving. By leveraging these capabilities embedded within the elluminate Clinical Data Cloud, you can significantly reduce time spent on clinical data review while ensuring data quality at scale. To learn more on how to bring AI/ML to the forefront for data management and take the conversation out of the hypothetical and into reality, check out this on-demand webinar.

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2024 Industry Outlook: Driving Tomorrow’s Breakthroughs with Clinical Data Transformation