Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

Introduction: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.Objective: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.Results: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.Conclusion: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health. [ABSTRACT FROM AUTHOR]