NetraMark Founder Coauthors New Publication on AI/ML Use in Clinical Trials, Alongside Authors From Leading Global Regulatory Organizations

Core Insights - NetraMark Holdings Inc. is focused on leveraging AI solutions to enhance clinical trial design in the pharmaceutical industry, as highlighted by a new publication led by founder Dr. Joseph Geraci [1][2][6] - The publication discusses the integration of AI/ML in clinical trials, emphasizing the need for alignment with Good Clinical Practice (GCP) [2][4] - The collaborative effort includes contributions from various regulatory and pharmaceutical organizations, showcasing a broad consensus on the potential of AI/ML in improving clinical trial outcomes [3][4] Group 1: Publication Overview - The manuscript titled "Current Opportunities for the Integration and Use of AI/ML in Clinical Trials: Good Clinical Practice Perspectives" is now available in the Journal of the Society for Clinical Data Management [2] - It outlines ideal conditions for AI applications in clinical trials and stresses the importance of compliance, transparency, and patient safety [2][4] - The publication identifies seven real-world use cases where AI can enhance clinical trial effectiveness [4] Group 2: Challenges and Ethical Considerations - The manuscript addresses challenges and ethical considerations in adopting AI/ML in clinical trials, including data quality monitoring and patient stratification [4][5] - Key challenges include data attributability in wearable device data and the need for robust privacy controls [5] - Ethical considerations focus on ensuring AI is lawful, ethical, and socially robust, with an emphasis on explainability and auditability [5] Group 3: Company Commitment and Future Directions - NetraMark is committed to collaborating with sponsors to maximize the benefits of AI/ML in clinical trials, aiming to set industry standards for ethical compliance [6] - The company utilizes a novel topology-based algorithm to analyze patient data, enabling effective segmentation and classification for clinical trials [7] - NetraMark's approach allows for the use of smaller datasets while maintaining accuracy in disease classification and treatment efficacy [7]