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

TORONTO, June 12, 2025 (GLOBE NEWSWIRE) -- NetraMark Holdings Inc. (the “Company” or “NetraMark”) (CSE: AIAI) (OTCQB: AINMF) (Frankfurt: PF0), an artificial intelligence (AI) solutions company transforming clinical trial design in the pharmaceutical industry through streamlined access to actionable analytics uncovered by AI, today announced that NetraMark founder Dr. Joseph Geraci was the lead author on a new publication that outlines the market opportunity for AI/ML to improve data quality and patient outcomes in clinical development.

This manuscript, now available in the Journal of the Society for Clinical Data Management (JSCDM), is titled “Current Opportunities for the Integration and Use of AI/ML in Clinical Trials: Good Clinical Practice Perspectives.” It identifies the ideal conditions and prerequisites for the use of emerging AI applications in clinical trials and the importance of aligning these technologies with Good Clinical Practice (GCP).

Dr. Joseph Geraci, Founder, CSO, and CTO of NetraMark, contributed to this collaborative effort alongside individuals affiliated with organizations such as the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Danish Medicines Agency (DKMA), Pfizer, Sanofi, Neurocrine Biosciences, Saama Technologies, Relation Therapeutics, Cognizant Technology Solutions, and Wega Informatik AG. These contributions represent the personal views of the authors and not the official positions of their respective organizations.

Drawing on the collective insights of regulators, sponsors, and technology developers, this publication explores the multifaceted landscape of AI/ML applications, where AI can deliver measurable benefits while ensuring compliance, transparency, patient safety, efficiency, accuracy, and overall effectiveness of clinical trials, specifically through seven real-world use cases:

1. Smart Data Query
2. Data Attributability Challenges in Wearable Device Data
3. Enhancing Protocol Deviation Trending
4. External Control Arms
5. Streamlining Complaint Handling
6. Patient Stratification in Diagnosis
7. Patient Enrichment for Placebo/Drug Response
   

Additionally, the manuscript highlights several challenges and ethical considerations facing AI/ML adoption in clinical trial, including:

Challenges

• Generalizability - how well AI models can perform beyond their original training, testing, and validation data
• Provenance - the decisions, implicit or hidden, that were made in the creation of a model
• Effective clinical trialist-AI/ML interaction