Detecting Site‐Level Fraud via an Artificial Intelligence/Machine Learning Paradoxical Patient Analysis in an Alzheimer's Disease Clinical Trial
This work demonstrates that NetraAI's paradoxical patient-focused approach can identify anomalous patients and sites in AD clinical trials using screening and baseline data alone, even without prior site-level fraud data. By relying solely on patient-level metrics, NetraAI provides a scalable, objective strategy to enhance data integrity in clinical trials. Future research will extend these insights to direct site-level assessments, further refining this framework for broader clinical trial applications.
Authors
- Larry Alphs (ORCID: https://orcid.org/0000-0001-9991-6905)
- Suzanne Hendrix (ORCID: https://orcid.org/0000-0002-2374-7727)
- Joseph Geraci (ORCID: https://orcid.org/0000-0003-0967-2164)
- Paul Leonchyk
- Kent Hendrix
- Luca Pani
- Patrick P O'Keefe
- Samuel P. Dickson
- Bessi Qorri
Institutions
- University of Miami (US)
- Queen's University (CA)
- University of California San Diego (US)
- TetraLogic Pharmaceuticals (United States) (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70859_103051
- Primary Topic
- Imbalanced Data Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00