AI for Clinical Study Risk Assessment and Inspection Preparation
Description
Challenges in assessing large amounts of clinical study information for risk assessment and inspection planning in a short period of time
Detailed example
Summary including any inconsistencies, discrepancies, missing information, protocol deviations, unforeseen circumstances, unexpected adverse events, severe or serious adverse events, and modifications to processes or procedures; description of potential impact on study outcome. Outputs are verified by FDA staff.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Efficient and thorough review of clinical portions of pivotal studies, enabling risk assessors and reviewers to identify and address potential issues. This benefit promotes public health by protecting study subjects, and by helping the office verify the quality, study integrity, and regulatory compliance of Bioavailability/Bioequivalence (BA/BE) studies supporting CDER-regulated drugs.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
No FDA data were used to train, fine-tune or evaluate