HIVE AI Pilot
Description
The AI is intended to assist in solving several problems related to the review process for INDs. Specifically, it aims to address issues of inefficiency, delays in identifying deficiencies and information overload. By providing recommendations for review disciplines, it helps reviewers to quickly identify potential review disciplines that are required. By identifying grossly deficient submissions early on, it reduces the workload and highlighting key data helps reviewers to focus on higher-level tasks that require their expertise.
Detailed example
The system's output include: 1. review discipline recommendations - automated suggestions for the most appropriate review disciplines for each incoming submission. 2. highlighted key data - generates reports highlighting critical information to facilitate quicker understanding by RPMs and reviewers. 3. Summaries - reports summarizing large documents to potentially accelerate review.
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
Overall, is designed to improve efficiency and effectiveness of the regulatory review process, allowing for quicker and well-informed decision making
Controls / human review
ATO: Yes; PIA: not publicly available
Data needed
use a dataset of previously submitted IND applications, which provided a comprehensive understanding of the types of data and information included in these submission. Feedback and annotations from experienced RPMs and reviewers on a subset of the historical submission, which helps fine-tune the model's understanding of what constitutes a high-quality submission.