DLS AI Assistant tool

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-05 (month precision)Source: May-25

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Administrative Functions

Operational functions

Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

Regulatory laboratory compliance has become increasingly complex, requiring scientists, lab personnel, and managers to understand laboratory quality regulations like the Clinical Laboratory Improvement Amendments (CLIA). The DLS AI Assistant is a tool designed to help scientists and lab workers by providing relevant guidance, such as the internal DLS Policy and Procedures Manual (DLS PPM) and external CLIA and International Organization for Standardization (ISO) regulations for laboratory quality. It offers personalized responses based on the user's level of expertise. In the final stage of the project, we plan to add a feature that will automatically evaluate Standard Operating Procedures (SOPs) to check if they comply with all relevant documents. However, the DLS AI Assistant is meant to assist and does not dictate compliance.

Expected benefits

We estimate that 5-10% of all time spent within the DLS is focused on compliance efforts, such as documentation, training sessions, and audit preparations and participation. The DLS AI Assistant tool supports these quality improvement efforts by helping staff understand and follow laboratory regulations more efficiently. The goal is to streamline the review of compliance efforts without compromising quality. Additional benefits include increased harmonization and reduced time spent on evaluating edge cases. For the CDC, this tool adds another layer of checks and balances and enhances knowledge sharing, ultimately leading to better and more accurate laboratory methods.

System outputs

Text-based information includes regulatory compliance details from both internal sources, such as the DLS Policy and Procedures Manual (DLS PPM), and external sources, like CLIA and International Organization for Standardization (ISO) regulations for laboratory quality.

Technology

AI classification

Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.

System name(s)

EDAV (Enterprise Data Analytics and Visualization Platform)

Custom-developed code

No

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported