AIGP · Question #136
CASE STUDY Please use the following to answer the next question: A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in…
The correct answer is C. Perform a quality assessment. Performing a quality assessment (C) is the critical first step when ingesting external data into an AI training pipeline. Before the licensed clinical data can be used to train the cancer detection algorithm, the team must evaluate it for completeness, accuracy, consistency…
Question
CASE STUDY Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records to a radiologist for secondary review pursuant to agreed-upon criteria (e.g., a confidence score below a threshold). To date, the healthcare network has:
- Defined its AI ethical principles.
- Conducted discovery to identify the intended uses and success
criteria for the system.
- Established an AI risk committee.
- Assembled a cross-functional team with clear roles and
responsibilities.
- Created policies and procedures to document standards, workflows,
timelines and risk thresholds during the project. The healthcare network intends to retain a cloud provider to host the solution. It also intends to retain a large consulting firm to supplement its small data science team and help develop the algorithm using the healthcare network’s existing data and de-identified data that is licensed from a large US clinical research partner. In the design phase, which of the following steps is most important in gathering the data from the clinical research partner?
Options
- ACombine only anonymized data.
- BSecure the combined data sets.
- CPerform a quality assessment.
- DReview the terms of use.
How the community answered
(29 responses)- A7% (2)
- B3% (1)
- C86% (25)
- D3% (1)
Explanation
Performing a quality assessment (C) is the critical first step when ingesting external data into an AI training pipeline. Before the licensed clinical data can be used to train the cancer detection algorithm, the team must evaluate it for completeness, accuracy, consistency, and representativeness of the target population. Garbage in, garbage out - a flawed dataset will silently corrupt the model's performance, and in a clinical setting, that means missed diagnoses or false positives with real patient harm.
Why the distractors are wrong:
- A (Combine only anonymized data) - The data is already described as de-identified; re-stating that as a step conflates a pre-condition with a design action. It also doesn't address whether the data is actually useful.
- B (Secure the combined data sets) - Security is essential but is an ongoing infrastructure concern, not the first design-phase step for a newly received external dataset. You can't meaningfully secure what you haven't yet validated.
- D (Review the terms of use) - This should have happened before data was licensed, during procurement/contracting. By the design phase, terms are already agreed upon; reviewing them again isn't the most impactful step.
Memory tip: Think "GIGO gate" - Quality Assessment is the checkpoint that stops bad data from entering the pipeline. In healthcare AI, data quality is patient safety, so it must come before combining or modeling.
Topics
Community Discussion
No community discussion yet for this question.