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Exam AIGP topic 1 question 61 discussion

Actual exam question from IAPP's AIGP
Question #: 61
Topic #: 1
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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?

  • A. Combine only anonymized data.
  • B. Secure the combined data sets.
  • C. Perform a quality assessment.
  • D. Review the terms of use.
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Suggested Answer: D 🗳️

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243426b
1 week, 3 days ago
Selected Answer: C
In the design phase, the most important step in gathering the data from the clinical research partner is: C. Perform a quality assessment. Ensuring the quality of external clinical data is crucial for accuracy, reliability, and fairness in AI healthcare solutions. Data quality checks address completeness, representativeness, accuracy, and consistency, and help detect issues such as missing values, biases, and anomalies before combining datasets for algorithm development. This is emphasized as a best practice for healthcare AI projects, since poor data quality can undermine model performance and compromise patient outcomes
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