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Safe AI in Pharmaceuticals: Validation, Data Integrity and Regulated AI in Life Sciences

Where AI Is Used Across Discovery, Trials, Manufacturing and Safety

Quiz: Where AI Is Used Across Discovery, Trials, Manufacturing and Safety

GxP Boundaries: Which AI Uses Are Regulated

Quiz: GxP Boundaries: Which AI Uses Are Regulated

Computerised System Validation Applied to Machine Learning

Quiz: Computerised System Validation Applied to Machine Learning

Data Integrity Principles and Audit Trails for AI Systems

Quiz: Data Integrity Principles and Audit Trails for AI Systems

Risk Based Validation and Supplier Assessment

Quiz: Risk Based Validation and Supplier Assessment

AI in Clinical Trial Design, Recruitment and Monitoring

Quiz: AI in Clinical Trial Design, Recruitment and Monitoring

Regulatory Expectations for AI in Marketing Authorisations

Quiz: Regulatory Expectations for AI in Marketing Authorisations

Pharmacovigilance: Signal Detection and Case Processing With AI

Quiz: Pharmacovigilance: Signal Detection and Case Processing With AI

Manufacturing Process Control and Batch Release Decisions

Quiz: Manufacturing Process Control and Batch Release Decisions

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Quiz: Data Integrity Principles and Audit Trails for AI Systems

Safe AI in Pharmaceuticals: Validation, Data Integrity and Regulated AI in Life Sciences
Data Integrity Principles and Audit Trails for AI Systems
Quiz: Data Integrity Principles and Audit Trails for AI Systems

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Question 1 of 5

1. Question

What is a key requirement for AI systems in pharmaceutical environments regarding data integrity?

AI systems should only process data from clinical trials
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AI systems must maintain accurate, complete, and consistent information throughout their lifecycle
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AI systems do not need to follow data integrity principles if they are machine learning models
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AI systems should allow any personnel to access and modify data
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Question 2 of 5

2. Question

Which of the following is an example of proper access control in AI systems?

A clinical data manager modifies AI model parameters directly
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Only data scientists can access clinical data for model refinement
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Any staff member can delete AI training datasets
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Unauthorised personnel can access AI decision-making algorithms
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Question 3 of 5

3. Question

Why must audit trails be maintained for AI decision-making in pharmaceutical settings?

To reduce the cost of data storage
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To support regulatory inspections and internal investigations
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To allow unlimited access to AI models
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To prevent data from being stored in secure repositories
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Question 4 of 5

4. Question

What is one benefit of automatic audit trail generation in AI systems?

It increases the risk of data breaches
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It helps maintain consistency and reduces human error
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It removes the need for data validation checks
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It makes data access less secure
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Question 5 of 5

5. Question

Which of the following is a recommended strategy for maintaining AI data integrity?

Avoiding regular audits of AI systems
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Providing staff training on data integrity responsibilities
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Storing data without version control
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Using unvalidated data sources for AI models
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