Ethical AI in Orthodontic Research

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AI ethics in research

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Yomna M.Y.

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Ethical AI in Orthodontic Research


Ethical AI in Orthodontic ResearchVersión en línea AI ethics in research por Yomna M.Y. 1 Which practice best protects participant data in AI research? a Ignoring data minimization and retention limits b Using only one data source c De-identification and secure data handling d Public release of raw data without consent 2 What reduces bias in AI models for research? a Disregarding demographic variables b Diverse training data and bias auditing c Using a single source dataset d Assuming models are unbiased by design 3 Who typically owns AI-generated research outputs? a Public domain by default b Always the patient involved c Always the AI developer d Depends on policy; researchers or institutions 4 What is essential for transparency in AI in clinical research? a Avoid explaining methods to prevent misinterpretation b Document model choice, data provenance, and limitations c Hide data sources to protect privacy d Only share final results, not methods 5 What practice supports responsible AI reporting? a Rely on AI as sole evidence without human oversight b Disclose model limitations and uncertainties c Overstate AI capabilities d Conceal performance metrics 6 Which risk is most associated with AI on imaging data? a Re-identification from derived features b Data loss due to hardware failure c No privacy risk if data is anonymized d Increased physical harm from devices 7 Which step helps mitigate bias after deployment? a Ignore performance in minority groups b Post-deployment monitoring for disparate impact c Remove all demographic data from models d Limit testing to training data only 8 Can AI-generated content be copyrighted? a No, never possible b Yes in some regions, depending on authorship rules c Always belongs to AI d Always public domain 9 Is broad consent sufficient for all AI research use? a Yes, broad consent covers all uses b Consent is not needed for AI c Only consent from researchers is needed d No; consent should be specific and ongoing 10 What should be disclosed about data sources? a Data provenance and consent status b Only the data format c Data source speed and size only d No disclosure needed if anonymized Feedback 1 De-identification protects privacy; avoid exposing identifiable data. 2 Diverse data and audits help identify and minimize bias. 3 Ownership hinges on policy, funder, and agreements; vary by jurisdiction. 4 Clear documentation enables replication and critique. 5 Honest reporting prevents overclaiming AI findings. 6 Even de-identified images can reveal identities via features. 7 Ongoing monitoring reveals real-world bias. 8 Copyright varies; often requires human authorship. 9 Dynamic consent and clear scope are needed for evolving AI use. 10 Provenance and consent status ensure ethical data use.

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Ethical AI in Orthodontic Research

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Ethical AI in Orthodontic ResearchVersión en línea

por Yomna M.Y.

Which practice best protects participant data in AI research?

What reduces bias in AI models for research?

Who typically owns AI-generated research outputs?

What is essential for transparency in AI in clinical research?

What practice supports responsible AI reporting?

Which risk is most associated with AI on imaging data?

Which step helps mitigate bias after deployment?

Can AI-generated content be copyrighted?

Is broad consent sufficient for all AI research use?

What should be disclosed about data sources?

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