Melissa McCradden

University of Adelaide

Australia

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Articles (20)

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

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Year:

2025

CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare

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Year:

2025

Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations

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Year:

2025

Machine learning used to study risk factors for chronic diseases: A scoping review.

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Year:

2025

What makes a ‘good’ decision with artificial intelligence? A grounded theory study in paediatric care

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Year:

2025

Can an accurate model be bad?

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Year:

2025

Harnessing technology in pediatric nursing: Balancing innovation, equity and sustainability.

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Year:

2025

Diversity and Inclusion Within Datasets in Heart Failure: A Systematic Review.

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Year:

2025

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

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Year:

2024

A causal perspective on dataset bias in machine learning for medical imaging

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Year:

2024

Explaining decisions without explainability? Artificial intelligence and medicolegal accountability

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Year:

2024

Complexities in Capacity Assessment for Persons with Severe and Enduring Anorexia

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Year:

2024

When the Model Trains You: Induced Belief Revision and Its Implications on Artificial Intelligence Research and Patient Care — A Case Study on Predicting Obstructive Hydronephrosis in Children

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Year:

2024

Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

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Year:

2024

Exploring patient and public participation in the STANDING Together initiative for AI in healthcare

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Year:

2024

Evidence, ethics and the promise of artificial intelligence in psychiatry

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Year:

2023

A normative framework for artificial intelligence as a sociotechnical system in healthcare

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Year:

2023

The value of standards for health datasets in artificial intelligence-based applications

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Year:

2023

Patient wisdom should be incorporated into health AI to avoid algorithmic paternalism

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Year:

2023

Response to Open Peer Commentaries: On Social Harms, Big Tech, and Institutional Accountability

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Year:

2022

Collaborators (9)

Karin Verspoor

AUSTRALIA

Marzyeh Ghassemi

Massachusetts Institute of Technology

UNITED STATES

Carrie Llewellyn

University of Sussex

UNITED KINGDOM

Alexander Peter Landy Martindale

King's College Hospital NHS Foundation Trust

UNITED KINGDOM

Daniel Z. Buchman

University of Toronto

UNITED STATES

Johanna Damen

Assistant Professor

University Medical Center Groningen

NETHERLANDS

Alex London

Professor

Carnegie Mellon University

UNITED STATES

Ozan Oktay

Imperial College London

UNITED KINGDOM

Lotty Hooft

Head of department/full professor/director Cochrane Netherlands

University Medical Center Utrecht, Utrecht University

NETHERLANDS

Melissa McCradden

Research Interests

Explore related searches

Contact this professor

Articles (20)

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare

Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations

Machine learning used to study risk factors for chronic diseases: A scoping review.

What makes a ‘good’ decision with artificial intelligence? A grounded theory study in paediatric care

Can an accurate model be bad?

Harnessing technology in pediatric nursing: Balancing innovation, equity and sustainability.

Diversity and Inclusion Within Datasets in Heart Failure: A Systematic Review.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

A causal perspective on dataset bias in machine learning for medical imaging

Explaining decisions without explainability? Artificial intelligence and medicolegal accountability

Complexities in Capacity Assessment for Persons with Severe and Enduring Anorexia

When the Model Trains You: Induced Belief Revision and Its Implications on Artificial Intelligence Research and Patient Care — A Case Study on Predicting Obstructive Hydronephrosis in Children

Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

Exploring patient and public participation in the STANDING Together initiative for AI in healthcare

Evidence, ethics and the promise of artificial intelligence in psychiatry

A normative framework for artificial intelligence as a sociotechnical system in healthcare

The value of standards for health datasets in artificial intelligence-based applications

Patient wisdom should be incorporated into health AI to avoid algorithmic paternalism

Response to Open Peer Commentaries: On Social Harms, Big Tech, and Institutional Accountability

Collaborators (9)

Karin Verspoor

Marzyeh Ghassemi

Carrie Llewellyn

Alexander Peter Landy Martindale

Daniel Z. Buchman

Johanna Damen

Alex London

Ozan Oktay

Lotty Hooft

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