Melissa D. McCradden

发表

Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning

Anna Goldenberg, Shalmali Joshi, Mjaye Mazwi, 2020, J. Am. Medical Informatics Assoc..

Ethical limitations of algorithmic fairness solutions in health care machine learning.

Shalmali Joshi, Mjaye Mazwi, James A. Anderson, 2020, The Lancet. Digital health.

Ethics methods are required as part of reporting guidelines for artificial intelligence in healthcare

Melissa D. McCradden, S. Rose, D. Moher, 2022, Nature Machine Intelligence.

University of Birmingham Reporting guidelines for clinical trial reports for interventions involving artificial intelligence

Melissa D. McCradden, Cecilia S Lee, Cecilia S. Lee, 2020 .

Up to the Challenge: Adapting Pediatric Intensive Care During a Global Pandemic

Melissa D. McCradden, E. Gilfoyle, S. Ibrahim, 2022, Frontiers in Pediatrics.

Questioning Assumptions About Vulnerability in Psychiatric Patients

Melissa D. McCradden, M. Cusimano, M. McCradden, 2018, AJOB Neuroscience.

An integration engineering framework for machine learning in healthcare

Melissa D. McCradden, D. Eytan, P. Laussen, 2022, Frontiers in Digital Health.

Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study.

Melissa D. McCradden, M. Cusimano, Ashirbani Saha, 2020, CMAJ open.

The silent trial - the bridge between bench-to-bedside clinical AI applications

Melissa D. McCradden, L. Erdman, Adree Khondker, 2022, Frontiers in Digital Health.

Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence

Melissa D. McCradden, Birgit B Ertl-Wagner, A. Biswas, 2022, Pediatric Radiology.