McCradden, Melissa D.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 1,421

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

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 933

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · BMJ

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 665

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., et al. (2020) · The Lancet Digital Health

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 555

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Ashrafian, H., Beam, A. L., Collins, G. S., Darzi, A., Deeks, J. J., et al. (2020) · The Lancet Digital Health

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 486

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 352

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 318

Ethical limitations of algorithmic fairness solutions in health care machine learning

McCradden, M. D., Joshi, S., Mazwi, M., & Anderson, J. A. (2020) · The Lancet Digital Health

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 291

Ambiguous identities of drugs and people: A scoping review of opioid-related stigma

McCradden, M. D., Vasileva, D., Orchanian-Cheff, A., & Buchman, D. Z. (2019) · International Journal of Drug Policy

Opioid Use Disorder TreatmentSubstance Abuse Treatment and Outcomes

Cited by 128

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

McCradden, M. D., Joshi, S., Anderson, J. A., Mazwi, M., Goldenberg, A., & Zlotnik Shaul, R. (2020) · Journal of the American Medical Informatics Association

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 111

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

McCradden, M. D., Baba, A., Saha, A., Ahmad, S., Boparai, K., Fadaiefard, P., & Cusimano, M. D. (2020) · CMAJ Open

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 101

Evidence, ethics and the promise of artificial intelligence in psychiatry

McCradden, M., Hui, K., & Buchman, D. Z. (2023) · Journal of Medical Ethics

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 97

Clinical research underlies ethical integration of healthcare artificial intelligence

McCradden, M. D., Stephenson, E. A., & Anderson, J. A. (2020) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 88

Tackling bias in AI health datasets through the STANDING TOGETHER initiative

Ganapathi, S., Palmer, J., Alderman, J. E., Calvert, M., Espinoza, C., Gath, J., Ghassemi, M., Heller, K., Mckay, F., Karthikesalingam, A., et al. (2022) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 82

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

Kwong, J. C. C., Erdman, L., Khondker, A., Skreta, M., Goldenberg, A., McCradden, M. D., Lorenzo, A. J., & Rickard, M. (2022) · Frontiers in Digital Health

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 73

Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance

Herington, J., McCradden, M. D., Creel, K., Boellaard, R., Jones, E. C., Jha, A. K., Rahmim, A., Scott, P. J. H., Sunderland, J. J., Wahl, R. L., et al. (2023) · Journal of Nuclear Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 61

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

Martindale, A. P. L., Llewellyn, C. D., de Visser, R. O., Ng, B., Ngai, V., Kale, A. U., di Ruffano, L. F., Golub, R. M., Collins, G. S., Moher, D., et al. (2024) · Nature Communications

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 58

What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning

Mccradden, M., Odusi, O., Joshi, S., Akrout, I., Ndlovu, K., Glocker, B., Maicas, G., Liu, X., Mazwi, M., Garnett, T., et al. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 33

A scoping review of silent trials for medical artificial intelligence

Tikhomirov, L., Semmler, C., Prizant, N., Bhasin, S., Kenyon, G., van der Vegt, A., Erdman, L., Kurian, N. C., Thompson, H., Palmer, L. J., et al. (2026) · Nature Health

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 17