Melissa McCradden

Dr Melissa McCradden

Externally-Funded Senior Research Fellow

School of Computer Science and Information Technology

College of Engineering and Information Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


Dr. Melissa McCradden is the Artificial Intelligence Director and Deputy Research Director with the Women's and Children's Health Network (WCHN). She is a Deputy Director and The Hospital Research Foundation (THRF) Group Fellow at the Australian Institute for Machine Learning (University of Adelaide). She holds an Adjunct Scientist appointment with the SickKids Research Institute and serves as a member of the SickKids Research Ethics Board in Toronto, Canada. Dr McCradden is an Associate Editor for the journal Research Ethics and is on the Advisory Board for the journal Patterns. She is a member of the World Health Organisation(WHO)/ITU's Focus Group on Clinical Evaluation of AI for Health (FG-AI4H).Dr. McCradden's expertise is in the development of ethical frameworks derived from clinical and technical knowledge grounded in policy, law, and moral theory. She has published on algorithmic bias, responsible clinical evaluation of healthcare machine learning, and clinical integration of AI in journals such as Nature Medicine, JAMA, Lancet Digital Health, JAMIA, and NEJM-AI. Dr. McCradden participates in a plethora of international reporting guideline initiatives including CONSORT and SPIRIT AI (clinical trials), DECIDE-AI (first-in-human trials) and many other international efforts in enhancing regulatory science for health AI. She is listed among the 100 Brilliant Women in AI Ethics and was a Top 40 Under 40 Finalist by InDaily SA (2025).Dr. McCradden holds a PhD in Neuroscience from McMaster University, a Master of Health Sciences in Bioethics from the University of Toronto, and was the inaugural Postdoctoral Fellow in AI Ethics at SickKids and Vector Institute, jointly trained in computer science and bioethics.

Dr. McCradden's work focuses on evidence-based frameworks for the ethical evaluation of novel technologies, particularly in real-world applications in hospital and community health settings. She employs both qualitative and quantitative methodologies to explore the most imminent ethical challenges at the edge of emerging technological frontiers. Her goal in all of her work is to develop and promote practices that enable robust evidence standards to safeguard equity and compassion in healthcare.

Current projects include
Project CANAIRI: https://www.adelaide.edu.au/aiml/our-key-initiatives/project-canairi 
Youths' perspectives on AI (study completed in Canada, now initiated in Australia): https://healthydebate.ca/2021/11/topic/children-ai-health-care/ 
Promoting health AI literacy using co-design and strengths-based learning among Australian consumers

Date Position Institution name
2025 - ongoing Deputy Research Director Women's and Children's Health Network
2024 - ongoing Deputy Director Australian Institute for Machine Learning
2024 - ongoing THRF Clinical Research Fellow Australian Institute for Machine Learning
2024 - ongoing Adjunct Scientist SickKids Research Institute
2024 - ongoing AI Director Women's and Children's Hospital Network
2022 - 2024 Associate Scientist SickKids Research Institute
2022 - 2024 John and Melinda Thompson Director of AI in Medicine The Hospital for Sick Children (SickKids)
2020 - 2023 Assistant Professor University of Toronto
2019 - 2024 Bioethicist The Hospital for Sick Children

Language Competency
French Can read, write, speak, understand spoken and peer review

Date Institution name Country Title
McMaster University Canada PhD
University of Toronto Canada MHSc

Date Title Institution Country
2017 - 2018 Postdoctoral fellow in neurosurgery and injury prevention Unity Health Toronto Canada
AI Ethics Fellow Vector Institute/SickKids Canada

Year Citation
2025 Laws, E., Palmer, J., Alderman, J., Sharma, O., Ngai, V., Salisbury, T., . . . Liu, X. (2025). Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review. Clinical Imaging, 118, 44 pages.
DOI Scopus6 WoS6 Europe PMC6
2025 Alderman, J. E., Palmer, J., Laws, E., McCradden, M. D., Ordish, J., Ghassemi, M., . . . Liu, X. (2025). Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. The Lancet Digital Health, 7(1), e64-e88.
DOI Scopus45 WoS39 Europe PMC30
2025 Alderman, J. E., Palmer, J., Laws, E., McCradden, M. D., Ordish, J., Ghassemi, M., . . . Liu, X. (2025). Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations. NEJM AI, 2(1).
DOI
2025 Morley, J., Hine, E., Roberts, H., Sirbu, R., Ashrafian, H., Blease, C., . . . Floridi, L. (2025). Global Health in the Age of AI: Charting a Course for Ethical Implementation and Societal Benefit. MINDS AND MACHINES, 35(3), 35 pages.
DOI Scopus1 WoS1
2025 Laws, E., Palmer, J., Alderman, J., Sharma, O., Ngai, V., Salisbury, T., . . . Liu, X. (2025). Corrigendum to “Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review” [Clin Imaging 118 (2025) 110369] (Clinical Imaging (2025) 118, (S0899707124002997), (10.1016/j.clinimag.2024.110369)). Clinical Imaging, 125, 4 pages.
DOI
2025 Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Calster, B. V., . . . Logullo, P. (2025). TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.. Ewha Med J, 48(3), e48.
DOI WoS3 Europe PMC2
2025 Huo, B., Collins, G. S., Chartash, D., Thirunavukarasu, A. J., Flanagin, A., Iorio, A., . . . Guyatt, G. H. (2025). Reporting Guideline for Chatbot Health Advice StudiesThe CHART Statement. JAMA NETWORK OPEN, 8(8), 14 pages.
DOI Scopus3 WoS3 Europe PMC2
2025 Huo, B., Collins, G., Chartash, D., Thirunavukarasu, A., Flanagin, A., Iorio, A., . . . Guyatt, G. (2025). Reporting guideline for Chatbot Health Advice studies: the CHART statement. BMC Medicine, 23(1), 447.
DOI
2025 Huo, B., Collins, G., Chartash, D., Thirunavukarasu, A., Flanagin, A., Iorio, A., . . . Guyatt, G. (2025). Reporting guideline for chatbot health advice studies: the Chatbot Assessment Reporting Tool (CHART) statement. BJS-BRITISH JOURNAL OF SURGERY, 112(8), 10 pages.
DOI
2025 Huo, B., Thirunavukarasu, A. J., Collins, G. S., Chartash, D., Flanagin, A., Iorio, A., . . . Guyatt, G. (2025). Reporting guidelines for chatbot health advice studies: explanation and elaboration for the Chatbot Assessment Reporting Tool (CHART). BMJ, 390, 19 pages.
DOI Scopus4 WoS8 Europe PMC5
2025 Xiao, L., Djordjevic, D., Kang, S., Gonorazky, H., Chiang, J., Ambreen, M., . . . Mccradden, M. D. (2025). Disease-modifying therapies for spinal muscular atrophy: Family experience, ethical considerations, and the role of social determinants of health. JOURNAL OF NEUROMUSCULAR DISEASES, 9 pages.
DOI
2025 Tjoeng, Y. L., Mazwi, M., Goodwin, A., McCradden, M. D., & Chan, T. (2025). Creating Data Systems to Promote Health Equity. Pediatrics, 156(Suppl 1), S109-S115.
DOI
2025 Rafique, D., Liu, X., Gong, B., Belsito, L., McCradden, M. D., Mazwi, M. L., . . . Khalvati, F. (2025). Predicting pediatric diagnostic imaging patient no-show and extended wait-times using LLMs, regression, and tree based models. Frontiers in Artificial Intelligence, 8, 16 pages.
DOI
2025 Huo, B., Collins, G., Chartash, D., Thirunavukarasu, A., Flanagin, A., Iorio, A., . . . Guyatt, G. (2025). Reporting guideline for chatbot health advice studies: the Chatbot Assessment Reporting Tool (CHART) statement. BMJ Medicine, 4(1), e001632.
DOI Scopus4 WoS1
2025 McCradden, M. D., Mazwi, M. L., & Oakden-Rayner, L. (2025). Can an accurate model be bad?. Patterns, 6(4), 101205.
DOI Scopus1 WoS1 Europe PMC1
2025 Plummer, K., Pope, N., McCradden, M. D., Ullman, A. J., Foster, M., Stinson, J. N., & Griffin, B. (2025). Harnessing technology in pediatric nursing: Balancing innovation, equity and sustainability. Journal of Pediatric Nursing, 82, A1-A4.
DOI
2025 Laws, E., Charalambides, M., Vadera, S., Keller, E., Alderman, J., Blackboro, B., . . . Liu, X. (2025). Diversity and Inclusion Within Datasets in Heart Failure: A Systematic Review. JACC: Advances, 4(3), 101610.
DOI Scopus1 WoS1
2025 McCradden, M. D., London, A. J., Gichoya, J. W., Sendak, M., Erdman, L., Stedman, I., . . . Liu, X. (2025). CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare.. Nat Med, 31(1), 9-11.
DOI Scopus7 WoS7 Europe PMC5
2025 McCradden, M. D., Thai, K., Assadi, A., Tonekaboni, S., Stedman, I., Joshi, S., . . . Goldenberg, A. (2025). What makes a ‘good’ decision with artificial intelligence? A grounded theory study in paediatric care. BMJ Evidence-Based Medicine, 30(3), 183-193.
DOI Scopus1 WoS1 Europe PMC1
2025 Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., . . . van Smeden, M. (2025). PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods.. BMJ, 388, e082505.
DOI Scopus69 WoS55 Europe PMC52
2025 Holst, J. J., Schéele, C., Scherer, P. E., Jia, W., Segal, E., Slavov, N., . . . Jacobs, P. G. (2025). Artificial intelligence in metabolic research.. Nat Metab, 7(11), 2183-2186.
DOI Europe PMC1
2025 Pinto, A. D., Birdi, S., Durant, S., Rabet, R., Parekh, R., Ali, S., . . . Mishra, S. (2025). Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation.. JMIR Public Health Surveill, 11, e68952.
DOI
2024 Birdi, S., Rabet, R., Durant, S., Patel, A., Vosoughi, T., Shergill, M., . . . Pinto, A. D. (2024). Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review. BMC Public Health, 24(1), 16 pages.
DOI Scopus3 WoS2 Europe PMC3
2024 Kwong, J. C. C., Nguyen, D. -D., Khondker, A., Kim, J. K., Johnson, A. E. W., McCradden, M. M., . . . Rickard, M. (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. NEJM AI, 1(2).
DOI
2024 Gath, J., Leung, C., Adebajo, A. O., Beng, J., Arora, A., Alderman, J. E., . . . Liu, X. (2024). Exploring patient and public participation in the STANDING Together initiative for AI in healthcare.. Nature medicine, 30(12), 3406-3408.
DOI
2024 Tikhomirov, L., Semmler, C., McCradden, M., Searston, R., Ghassemi, M., & Oakden-Rayner, L. (2024). Medical artificial intelligence for clinicians: the lost cognitive perspective. The Lancet: Digital Health, 6(8), e589-e594.
DOI Scopus20 WoS15 Europe PMC10
2024 Martindale, A. P. L., Llewellyn, C. D., de Visser, R. O., Ng, B., Ngai, V., Kale, A. U., . . . Liu, X. (2024). Author Correction: Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.. Nat Commun, 15(1), 6376.
DOI Scopus1 WoS1 Europe PMC1
2024 Turner, L. M., & McCradden, M. D. (2024). Complexities in Capacity Assessment for Persons with Severe and Enduring Anorexia.. Am J Bioeth, 24(8), 111-113.
DOI Scopus1 WoS1
2024 McCradden, M. D., & Stedman, I. (2024). Explaining decisions without explainability? Artificial intelligence and medicolegal accountability. Future Healthcare Journal, 11(3), 100171.
DOI Europe PMC2
2024 Ning, Y., Liu, X., Collins, G. S., Moons, K. G. M., McCradden, M., Ting, D. S. W., . . . Liu, N. (2024). An ethics assessment tool for artificial intelligence implementation in healthcare: CARE-AI. Nature Medicine, 30(11), 2 pages.
DOI Scopus12 WoS10 Europe PMC10
2024 Alderman, J. E., Charalambides, M., Sachdeva, G., Laws, E., Palmer, J., Lee, E., . . . Liu, X. (2024). Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development—a systematic review. Lancet Digital Health, 6(11), e827-e847.
DOI Scopus7
2024 Martindale, A. P. L., Ng, B., Ngai, V., Kale, A. U., Ferrante di Ruffano, L., Golub, R. M., . . . Liu, X. (2024). Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nature Communications, 15(1), 1619-1-1619-11.
DOI Scopus24 WoS23 Europe PMC19
2024 Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., . . . Logullo, P. (2024). TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.. BMJ, 385, 14 pages.
DOI Scopus1004 WoS854 Europe PMC720
2024 Anderson, K. (2024). Andrew Caillard: <i>The Australian Ark</i>:<i> The Story of Australian Wine</i>. JOURNAL OF WINE ECONOMICS, 6(11), 4 pages.
DOI WoS7 Europe PMC7
2024 Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., Denniston, A. K., Chan, A. W., . . . Rowley, S. (2024). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Revista Panamericana de Salud Publica/Pan American Journal of Public Health, 48, 15 pages.
DOI Scopus2 WoS1 Europe PMC1
2024 Rivera, S. C., Liu, X., Chan, A. W., Denniston, A. K., Calvert, M. J., Darzi, A., . . . Rowley, S. (2024). Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Revista Panamericana de Salud Publica/Pan American Journal of Public Health, 48, e12.
DOI Scopus1 Europe PMC1
2024 Ferretti, A., Adjei, K. K., Ali, J., Atuire, C., Ayuk, B. T., Banougnin, B. H., . . . Vayena, E. (2024). Digital tools for youth health promotion: principles, policies and practices in sub-Saharan Africa. Health Promotion International, 39(2), 11 pages.
DOI Scopus3 WoS4 Europe PMC2
2024 Jones, C., Castro, D. C., De Sousa Ribeiro, F., Oktay, O., McCradden, M., & Glocker, B. (2024). A causal perspective on dataset bias in machine learning for medical imaging. Nature Machine Intelligence, 6(2), 138-146.
DOI Scopus30 WoS21
2023 Herington, J., McCradden, M. D., Creel, K., Boellaard, R., Jones, E. C., Jha, A. K., . . . Saboury, B. (2023). Ethical considerations for artificial intelligence in medical imaging: Data collection, development, and evaluation. Journal of Nuclear Medicine, 64(12), 1848-1854.
DOI Scopus34 WoS22 Europe PMC21
2023 McCradden, M. D., Joshi, S., Anderson, J. A., & London, A. J. (2023). A normative framework for artificial intelligence as a sociotechnical system in healthcare. Patterns, 4(11), 9 pages.
DOI Scopus14 WoS13 Europe PMC6
2023 Herington, J., McCradden, M. D., Creel, K., Boellaard, R., Jones, E. C., Jha, A. K., . . . Saboury, B. (2023). Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance. Journal of Nuclear Medicine, 64(10), 1509-1515.
DOI Scopus35 WoS24 Europe PMC25
2023 Kwong, J. C. C., Khondker, A., Lajkosz, K., McDermott, M. B. A., Frigola, X. B., McCradden, M. D., . . . Johnson, A. E. W. (2023). APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Network Open, 6(9), 11 pages.
DOI Scopus67 WoS56 Europe PMC52
2023 McCradden, M. D. (2023). Ethics, First. American Journal of Bioethics, 23(9), 55-56.
DOI
2023 Dhiman, P., Whittle, R., Van Calster, B., Ghassemi, M., Liu, X., McCradden, M. D., . . . Collins, G. S. (2023). The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols. Nature Machine Intelligence, 5(8), 816-817.
DOI Scopus9 WoS11
2023 McCradden, M., Hui, K., & Buchman, D. Z. (2023). Evidence, ethics and the promise of artificial intelligence in psychiatry. Journal of Medical Ethics, 49(8), 573-579.
DOI Scopus65 WoS49 Europe PMC38
2023 Xiao, L., Kang, S., Djordjevic, D., Gonorazky, H., Chiang, J., Ambreen, M., . . . Amin, R. (2023). Understanding caregiver experiences with disease-modifying therapies for spinal muscular atrophy: A qualitative study. Archives of Disease in Childhood, 108(11), 929-934.
DOI Scopus8 WoS8 Europe PMC8
2023 Bradshaw, T. J., McCradden, M. D., Jha, A. K., Dutta, J., Saboury, B., Siegel, E. L., & Rahmim, A. (2023). Artificial Intelligence Algorithms Need to Be Explainable—or Do They?. Journal of Nuclear Medicine, 64(6), 976-977.
DOI Scopus11 WoS7 Europe PMC7
2023 Katzman, D. K., & McCradden, M. D. (2023). Capacity for Preferences: Adolescents With AN-PLUS. Journal of Adolescent Health, 72(6), 827-828.
DOI Scopus3 WoS2 Europe PMC2
2023 Taheri-Shirazi, M., Namdar, K., Ling, K., Karmali, K., McCradden, M. D., Lee, W., & Khalvati, F. (2023). Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning. Frontiers in Public Health, 11, 968319.
DOI Scopus5 WoS4 Europe PMC4
2023 McCradden, M. D., & Kirsch, R. E. (2023). Patient wisdom should be incorporated into health AI to avoid algorithmic paternalism. Nature Medicine, 29(4), 765-766.
DOI Scopus16 WoS13 Europe PMC11
2023 Tsiandoulas, K., McSheffrey, G., Fleming, L., Rawal, V., Fadel, M. P., Katzman, D. K., & McCradden, M. D. (2023). Ethical tensions in the treatment of youth with severe anorexia nervosa. The Lancet Child and Adolescent Health, 7(1), 69-76.
DOI Scopus15 WoS13 Europe PMC11
2023 Arora, A., Alderman, J. E., Palmer, J., Ganapathi, S., Laws, E., McCradden, M. D., . . . Liu, X. (2023). The value of standards for health datasets in artificial intelligence-based applications. Nature Medicine, 29(11), 2929-2938.
DOI Scopus164 WoS131 Europe PMC106
2023 Thai, K., Tsiandoulas, K. H., Stephenson, E. A., Menna-Dack, D., Zlotnik Shaul, R., Anderson, J. A., . . . McCradden, M. D. (2023). Perspectives of Youths on the Ethical Use of Artificial Intelligence in Health Care Research and Clinical Care. JAMA network open, 6(5), 10 pages.
DOI Scopus33 WoS29 Europe PMC17
2023 McCradden, M. D. (2023). Partnering with children and youth to advance artificial intelligence in healthcare. Pediatric Research, 93(2), 284-286.
DOI Scopus2 WoS2 Europe PMC1
2023 Cruz Rivera, S., Liu, X., Chan, A. -W., Denniston, A. K., Calvert, M. J., Grupo de Trabajo SPIRIT-AI y CONSORT-AI., . . . Grupo de Consenso SPIRIT-AI y CONSORT-AI. (2023). [Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI].. Revista panamericana de salud publica = Pan American journal of public health, 47, e149.
DOI Europe PMC1
2022 Anderson, J. A., McCradden, M. D., & Stephenson, E. A. (2022). Response to Open Peer Commentaries: On Social Harms, Big Tech, and Institutional Accountability. American Journal of Bioethics, 22(10), W6-W8.
DOI Scopus1 WoS1 Europe PMC1
2022 McCradden, M. D., Anderson, J. A., A. Stephenson, E., Drysdale, E., Erdman, L., Goldenberg, A., & Zlotnik Shaul, R. (2022). A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning. American Journal of Bioethics, 22(5), 8-22.
DOI Scopus84 WoS70 Europe PMC56
2022 Sounderajah, V., McCradden, M. D., Liu, X., Rose, S., Ashrafian, H., Collins, G. S., . . . Darzi, A. (2022). Ethics methods are required as part of reporting guidelines for artificial intelligence in healthcare. Nature Machine Intelligence, 4(4), 316-317.
DOI Scopus11 WoS8
2022 McCradden, M. (2022). Melissa McCradden. CELL REPORTS MEDICINE, 3(12), 2 pages.
DOI
2022 Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022). The medical algorithmic audit (vol 4, pg e384, 2022). LANCET DIGITAL HEALTH, 4(6), E405.
DOI
2022 Kwong, J. C. C., Erdman, L., Khondker, A., Skreta, M., Goldenberg, A., McCradden, M. D., . . . Rickard, M. (2022). The silent trial - the bridge between bench-to-bedside clinical AI applications. Frontiers in Digital Health, 4, 10 pages.
DOI Scopus33 WoS30 Europe PMC21
2022 Assadi, A., Laussen, P. C., Goodwin, A. J., Goodfellow, S., Dixon, W., Greer, R. W., . . . Mazwi, M. L. (2022). An integration engineering framework for machine learning in healthcare. Frontiers in Digital Health, 4, 12 pages.
DOI Scopus14 WoS9 Europe PMC8
2022 Pereira, M., Akinkugbe, O., Buckley, L., Gilfoyle, E., Ibrahim, S., McCradden, M., . . . Dryden-Palmer, K. (2022). Up to the Challenge: Adapting Pediatric Intensive Care During a Global Pandemic. Frontiers in Pediatrics, 10, 10 pages.
DOI Scopus4 WoS3 Europe PMC3
2022 Monah, S. R., Wagner, M. W., Biswas, A., Khalvati, F., Erdman, L. E., Amirabadi, A., . . . Ertl-Wagner, B. B. (2022). Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence. Pediatric Radiology, 52(11), 2111-2119.
DOI Scopus12 WoS10 Europe PMC5
2022 Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., . . . Mcculloch, P. (2022). Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. The BMJ, 377, e070904.
DOI Scopus178 WoS385 Europe PMC133
2022 Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022). The medical algorithmic audit.. Lancet Digit Health, 4(5), E384-E397.
DOI Scopus165 WoS144 Europe PMC103
2022 Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., . . . Militello, L. (2022). Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9). Nature Medicine, 28(10), 2218.
DOI Scopus8 WoS2 Europe PMC3
2022 Ganapathi, S., Palmer, J., Alderman, J. E., Calvert, M., Espinoza, C., Gath, J., . . . Liu, X. (2022). Tackling bias in AI health datasets through the STANDING Together initiative.. Nature medicine, 28(11), 2232-2233.
DOI Scopus61 WoS60 Europe PMC52
2022 Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., . . . Yoon, J. H. (2022). Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature Medicine, 28(5), 924-933.
DOI Scopus308 WoS44 Europe PMC254
2021 Baba, A., Saha, A., McCradden, M. D., Boparai, K., Zhang, S., Pirouzmand, F., . . . Cusimano, M. D. (2021). Development and validation of a patient-centered, meningioma-specific quality-of-life questionnaire. Journal of Neurosurgery, 135(6), 1685-1694.
DOI Scopus12 WoS10 Europe PMC10
2021 McCradden, M. D., & Chad, L. (2021). Screening for facial differences worldwide: equity and ethics. The Lancet Digital Health, 3(10), e615-e616.
DOI Scopus2 WoS2 Europe PMC1
2021 Sounderajah, V., Ashrafian, H., Rose, S., Shah, N. H., Ghassemi, M., Golub, R., . . . Darzi, A. (2021). A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nature Medicine, 27(10), 1663-1665.
DOI Scopus159 WoS149 Europe PMC134
2021 McCradden, M. D. (2021). When is accuracy off-target?. Translational Psychiatry, 11(1), 369.
DOI Scopus8 WoS8 Europe PMC5
2021 Zlotnik Shaul, R., Shaul, D., Anderson, J., & McCradden, M. (2021). The Gift in Precision Medicine: Unwrapping the Significance of Reciprocity and Generosity. American Journal of Bioethics, 21(4), 78-80.
DOI Scopus2 WoS4 Europe PMC2
2021 Helmers, A., McCradden, M., Kirsch, R., & Shaul, R. Z. (2021). Cracking the Code: COVID-19 and the Future of Professional Promises. American Journal of Bioethics, 21(1), 19-21.
DOI
2021 McCradden, M. D., Patel, E., & Chad, L. (2021). The point-of-care use of a facial phenotyping tool in the genetics clinic: An ethics tête-a-tête. American Journal of Medical Genetics, Part A, 185(2), 658-660.
DOI Scopus7 WoS7 Europe PMC3
2021 Shergill, M., Durant, S., Birdi, S., Rabet, R., Ziegler, C., Ali, S., . . . Pinto, A. D. (2021). Machine learning used to study risk factors for chronic diseases: A scoping review. CANADIAN JOURNAL OF PUBLIC HEALTH-REVUE CANADIENNE DE SANTE PUBLIQUE, 15 pages.
DOI Scopus3 WoS3 Europe PMC1
2020 McCradden, M. D., Joshi, S., Anderson, J. A., Mazwi, M., Goldenberg, A., & Shaul, R. Z. (2020). Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. Journal of the American Medical Informatics Association, 27(12), 2024-2027.
DOI Scopus78 WoS59 Europe PMC44
2020 McCradden, M. D., Baba, A., Saha, A., Ahmad, S., Boparai, K., Fadaiefard, P., & Cusimano, M. D. (2020). 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. CMAJ open, 8(1), E90-E95.
DOI Scopus70 Europe PMC47
2020 McCradden, M. D., Sarker, T., & Paprica, P. A. (2020). Conditionally positive: A qualitative study of public perceptions about using health data for artificial intelligence research. BMJ Open, 10(10), e039798.
DOI Scopus55 WoS52 Europe PMC42
2020 McCradden, M. D., Anderson, J. A., & Zlotnik Shaul, R. (2020). Accountability in the Machine Learning Pipeline: The Critical Role of Research Ethics Oversight. American Journal of Bioethics, 20(11), 40-42.
DOI Scopus8 WoS9 Europe PMC4
2020 McCradden, M. D., Stephenson, E. A., & Anderson, J. A. (2020). Clinical research underlies ethical integration of healthcare artificial intelligence. Nature Medicine, 26(9), 1325-1326.
DOI Scopus61 WoS51 Europe PMC37
2020 McCradden, M. D., Joshi, S., Mazwi, M., & Anderson, J. A. (2020). Ethical limitations of algorithmic fairness solutions in health care machine learning. The Lancet Digital Health, 2(5), e221-e223.
DOI Scopus170 WoS141 Europe PMC117
2020 Baba, A., McCradden, M. D., Rabski, J., & Cusimano, M. D. (2020). Determining the unmet needs of patients with intracranial meningioma - A qualitative assessment. Neuro-Oncology Practice, 7(2), 228-238.
DOI Scopus12 WoS13
2020 Cruz Rivera, S., Liu, X., Chan, A. W., Denniston, A. K., Calvert, M. J., Darzi, A., . . . Rowley, S. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nature Medicine, 26(9), 1351-1363.
DOI Scopus381 Europe PMC288
2020 Cruz Rivera, S., Liu, X., Chan, A. W., Denniston, A. K., Calvert, M. J., Ashrafian, H., . . . Yau, C. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. The Lancet Digital Health, 2(10), e549-e560.
DOI Scopus264 Europe PMC177
2020 Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., . . . Yau, C. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. The Lancet Digital Health, 2(10), e537-e548.
DOI Scopus254 Europe PMC201
2020 Rivera, S. C., Liu, X., Chan, A. -W., Denniston, A. K., Calvert, M. J., Ashrafian, H., . . . Yau, C. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ-BRITISH MEDICAL JOURNAL, 370, 14 pages.
DOI WoS45 Europe PMC161
2020 Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., . . . Yau, C. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ-BRITISH MEDICAL JOURNAL, 370, 13 pages.
DOI WoS67 Europe PMC217
2020 Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Chan, A. W., . . . Rowley, S. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nature Medicine, 26(9), 1364-1374.
DOI Scopus594 Europe PMC480
2019 McCradden, M. D., Vasileva, D., Orchanian-Cheff, A., & Buchman, D. Z. (2019). Ambiguous identities of drugs and people: A scoping review of opioid-related stigma. International Journal of Drug Policy, 74, 205-215.
DOI Scopus108 WoS100 Europe PMC89
2019 McCradden, M. D., & Anderson, J. A. (2019). The Last Refuge of Privacy. AJOB Neuroscience, 10(1), 25-28.
DOI
2019 McCradden, M. D., & Cusimano, M. D. (2019). Staying true to Rowan’s Law: how changing sport culture can realize the goal of the legislation. Canadian Journal of Public Health, 110(2), 165-168.
DOI Scopus15 WoS13 Europe PMC7
2019 McCradden, M. D., & Cusimano, M. D. (2019). Voices of survivors: 'you will not destroy our light'. British Journal of Sports Medicine, 53(22), 1384-1385.
DOI Scopus6 WoS4 Europe PMC2
2018 McCradden, M. D., & Cusimano, M. D. (2018). Concussions in sledding sports and the unrecognized "sled head": A systematic review. Frontiers in Neurology, 9(SEP), 8 pages.
DOI Scopus16 WoS12
2018 McCradden, M. D., & Cusimano, M. D. (2018). Optimized or Hijacked? The Moral Boundaries of Natural Athletic Performance. American Journal of Bioethics, 18(6), 26-28.
DOI Scopus1 WoS1 Europe PMC1
2018 McCradden, M. D., & Cusimano, M. D. (2018). Questioning Assumptions About Vulnerability in Psychiatric Patients. AJOB Neuroscience, 9(4), 221-223.
DOI

Year Citation
2023 Mccradden, M., Odusi, O., Joshi, S., Akrout, I., Ndlovu, K., Glocker, B., . . . Goldenberg, A. (2023). What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning. In 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 15 pages). Online: ACM.
DOI Scopus17 WoS17
2023 Xiao, L., Kang, S., Gonorazky, H., Chiang, J., Ambreen, M., Weinstock, L., . . . Amin, R. (2023). Understanding the Experiences of Caregivers of Children With Spinal Muscular Atrophy in the Era of Disease-modifying Therapies. In AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE Vol. 207 (pp. 2 pages). DC, Washington: AMER THORACIC SOC.
2022 Tonekaboni, S., Morgenshtern, G., Assadi, A., Pokhrel, A., Huang, X., Jayarajan, A., . . . Goldenberg, A. (2022). How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU. In Proceedings of Machine Learning Research Vol. 174 (pp. 169-182). ELECTR NETWORK: JMLR-JOURNAL MACHINE LEARNING RESEARCH.
Scopus7 WoS6
2020 McCradden, M., Mazwi, M., Joshi, S., & Anderson, J. A. (2020). When Your Only Tool Is A Hammer Ethical Limitations of Algorithmic Fairness Solutions in Healthcare Machine Learning. In PROCEEDINGS OF THE 3RD AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY AIES 2020 (pp. 109). NY, New York: ASSOC COMPUTING MACHINERY.
DOI WoS3
2019 Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. In J. Fackler, K. Jung, D. Kale, R. Ranganath, B. Wallace, J. Wiens, & F. Doshi-Velez (Eds.), Proceedings of Machine Learning Research Vol. 106 (pp. 359-380). MI, Ann Arbor: JMLR-JOURNAL MACHINE LEARNING RESEARCH.
Scopus348 WoS280

Year Citation
2025 McCradden, M., Tng, S., Jeyaseelan, D., Leane, C., Campbell, M., Braund, T., . . . Tang, J. (2025). A medical algorithmic audit framework for evaluating the safety, equity, and quality of an AI Scribe tool in a paediatric developmental assessment clinic.
DOI
2020 McCradden, M., Sarker, T., & Paprica, A. (2020). Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research.
DOI

The Hospital Research Foundation Group Fellowship Grant

Centre for Augmented Reasoning, Australian Institute for Machine Learning

Canadian Institutes for Health Research

Date Role Research Topic Program Degree Type Student Load Student Name
2025 Principal Supervisor Ethics of AI in Healthcare Doctor of Philosophy Doctorate Full Time Mr Humphrey Thompson
2025 Principal Supervisor Ethics of AI in Healthcare Doctor of Philosophy Doctorate Full Time Mr Humphrey Thompson
2024 Co-Supervisor Understanding Bias Caused by User Interaction with AI in High-Risk Decision Making Doctor of Philosophy Doctorate Full Time Miss Lana Tikhomirov
2024 Co-Supervisor Understanding Bias Caused by User Interaction with AI in High-Risk Decision Making Doctor of Philosophy Doctorate Full Time Miss Lana Tikhomirov

Date Role Membership Country
2025 - ongoing Member Sigma Xi Scientific Research Honor Society United States

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