
Dr Melissa McCradden
THRF Clinical Research Fellow
Australian Institute for Machine Learning - Projects
Faculty of Sciences, Engineering and 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
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Appointments
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 Competencies
Language Competency French Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title McMaster University Canada PhD University of Toronto Canada MHSc -
Postgraduate Training
Date Title Institution Country 2017 - 2018 Postdoctoral fellow in neurosurgery and injury prevention Unity Health Toronto Canada AI Ethics Fellow Vector Institute/SickKids Canada
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Journals
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.
Scopus6 WoS6 Europe PMC52025 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.
Scopus33 WoS23 Europe PMC232025 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).
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.
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.
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.
WoS2 Europe PMC22025 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.
Europe PMC12025 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.
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.
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.
Scopus4 WoS22025 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.
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.
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.
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.
Scopus22025 McCradden, M. D., Mazwi, M. L., & Oakden-Rayner, L. (2025). Can an accurate model be bad?. Patterns, 6(4), 101205.
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.
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.
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.
Scopus4 WoS4 Europe PMC22025 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.
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.
Scopus23 WoS16 Europe PMC132025 Holst, J. J., Schéele, C., Scherer, P. E., Jia, W., Segal, E., Slavov, N., . . . Jacobs, P. G. (2025). Artificial intelligence in metabolic research.. Nature metabolism.
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.
Scopus1 Europe PMC12024 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).
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.
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.
Scopus14 WoS11 Europe PMC92024 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.
Europe PMC12024 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.
Scopus1 WoS12024 McCradden, M. D., & Stedman, I. (2024). Explaining decisions without explainability? Artificial intelligence and medicolegal accountability. Future Healthcare Journal, 11(3), 100171.
Europe PMC22024 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.
Scopus9 WoS8 Europe PMC82024 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.
Scopus62024 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.
Scopus18 WoS17 Europe PMC142024 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.
Scopus753 WoS645 Europe PMC5882024 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.
WoS5 Europe PMC52024 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.
Scopus2 WoS1 Europe PMC12024 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.
Scopus1 Europe PMC12024 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.
Scopus2 WoS3 Europe PMC22024 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.
Scopus23 WoS202023 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.
Scopus28 WoS19 Europe PMC172023 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.
Scopus12 WoS11 Europe PMC62023 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.
Scopus32 WoS22 Europe PMC242023 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.
Scopus50 WoS41 Europe PMC402023 McCradden, M. D. (2023). Ethics, First. American Journal of Bioethics, 23(9), 55-56.
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.
Scopus7 WoS92023 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.
Scopus55 WoS43 Europe PMC342023 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.
Scopus7 WoS7 Europe PMC72023 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.
Scopus10 WoS7 Europe PMC72023 Katzman, D. K., & McCradden, M. D. (2023). Capacity for Preferences: Adolescents With AN-PLUS. Journal of Adolescent Health, 72(6), 827-828.
Scopus2 WoS2 Europe PMC22023 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.
Scopus5 WoS4 Europe PMC32023 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.
Scopus13 WoS12 Europe PMC102023 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.
Scopus13 WoS12 Europe PMC112023 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.
Scopus133 WoS106 Europe PMC862023 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.
Scopus29 WoS27 Europe PMC152023 McCradden, M. D. (2023). Partnering with children and youth to advance artificial intelligence in healthcare. Pediatric Research, 93(2), 284-286.
Scopus2 WoS2 Europe PMC12023 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.
Europe PMC12022 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.
Scopus1 WoS12022 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.
Scopus81 WoS69 Europe PMC512022 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.
Scopus11 WoS82022 McCradden, M. (2022). Melissa McCradden. CELL REPORTS MEDICINE, 3(12), 2 pages.
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.
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.
Scopus23 WoS23 Europe PMC162022 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.
Scopus14 WoS9 Europe PMC82022 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.
Scopus4 WoS3 Europe PMC32022 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.
Scopus11 WoS10 Europe PMC52022 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.
Scopus158 WoS340 Europe PMC1042022 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.
Scopus157 WoS136 Europe PMC982022 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.
Scopus6 WoS2 Europe PMC32022 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.
Scopus58 WoS58 Europe PMC492022 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.
Scopus267 WoS42 Europe PMC2202021 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.
Scopus12 WoS10 Europe PMC102021 McCradden, M. D., & Chad, L. (2021). Screening for facial differences worldwide: equity and ethics. The Lancet Digital Health, 3(10), e615-e616.
Scopus2 WoS2 Europe PMC12021 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.
Scopus143 WoS135 Europe PMC1192021 McCradden, M. D. (2021). When is accuracy off-target?. Translational Psychiatry, 11(1), 369.
Scopus8 WoS8 Europe PMC52021 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.
Scopus2 WoS4 Europe PMC22021 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.
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.
Scopus7 WoS7 Europe PMC32021 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.
Scopus1 WoS12020 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.
Scopus72 WoS57 Europe PMC422020 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.
Scopus66 Europe PMC462020 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.
Scopus51 WoS47 Europe PMC402020 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.
Scopus8 WoS9 Europe PMC42020 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.
Scopus54 WoS46 Europe PMC292020 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.
Scopus163 WoS138 Europe PMC1072020 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.
Scopus12 WoS132020 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.
Scopus352 Europe PMC2592020 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.
Scopus240 Europe PMC1672020 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.
Scopus225 Europe PMC1732020 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.
WoS45 Europe PMC1412020 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.
WoS66 Europe PMC1882020 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.
Scopus537 Europe PMC4222019 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.
Scopus104 WoS95 Europe PMC832019 McCradden, M. D., & Anderson, J. A. (2019). The Last Refuge of Privacy. AJOB Neuroscience, 10(1), 25-28.
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.
Scopus14 WoS13 Europe PMC72019 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.
Scopus6 WoS4 Europe PMC12018 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.
Scopus15 WoS122018 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.
Scopus1 WoS1 Europe PMC12018 McCradden, M. D., & Cusimano, M. D. (2018). Questioning Assumptions About Vulnerability in Psychiatric Patients. AJOB Neuroscience, 9(4), 221-223.
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Conference Papers
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 WoS172023 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 WoS62020 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 WoS32019 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.
Scopus307 WoS260 -
Report for External Bodies
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Preprint
Year Citation 2025 McCradden, M. D., 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.
DOI2020 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.
DOI
The Hospital Research Foundation Group Fellowship Grant
Centre for Augmented Reasoning, Australian Institute for Machine Learning
Canadian Institutes for Health Research
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Current Higher Degree by Research Supervision (University of Adelaide)
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 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
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Memberships
Date Role Membership Country 2025 - ongoing Member Sigma Xi Scientific Research Honor Society United States
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