Melissa McCradden

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

  • 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

  • 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.
    DOI Scopus4 Europe PMC3
    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 Scopus26 Europe PMC18
    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 McCradden, M. D., Mazwi, M. L., & Oakden-Rayner, L. (2025). Can an accurate model be bad?. Patterns, 6(4), 101205.
    DOI
    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
    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 Scopus4 Europe PMC2
    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
    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 Scopus11
    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
    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.
    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
    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 Scopus1 Europe PMC1
    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 Scopus13 Europe PMC6
    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 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
    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 Scopus8 Europe PMC7
    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 Scopus5
    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 Scopus15 Europe PMC11
    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 Scopus592 Europe PMC487
    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 Europe PMC3
    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, e13.
    DOI Scopus2 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 Scopus2 Europe PMC1
    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 Scopus21
    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 Scopus22 Europe PMC12
    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 Scopus10 Europe PMC4
    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 Scopus28 Europe PMC17
    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 Scopus42 Europe PMC33
    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 Scopus7
    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 Scopus53 Europe PMC31
    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 Scopus7 Europe PMC6
    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 Scopus10 Europe PMC5
    2023 Katzman, D. K., & McCradden, M. D. (2023). Capacity for Preferences: Adolescents With AN-PLUS. Journal of Adolescent Health, 72(6), 827-828.
    DOI Scopus2 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 Scopus4 Europe PMC3
    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 Scopus13 Europe PMC8
    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 Scopus12 Europe PMC9
    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 Scopus122 WoS2 Europe PMC75
    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 Scopus27 WoS2 Europe PMC13
    2023 McCradden, M. D. (2023). Partnering with children and youth to advance artificial intelligence in healthcare. Pediatric Research, 93(2), 284-286.
    DOI Scopus2 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
    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 Scopus80 Europe PMC46
    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
    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 Scopus22 Europe PMC11
    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 Scopus13 Europe PMC7
    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 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 Scopus11 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 Scopus138 Europe PMC96
    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 Scopus150 WoS37 Europe PMC88
    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 Scopus6 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 Scopus55 WoS5 Europe PMC42
    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 Scopus254 Europe PMC194
    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 Scopus11 Europe PMC8
    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 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 Scopus132 Europe PMC109
    2021 McCradden, M. D. (2021). When is accuracy off-target?. Translational Psychiatry, 11(1), 369.
    DOI Scopus8 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 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 Europe PMC2
    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
    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 Scopus71 Europe PMC39
    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 Scopus63 Europe PMC42
    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 Scopus50 Europe PMC39
    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 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 Scopus52 Europe PMC25
    2020 McCradden, M. D., Joshi, S., Mazwi, M., & Anderson, J. A. (2020). Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digital Health, 2(5), e221-e223.
    DOI Scopus159 Europe PMC99
    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
    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 Scopus345 Europe PMC245
    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 Scopus231 Europe PMC154
    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 Scopus213 Europe PMC159
    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 WoS40 Europe PMC115
    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 WoS53 Europe PMC172
    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 Scopus506 Europe PMC376
    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 Scopus102 Europe PMC73
    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 Scopus14 Europe PMC6
    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 Europe PMC1
    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 Scopus15
    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 Europe PMC1
    2018 McCradden, M. D., & Cusimano, M. D. (2018). Questioning Assumptions About Vulnerability in Psychiatric Patients. AJOB Neuroscience, 9(4), 221-223.
    DOI

  • 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 Scopus16
    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
    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
    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.
    Scopus294

  • Report for External Bodies
    Year Citation
    2024 Semmler, C., Palmer, L., McCradden, M., Oakden-Rayner, L., & Tikhomirov, L. (2024). Submission to Senate Select Committee on Adopting Artificial Intelligence (AI) - Submission 95 (95).

  • Preprint
    Year Citation
    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.
    DOI

The Hospital Research Foundation Group Fellowship Grant

Centre for Augmented Reasoning, Australian Institute for Machine Learning

Canadian Institutes for Health Research

  • Memberships
    Date Role Membership Country
    2025 - ongoing Member Sigma Xi Scientific Research Honor Society United States
  • Position: THRF Clinical Research Fellow
  • Phone: 83133909
  • Email: melissa.mccradden@adelaide.edu.au
  • Campus: Lot 14
  • Building: Australian Institute for Machine Learning Building, floor Second Floor
  • Room: 2.21
  • Org Unit: Australian Institute for Machine Learning - Projects
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