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 Women's and Children's Health Network Artificial Intelligence Director and The Hospital Research Foundation (THRF) Group Clinical Research Fellow in Ethics of AI at the Australian Institute for Machine Learning at the University of Adelaide. She is an Adjunct Scientist with the SickKids Research Institute. Dr McCradden serves as an Associate Editor for the journal Research Ethics.

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. 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 others. She was listed among the 100 Brilliant Women in AI Ethics List (2022).

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.

  • Appointments
    Date Position Institution name
    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 Integration 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
    AI Ethics Fellow Vector Institute/SickKids Canada

  • Journals
    Year Citation
    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 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
    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
    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
    2024 McCradden, M. D., & Stedman, I. (2024). Explaining decisions without explainability? Artificial intelligence and medicolegal accountability.. Future healthcare journal, 11(3), 100171.
    DOI
    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.
    DOI
    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. The Lancet Digital Health, 6(11), e827-e847.
    DOI
    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 Scopus68 Europe PMC35
    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, 1.
    DOI
    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, 1.
    DOI
    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
    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 Scopus4
    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 Scopus3 Europe PMC3
    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 Scopus28 WoS2 Europe PMC10
    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 Scopus11 Europe PMC3
    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 Scopus2 Europe PMC1
    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 Scopus11 Europe PMC6
    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 Scopus17 Europe PMC10
    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 Scopus5
    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 Scopus27 Europe PMC15
    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 Scopus4 Europe PMC3
    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 Scopus6 Europe PMC1
    2023 Katzman, D. K., & McCradden, M. D. (2023). Capacity for Preferences: Adolescents With AN-PLUS. Journal of Adolescent Health, 72(6), 827-828.
    DOI Scopus1 Europe PMC1
    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, 11 pages.
    DOI Scopus3 Europe PMC2
    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 Scopus6 Europe PMC4
    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 Scopus8 Europe PMC5
    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), e2310659.
    DOI Scopus11 WoS2 Europe PMC7
    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
    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 Scopus54 Europe PMC23
    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 Scopus9
    2022 McCradden, M. (2022). Melissa McCradden. CELL REPORTS MEDICINE, 3(12), 2 pages.
    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 Scopus9
    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.
    DOI Scopus5
    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 Scopus3
    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 Scopus7 Europe PMC3
    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 Scopus79 Europe PMC51
    2022 Shi, P., Liu, Z. -Y., Zhang, X. -Q., Chen, X., Yao, N., Xie, J., . . . Zhang, Q. (2022). Polar interaction of polymer host-solvent enables stable solid electrolyte interphase in composite lithium metal anodes. JOURNAL OF ENERGY CHEMISTRY, 64, 172-178.
    DOI WoS39
    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 Scopus108 WoS37 Europe PMC48
    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 Scopus3 Europe PMC1
    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 Scopus34 WoS5 Europe PMC18
    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 Scopus178 Europe PMC102
    2021 Chen, B., Gao, H., Li, H., Ma, H., Gao, P., Chu, P., & Shi, P. (2021). Indoor and Outdoor Surface Measurement of 3D Objects under Different Background Illuminations and Wind Conditions Using Laser-Beam-Based Sinusoidal Fringe Projections. PHOTONICS, 8(6), 17 pages.
    DOI WoS3
    2021 Chen, B., Li, H., Yue, J., & Shi, P. (2021). Fourier-Transform-Based Surface Measurement and Reconstruction of Human Face Using the Projection of Monochromatic Structured Light. SENSORS, 21(7), 14 pages.
    DOI WoS3
    2021 Liu, S., Shi, P., Wan, Z., Lu, S., Lv, J., & Meng, F. (2021). Interrelationships between Acoustic Emission and Cutting Force in Rock Cutting. GEOFLUIDS, 2021, 12 pages.
    DOI WoS1
    2021 Yin, J., Gao, C., Zhu, M., Wang, H., Shi, P., Chen, Y. Y., . . . Yu, B. (2021). Oil Accumulation Model and Its Main Controlling Factors in Lower Yanchang Formation, Wuqi-Dingbianarea, Ordos Basin, China. GEOFLUIDS, 2021, 10 pages.
    DOI WoS2
    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 Scopus7 Europe PMC5
    2021 McCradden, M. D., & Chad, L. (2021). Screening for facial differences worldwide: equity and ethics. The Lancet Digital Health, 3(10), e615-e616.
    DOI Scopus1 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 Scopus90 Europe PMC62
    2021 McCradden, M. D. (2021). When is accuracy off-target?. Translational Psychiatry, 11(1), 2 pages.
    DOI Scopus6 Europe PMC3
    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 Scopus1 Europe PMC1
    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 Scopus6 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 Scopus57 Europe PMC29
    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 Scopus46 Europe PMC31
    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), 9 pages.
    DOI Scopus33 Europe PMC22
    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 Scopus6 Europe PMC3
    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 Scopus41 Europe PMC16
    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 Scopus124 Europe PMC73
    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 Scopus9
    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 Scopus270 Europe PMC141
    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 Scopus161 Europe PMC87
    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 Scopus142 Europe PMC81
    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 PMC86
    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 PMC115
    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 Scopus393 Europe PMC217
    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 Scopus83 Europe PMC49
    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 Scopus12 Europe PMC4
    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 Scopus4
    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 Scopus13
    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 Vol. 27 (pp. 15 pages). Online: ACM.
    DOI Scopus8
    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).
    Scopus4
    2019 Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. In Proceedings of Machine Learning Research Vol. 106 (pp. 359-380).
    Scopus219

  • 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
  • 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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