| Question 1: True/False – The principles in the Beauchamp & Childress moral framework for bioethics are Respect for Autonomy, Beneficence, Nonmaleficence, and Justice. |
| Reference: | Beauchamp, T. and Childress, J. (2009). Principles of Biomedical Ethics, sixth edition. New York: Oxford University Press. |
| Choice A: | True |
| Choice B: | False |
| Question 2: Which of the following is a principle from AAPM’s Task Group 109, the revised code of ethics: |
| Reference: | Skourou, C., Sherouse, G.W., Bahar, N., Fairobent, L., Freedman, D.J., Genovese, L.M., Halvorsen, P.H., Kirby, N.A., Mahmood, U., Ozturk, N., Osterman, K.S., Serago, C.F., Svatos, M.M., Wilson, M.L. (2019). Code of ethics for the American Association of Physicists in Medicine (Revised): Report of Task Group 109. Medical Physics, 46(4), e79-e93. https://doi.org/10.1002/mp.13351 |
| Choice A: | Members must be certified to perform clinical medical physics tasks |
| Choice B: | Members must hold as paramount the best interests of the patient under all circumstances |
| Choice C: | Members shall hold their employers accountable for members’ practice, attitudes and actions |
| Question 3: Which of the following is NOT a foundational principle in the 2019 European and North American Joint Multisociety Statement on ethics and AI in radiology? |
| Reference: | Geis, J.R., Brady, A.P., Wu, C.C., Spencer, J., Ranschaert, E., Jaremko, J.L., Langer, S.G., Kitts, A.B., Birch, J., Shields, W.F., van den Hoven van Genderen, R., Kotter, E., Gichoya, J.W., Cook, T.S., Morgan, M.B., Tang, A., Safdar, N.M., Kohli, M. (2019) Ethics of artificial intelligence in radiology: summary of the joint European and North American multiscociety statement. Radiology 293(2), 436-440. https://doi.org/10.1148/radiol.2019191586 |
| Choice A: | Promote well-being, minimize harm, and ensure that the benefits and harms are distributed in a just manner |
| Choice B: | Respect human rights and freedoms |
| Choice C: | Be transparent and dependable, curtailing bias and decision, ensuring the locus of responsibility remains with human designers/operators |
| Choice D: | Design AI tools that will offer patients benefits without substantially increasing the cost of their care |
| Question 4: What is machine learning? |
| Reference: | Wakefield, K. (2022). A guide to the types of machine learning algorithms and their applications. SAS UK, accessed May 5, 2022. https://www.sas.com/en_gb/insights/articles/analytics/machine-learning-algorithms.html |
| Choice A: | A training program for machinists |
| Choice B: | Equivalent to artificial intelligence |
| Choice C: | A sub-field of AI in which algorithms analyze input data to predict output values, learning and improving their performance as they receive new data |
| Choice D: | A convolutional neural network |
| Question 5: True/False – Supervised learning requires humans to label an example set of data |
| Reference: | Wakefield, K. (2022). A guide to the types of machine learning algorithms and their applications. SAS UK, accessed May 5, 2022. https://www.sas.com/en_gb/insights/articles/analytics/machine-learning-algorithms.html |
| Choice A: | True |
| Choice B: | False |
| Question 6: Which of the following is NOT an application of AI in radiology or radiation oncology? |
| Reference: | Giger, M.L., Chan, H.P., and Boone, J. (2008). Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 35(12), 5799-5820. doi: 10.1118/1.3013555.
Huynh, E., Hosny, A., Guthier, C. et al. (2020). Artificial intelligence in radiation oncology. Nat Rev Clin Oncol 17, 771–781. https://doi.org/10.1038/s41571-020-0417-8
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. (2018). Artificial intelligence in radiology. Nature reviews. Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5 |
| Choice A: | Treatment planning |
| Choice B: | Computer-aided diagnosis |
| Choice C: | Patient consultation discussion |
| Choice D: | Quality assurance |
| Choice E: | Computer-aided detection |
| Question 7: A physician refrains from revealing information to a patient that may impact that patient’s decisions about their care. Which principle of the Beauchamp & Childress framework is violated, if any? |
| Reference: | Beauchamp, T. and Childress, J. (2009). Principles of Biomedical Ethics, sixth edition. New York: Oxford University Press. |
| Choice A: | Respect for autonomy |
| Choice B: | Beneficence |
| Choice C: | Nonmaleficence |
| Choice D: | Justice |
| Choice E: | None of these principles is violated |
| Question 8: According to the “Life Cycle” approach, which of the following is an example of a question that should be asked at the implementation stage of an AI tool’s life cycle? |
| Reference: | Vollmer, S., Mateen, B.A., Bohner, G., Kiraly, F.J., Ghani, R., Jonsson, P., Cumbers, S., Jonas, A., McAllister, K.S.L., Myles, P., Granger, D., Birse, M., Branson, R., Moons, K.G.M., Collins, G.S., Ioannidis, J.P.A., Holmes, C., Hemingway, H. Machine learning and AI research for patient benefit: 20 critical questions on transparency, replicability, ethics and effectiveness. (2020). British Medical Journal 368(I6927), 1-12. https://doi.org/10.1136/bmj.l6927 |
| Choice A: | Do the data capture relevant real-world heterogeneity? |
| Choice B: | Is the algorithm compared to current best technologies? |
| Choice C: | Is there transparency about flow of data and results? |
| Choice D: | Does the model create or exacerbate inequities? |
| Choice E: | How is the model being regularly assessed? |
| Question 9: A company develops an AI tool to diagnose skin cancer. The tool is based on data from only white patients. The company is aware that the tool tends to misdiagnose patients of color. They release the tool for clinical use, with a disclosure that it is only usable for white patients. Which principle of the Beauchamp & Childress framework is violated, if any? |
| Reference: | Beauchamp, T. and Childress, J. (2009). Principles of Biomedical Ethics, sixth edition. New York: Oxford University Press. |
| Choice A: | Respect for autonomy |
| Choice B: | Beneficence |
| Choice C: | Nonmaleficence |
| Choice D: | Justice |
| Choice E: | None of these principles is violated |
| Question 10: According to the “Life Cycle” approach, which of the following is an example of a question that should be asked at the study stage of an AI tool’s life cycle? |
| Reference: | Vollmer, S., Mateen, B.A., Bohner, G., Kiraly, F.J., Ghani, R., Jonsson, P., Cumbers, S., Jonas, A., McAllister, K.S.L., Myles, P., Granger, D., Birse, M., Branson, R., Moons, K.G.M., Collins, G.S., Ioannidis, J.P.A., Holmes, C., Hemingway, H. Machine learning and AI research for patient benefit: 20 critical questions on transparency, replicability, ethics and effectiveness. (2020). British Medical Journal 368(I6927), 1-12. https://doi.org/10.1136/bmj.l6927 |
| Choice A: | Do the data capture relevant real-world heterogeneity? |
| Choice B: | Is the algorithm compared to current best technologies? |
| Choice C: | Is there transparency about flow of data and results? |
| Choice D: | Does the model create or exacerbate inequities? |
| Choice E: | How is the model being regularly assessed? |
