03 - KI, Dystopien und süße kleine Robben

Gast: Prof. Dr. Kerstin Prechel
In unserer dritten Folge sprechen wir mit KI- und Ethik-Expertin
Prof. Dr. Kerstin Prechel über die neuen Herausforderungen an
Ärzte, aber auch an uns alle, die durch das Fortschreiten der
KI-Technologien auf uns warten. Es geht außerdem um kleine
Roboter mit Strickmützchen, niedliche Robben und Hollywood -
irgendwie.
Eine Folge, nach der es definitiv mehr Fragen als vorher gibt
...

Shownotes:
Relevante Studien zum Thema KI und Medizin: 
(Buchempfehlung nicht wissenschaftlich: Marc-Uwe Kling "Views")


Abdul-Kader, S. A., & Woods, J.
(2015).
Survey on chatbot design techniques in speech conversation
systems. International Journal of Advanced Computer Science and
Applications, 6(7), 72-80.


Alvarado, R. (2022).
What kind of trust does AI deserve, if any? AI and Ethics. DOI:
10.1007/s43681-022-00224-x.


Amann, J., Vetter, D., Blomberg, S.N., Christensen, H.C.,
Coffee, M., Gerke, S., Gilbert, T.K., Hagendorff, T., Holm, S.,
Livne, M., Spezzatti, A., Strümke, I., Zicari, R.V., & Madai,
V.I. (2022).
To explain or not to explain? Artificial intelligence
explainability in clinical decision support systems. PLOS Digital
Health 1(2), e0000016. DOI: 10.1371/journal.pdig.0000016 [Open
Access].


Arbelaez Ossa, L., Starke, G., Lorenzini, G., Vogt, J.E.,
Shaw, D.M., & Elger, B.S. (2022).
Re-focusing explainability in medicine. Digital Health, 8. DOI:
10.1177/20552076221074488 [Open Access].


Babushkina, D. (2022).
Are we justified attributing a mistake in diagnosis to an AI
diagnostic system? AI and Ethics. DOI: 10.1007/s43681-022-00189-x
[Open Access].


Baile, W. F., Buckman, R., Lenzi, R., Glober, G., Beale,
E. A., & Kudelka, A. P. (2000).
SPIKES—A six-step protocol for delivering bad news: Application
to the patient with cancer. The Oncologist, 5(4), 302-311.


Benrimoh, D., Hawco, C., & Fratila, R.
(2020).
Using artificial intelligence to support patients facing cancer:
From chatbot to clinical decision-making tools. Current Oncology
Reports, 22(11), 1-8.


Bickmore, T. W., & Giorgino, T.
(2006).
Health dialog systems for patients and consumers. Journal of
Biomedical Informatics, 39(5), 556-571.


Bickmore, T. W., & Schulman, D.
(2011).
Practical approaches to comforting patients with relational
agents. Interacting with Computers, 23(3), 279-288.


Bleher, H., & Braun, M. (2022).
Diffused responsibility: Attributions of responsibility in the
use of AI-driven clinical decision support systems. AI and
Ethics, 2(4), 747-761. DOI: 10.1007/s43681-022-00135-x [Open
Access].


Chen, H., Gomez, C., Huang, C.-M., & Unberath, M.
(2022).
Explainable medical imaging AI needs human-centered design:
Guidelines and evidence from a systematic review. npj Digital
Medicine, 5, 156. DOI: 10.1038/s41746-022-00699-2 [Open Access].


Combi, C., Amico, B., Bellazzi, R., Holzinger, A., Moore,
J.H., Zitnik, M., & Holmes, J.H. (2022).
A manifesto on explainability for artificial intelligence in
medicine. Artificial Intelligence in Medicine, 133, 102423. DOI:
10.1016/j.artmed.2022.102423 [Open Access].


Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S.
M., Blau, H. M., & Thrun, S. (2017).
Dermatologist-level classification of skin cancer with deep
neural networks. Nature, 542(7639), 115-118.


Floridi, L., & Cowls, J. (2019).
A unified framework of five principles for AI in society. Harvard
Data Science Review, 1(1), 1-15.


Friedrich, A.B., Mason, J., & Malone, J.R.
(2022).
Rethinking explainability: Toward a postphenomenology of
black-box artificial intelligence in medicine. Ethics and
Information Technology, 24, 8. DOI: 10.1007/s10676-022-09631-4.


Funer, F. (2022).
Accuracy and Interpretability: Struggling with the Epistemic
Foundations of Machine Learning-Generated Medical Information and
Their Practical Implications for the Doctor-Patient Relationship.
Philosophy & Technology 35(5). DOI:
10.1007/s13347-022-00505-7 [Open Access].


Funer, F. (2022).
The Deception of Certainty: how Non-Interpretable Machine
Learning Outcomes Challenge the Epistemic Authority of
Physicians. A deliberative-relational Approach. Medicine, Health
Care and Philosophy, 25, 167–178. DOI: 10.1007/s11019-022-10076-1
[Open Access].


Gardner, A., Smith, A.L., Steventon, A., Coughlan, E.,
& Oldfield, M. (2022).
Ethical funding for trustworthy AI: Proposals to address the
responsibility of funders to ensure that projects adhere to
trustworthy AI practice. AI and Ethics, 2, 277–291.


Grote, T., & Berens, P. (2020).
On the ethics of algorithmic decision-making in healthcare.
Journal of Medical Ethics, 46(3), 205-211.


Hallowell, N., Badger, S., Sauerbrei, A., Nellåker, C.,
& Kerasidou, A. (2022).
“I don’t think people are ready to trust these algorithms at face
value”: Trust and the use of machine learning algorithms in the
diagnosis of rare disease. BMC Medical Ethics, 23, 112. DOI:
10.1186/s12910-022-00842-4 [Open Access].


Hasani, N., Morris, M.A., Rhamim, A., Summers, R.M.,
Jones, E., Siegel, E., & Saboury, B. (2022).
Trustworthy Artificial Intelligence in Medical Imaging. PET Clin,
17(1), 1–12. DOI: 10.1016/j.cpet.2021.09.007.


Herzog, C. (2022).
On the risk of confusing interpretability with explicability. AI
and Ethics, 2, 219–225.


Herzog, C. (2022).
On the ethical and epistemological utility of explicable AI in
medicine. Philosophy & Technology, 35, 50. DOI:
10.1007/s13347-022-00546-y [Open Access].


Hatherley, J., Sparrow, R., & Howard, M.
(2022).
The virtues of interpretable medical artificial intelligence.
Cambridge Quarterly of Healthcare Ethics. DOI:
10.1017/S0963180122000305 [Open Access].


Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S.,
... & Wang, Y. (2017).
Artificial intelligence in healthcare: Past, present and future.
Stroke and Vascular Neurology, 2(4), 230-243.


Jobin, A., Ienca, M., & Vayena, E.
(2019).
The global landscape of AI ethics guidelines. Nature Machine
Intelligence, 1(9), 389-399.


Kawamleh, S. (2022).
Against explainability requirements for ethical artificial
intelligence in health care. AI and Ethics. DOI:
10.1007/s43681-022-00212-1.


Kawamleh, S. (2022).
Against explainability requirements for ethical artificial
intelligence in health care. AI and Ethics. DOI:
10.1007/s43681-022-00212-1.


Kemp, H., Freyer, N., & Nagel, S.K.
(2022).
Justice and the normative standards of explainability in
healthcare. Philosophy & Technology 35, 100. DOI:
10.1007/s13347-022-00598-0 [Open Access].


Kerasidou, C., Kerasidou, A., Buscher, M., &
Wilkinson, S. (2022).
Before and beyond trust: Reliance in medical AI. Journal of
Medical Ethics, 48(11), 852–856. DOI: 10.113


Kiseleva, A., Kotzinos, D., & De Hert, P.
(2022).
Transparency of AI in healthcare as a multilayered system of
accountabilities: Between legal requirements and technical
limitations. Frontiers in Artificial Intelligence, 5, 879603.
DOI: 10.3389/frai.2022.879603.


Lütge, C. (2020).
Ethik der Künstlichen Intelligenz. Springer.


Lütge, C., & Maas, J. (2021).
The Ethics of AI and Robotics: A German Perspective. In Ethics of
Artificial Intelligence and Robotics: Fundamentals and
Applications (pp. 33-51). Springer.


McDougall, R. J. (2019).
Computer knows best? The need for value-flexibility in medical
AI. Journal of Medical Ethics, 45(3), 156-160.


McTear, M. F., Callejas, Z., & Griol, D.
(2016).
The role of conversational agents in healthcare: A literature
review. Journal of Medical Systems, 40(7), 1-12.


Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de
Pennington, N., Mole, G., & Meinert, E.
(2020).
The effectiveness of artificial intelligence conversational
agents in health care: Systematic review. Journal of Medical
Internet Research, 22(10), e20346.


Morley, J., Floridi, L., Kinsey, L., & Elhalal, A.
(2020).
From what to how: An initial review of publicly available AI
ethics tools, methods, and research to translate principles into
practices. Science and Engineering Ethics, 26(4), 2141-2168.


Ott, T., & Dabrock, P. (2022).
Transparent human – (non-)transparent technology? The Janus-faced
call for transparency in AI-based health care technologies.
Frontiers in Genetics 13, 902960. DOI: 10.3389/fgene.2022.902960
[Open Access].


Petch, J., Di, S., & Nelson, W.
(2022).
Opening the black box. The promise and limitations of explainable
machine learning in cardiology. Canadian Journal of Cardiology
38(2), 204–213. DOI: 10.1016/j.cjca.2021.09.004 [Open Access].


Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H.,
Duan, T., ... & Ng, A. Y. (2017).
CheXNet: Radiologist-level pneumonia detection on chest X-rays
with deep learning. arXiv preprint arXiv:1711.05225.


Salahuddin, Z., Woodruff, H.C., Chatterjee, A., &
Lambin, P. (2022).
Transparency of deep neural networks for medical image analysis.
A review of interpretability methods. Computers in Biology and
Medicine 140, 105111. DOI: 10.1016/j.compbiomed.2021.105111 [Open
Access].


Sand, M., Durán, J.M., & Jongsma, K.R.
(2022).
Responsibility beyond design: Physicians’ requirements for
ethical medical AI. Bioethics 36(2), 162–169. DOI:
10.1111/bioe.12887 [Open Access].


Schmitz, R., Werner, R., Repici, A., Bisschops, R.,
Meining, A., Zornow, M., Messmann, H., Hassan, C., Sharma, P.,
& Rösch, T. (2022).
Artificial intelligence in GI endoscopy: Stumbling blocks, gold
standards and the role of endoscopy societies. Gut 71(3),
451–454. DOI: 10.1136/gutjnl-2020-323115.


Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P.
(2018).
Deep EHR: A survey of recent advances in deep learning techniques
for electronic health record (EHR) analysis. IEEE Journal of
Biomedical and Health Informatics, 22(5), 1589-1604.


Starke, G., & van den Brule, R., Elger, B.S., &
Haselager, P. (2022).
Intentional machines: A defence of trust in medical artificial
intelligence. Bioethics 36, 154–161.


Starke, G., & Ienca, M. (2022).
Misplaced trust and distrust: How not to engage with medical
artificial intelligence. Cambridge Quarterly of Healthcare
Ethics. DOI: 10.1017/S0963180122000445 [Open Access].


Topol, E. J. (2019).
High-performance medicine: The convergence of human and
artificial intelligence. Nature Medicine, 25(1), 44-56.


Ursin, F., Timmermann, C., & Steger, F.
(2022).
Explicability of artificial intelligence in radiology: Is a fifth
bioethical principle conceptually necessary? Bioethics 36(2),
143–153. DOI: 10.1111/bioe.12918 [Open Access].


Verdicchio, M., & Perin, A. (2022).
When doctors and AI interact: On human responsibility for
artificial risks. Philosophy & Technology 35, 11. DOI:
10.1007/s13347-022-00506-6 [Open Access].


Wadden, J.J. (2022).
Defining the undefinable: The black box problem in healthcare
artificial intelligence. Journal of Medical Ethics 48(10),
764–768. DOI: 10.1136/medethics-2021-107529.


Winter, P., & Carusi, A. (2022).
‘If you’re going to trust the machine, then that trust has got to
be based on something’: Validation and the co-constitution of
trust in developing artificial intelligence (AI) for the early
diagnosis of pulmonary hypertension (PH). Science &
Technology Studies 35(4), 58–77. DOI: 10.23987/sts.102198 [Open
Access].


Winter, P.D., & Carusi, A. (2022).
(De)troubling transparency: Artificial intelligence (AI) for
clinical applications. Medical Humanities. DOI:
10.1136/medhum-2021-012318.


Yoon, C.H., Torrance, R., & Scheinerman, N.
(2022).
Machine learning in medicine: Should the pursuit of enhanced
interpretability be abandoned? Journal of Medical Ethics 48(9),
581–585. DOI: 10.1136/medethics-2020-107102 [Open Access].


Yu, K. H., Beam, A. L., & Kohane, I. S.
(2018).
Artificial intelligence in healthcare. Nature Biomedical
Engineering, 2(10), 719-731.



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