The European Legal Framework for Medical AI
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Medical processes with and without the use of AI already reach a complexity today that obliges medical personnel to explain with appropriately reduced complexity.
“Medical processes with and without the use of AI already reach a complexity today that obliges medical personnel to explain with “appropriately reduced complexity” (Schneeberger et al., 2020 with reference to Eberbach, 2019).”
From Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations (2022) · cites this paper as Schneeberger et al., 2020
Cites
The mythos of model interpretability
Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131 · see citing works
Wachter, S., Mittelstadt, B., & Floridi, L. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx005 · Cited by 1,308 · see citing works
O'Sullivan, S., Nevejans, N., Allen, C., Blyth, A., Leonard, S., Pagallo, U., Holzinger, K., Holzinger, A., Sajid, M. I., & Ashrafian, H. (2019) · The International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.1968 · Cited by 437 · see citing works
Self-citationSlave to the Algorithm? Why a Right to Explanationn is Probably Not the Remedy You are Looking for
Edwards, L., & Veale, M. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2972855 · Cited by 244 · see citing works
Informed Consent and Medical Artificial Intelligence: What to Tell the Patient?
Cohen, I. G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3529576 · Cited by 165 · see citing works
"The Right to Explanation, Explained"
Kaminski, M. E. (2019) · btlj · doi:10.15779/z38td9n83h · Cited by 146 · see citing works
Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”?
Edwards, L., & Veale, M. (2018) · IEEE Security & Privacy · doi:10.1109/msp.2018.2701152 · Cited by 141 · see citing works
Casey, B. F. (2019) · btlj · doi:10.15779/z38m32n986 · Cited by 91 · see citing works
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Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works
Evaluating the quality of machine learning explanations: a survey on methods and metrics
Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606 · see citing works
Self-citationHolzinger, A., Malle, B., Saranti, A., & Pfeifer, B. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.01.008 · Cited by 352 · see citing works
Self-citationExplainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare
Hulsen, T. (2023) · AI · doi:10.3390/ai4030034 · Cited by 305 · see citing works
Čartolovni, A., Tomičić, A., & Lazić Mosler, E. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104738 · Cited by 267 · see citing works
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The explainability paradox: Challenges for xAI in digital pathology
Evans, T., Retzlaff, C. O., Geißler, C., Kargl, M., Plass, M., Müller, H., Kiehl, T.-R., Zerbe, N., & Holzinger, A. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.009 · Cited by 135 · see citing works
Self-citationSheu, R.-K., & Pardeshi, M. S. (2022) · Sensors · doi:10.3390/s22208068 · Cited by 129 · see citing works
Patient perspectives on informed consent for medical AI: A web-based experiment
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Explainable AI and Multi-Modal Causability in Medicine
Holzinger, A. (2021) · i-com · doi:10.1515/icom-2020-0024 · Cited by 87 · see citing works
Self-citationHolm, S., Stanton, C., & Bartlett, B. (2021) · Health Care Analysis · doi:10.1007/s10728-021-00430-4 · Cited by 32 · see citing works
- Authors
- Schneeberger, D., Stöger, K., & Holzinger, A.
- Affiliations
- University of Graz (AT)
- Medical University of Graz (AT)
- Corresponding author
- Schneeberger, David
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Volume
- LNCS-12279
- Pages
- 209-226
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3080562335
- Cited by
- 85
- References
- 10
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 11 · 2023: 16 · 2022: 18 · 2021: 22 · 2020: 4
- FWCI
- 111.23
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, cc-by)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in healthcare, AI regulation, European Union law, high-risk AI systems, fundamental rights, data protection, liability law, human-centric approach, Internet of Things, robotics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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