The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Artificial Intelligence and Patient-Centered Decision-Making
Open accessAbstract
Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient.
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“studies show that many AI systems used in medicine are highly reliable, and sometimes have greater precision than human doctors.”
From The Epistemic Cost of Opacity: How the Use of Artificial Intelligence Undermines the Knowledge of Medical Doctors in High-Stakes Contexts (2025) · cites this paper as Bjerring and Busch (2021, 350)
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“For specific medical treatments, the studies establishing their effec -tiveness are available in journals, or online. So, as Bjerring and Busch ( 2021 , 364) point out, there is indeed some information made available that can be used by medi -cal doctors to justify their belief that a certain treatment is effective.”
From The Epistemic Cost of Opacity: How the Use of Artificial Intelligence Undermines the Knowledge of Medical Doctors in High-Stakes Contexts (2025) · cites this paper as Bjerring and Busch, 2021
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In the citing paper
“Similarly, AI can help patients with diabetes to manage their blood glucose levels, diet, and exercise using a chatbot that provides education and coaching based on natural language processing and machine learning. 66”
From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [66]
Experimental — accuracy not yet measuredIn the citing paper
“Since clinicians are no longer able to fully comprehend the inner workings and calculations of the decision aid they are not able to explain to the patient how certain outcomes or recommendations were derived [33].”
From Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020) · cites this paper as [33]
In the citing paper
“Seeking to address these questions, it was recently argued that so-called ‘black-box medicine’ conflicts with core ideals of patient-centered medicine [33].”
From Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020) · cites this paper as [33]
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Cites
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Evidence-Based Medicine: How to Practice and Teach EBM
Sackett, D. L. (2000) · Cited by 5,950
Actual causes of death in the United States
McGinnis, J. M. (1993) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1993.03510180077038 · Cited by 5,480
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312
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
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774
Research works printed but not linked: 2
- Faes, L., Liu, X., Kale, A., Bruynseels, A., Shamdas, M., Moraes, G., Fu, D.J., Wagner, S.K., Kern, C., Ledsam, J.R. and Schmid, M.K. (2019). Deep learning under scrutiny: performance against health care professionals in detecting diseases from medical imaging-systematic review and meta-Analysis (preprint). · DOI · Paper page
- Price II, W. N. (2017). Artificial intelligence in healthcare: applications and legal implications. The SciTech Lawyer, 14(1), 10–13.
Cited by
Sorted by citations
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203
Sauerbrei, A., Kerasidou, A., Lucivero, F., & Hallowell, N. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02162-y · Cited by 353
“They explain that, currently, there is always a human in the loop who is able to give an explanation of how technology works (for example, there will be an engineer able to explain how an MRI machine works), but this cannot be said of some AI systems [30].”
BackgroundInfluentialTrust and medical AI: the challenges we face and the expertise needed to overcome them
Quinn, T. P., Senadeera, M., Jacobs, S., Coghlan, S., & Le, V. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa268 · Cited by 287
Čartolovni, A., Tomičić, A., & Lazić Mosler, E. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104738 · Cited by 267
Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024) · Health Services Research and Managerial Epidemiology · doi:10.1177/23333928241234863 · Cited by 265
“66 The last subtheme is enhancing patient medication adherence.”
Background
Metadata
- Authors
- Bjerring, J. C., & Busch, J.
- Affiliations
- Aarhus University (DK)
- Corresponding author
- Bjerring, Jens Christian
- Busch, Jacob
- Venue
- Philosophy & Technology
- Published
- 2020-01-08
- Volume
- 34
- Issue
- 2
- Pages
- 349-371
- ISSN
- 2210-5433 (ISSN-L)
- 2210-5441
- OpenAlex
- W2999885075
- Cited by
- 274
- References
- 77
- Citations by year
- 2026: 47 · 2025: 66 · 2024: 65 · 2023: 34 · 2022: 36 · 2021: 22 · 2020: 4
- FWCI
- 7.04
- Type
- journal article
- Language
- en
- Open access
- green
- pure.au.dk
- Licence
- Locations
- Philosophy & Technology (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in healthcare, patient-centered medicine, shared decision-making, informed consent, clinical judgment
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