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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)
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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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“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]
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“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]
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“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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“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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