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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]
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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“Patients are considered experts in their experiences and are provided with all the information necessary to make informed decisions regarding their care [3, 14, 17].”
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“AI in decision-making systems carries out complex operations on complex content that yields results in highly complex tasks (Bjerring & Busch, 2021).”
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The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
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Busch, F., Hoffmann, L., Xu, L., Zhang, L. J., Hu, B., García-Juárez, I., Toapanta-Yanchapaxi, L. N., Gorelik, N., Gorelik, V., Rodriguez-Granillo, G. A., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.14452 · Cited by 124
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InfluentialAI support for ethical decision-making around resuscitation: proceed with care
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“First, enabling patient-centred care and patient autonomy through informed consent and shared decision-making practices arguably requires its own type of explainability (Bjerring & Busch, 2020; Keeling and Nyrup, manuscript).”
BackgroundDeep learning based deep-sea automatic image enhancement and animal species classification
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“(2021), Ferreira (2021), and others for an increased role (McDougall 2019; Grote & Berens, 2020; Bjerring & Busch, 2020), the relevance is given to analyze the value of explainability closer.”
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“However, this claim of higher certainty is a fallacy because it is based on reductionisms and simplifications of an intrinsically uncertain and ambiguous medical reality (cf. Cabitza et al. 2017; Bjerring and Busch 2021; Mallia 2016; Wiesing 1995).”
BackgroundDigital Transformation in Norwegian Enterprises
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Randomized Controlled Trials in Medical AI
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“This demand is also revealing, we believe, insofar as it implies that AI—including ML—systems generate and contain knowledge about the world, albeit in a form that can be hard for human beings to access (Bjerring and Busch 2021, 349–351).”
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“This could prove to be crucial in what may become a new era of medical paternalism, where both the patient and the clinician are beholden to black box AI systems [46].”
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“When AI systems are designed without a thorough understanding of patient autonomy, self-determination, and individual health goals, there is a risk of eroding the essence of patient-centered care [47].”
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“23 24 For them, the risk is to become over-reliant on AI systems and ultimately accept decisions which are incorrect—as has been demonstrated by studies examining the interaction of clinicians and AI systems.”
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“Some argue that the use of these ‘black-box’ systems could diminish physicians’ comprehension of medical processes and challenge their epistemic authority, potentially conflicting with the fundamental principles of patient-centered medicine and thereby ultimately eroding patient trust [47–49].”
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“In a recent article, Bjerring & Busch put forward an argument to show that patient-centred decision-making (such as SDM), is undermined by what they call “black-box medicine,” involving DL systems.(5) They begin by assuming that DL systems outperform, or with enough development would outperform, human practitioners.”
BackgroundMethodInfluentialThe Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making
Macri, R., & Roberts, S. L. (2023) · Current Oncology · doi:10.3390/curroncol30020168 · Cited by 29
“During a shared decision-making conversation, the clinician provides the patient with information to build an accurate illness understanding [32,40].”
BackgroundReflection Machines: Supporting Effective Human Oversight Over Medical Decision Support Systems
Haselager, P., Schraffenberger, H., Thill, S., Fischer, S. W. S., Lanillos, P., van de Groes, S., & van Hooff, M. L. (2023) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180122000718 · Cited by 27
“32 To propose relevant questions, the RM can explicitly take into account: (1) the weakest evidence for the accepted decision, (2) the strongest evidence for the alternative decision, and (3) missing evidence that would have had a significant impact on the recommended decision (based on unperformed…”
Should AI allocate livers for transplant? Public attitudes and ethical considerations
Drezga‐Kleiminger, M., Demaree‐Cotton, J., Koplin, J. J., Savulescu, J., & Wilkinson, D. J. C. (2023) · BMC Medical Ethics · doi:10.1186/s12910-023-00983-0 · Cited by 26
“On the other hand, there is ethical concern about AI bias [4, 45, 46] and that some (non-rule-based) types of AI are a “black box”, i.e., the decision-making process is uninterpretable to the user [45, 47, 48].”
BackgroundThe value of responsibility gaps in algorithmic decision-making
Munch, L. A., Mainz, J. T., & Bjerring, J. C. (2023) · Ethics and Information Technology · doi:10.1007/s10676-023-09699-6 · Cited by 25
“Recent advances in machine learning suggest that AI systems in the near future could replace humans in carrying out many critical decision-making tasks (Bjerring & Busch, 2021; Baum et al., 2022).”
BackgroundSelf-citationArtificial intelligence in healthcare and its implications for patient centered care
Kumah, E. (2025) · Discover Public Health · doi:10.1186/s12982-025-00924-9 · Cited by 31
Are we justified attributing a mistake in diagnosis to an AI diagnostic system?
Babushkina, D. (2022) · AI and Ethics · doi:10.1007/s43681-022-00189-x · Cited by 24
Dimitsaki, S., Gavriilidis, G. I., Dimitriadis, V. K., & Natsiavas, P. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102490 · Cited by 24
“) [9], there are still several lingering caveats considering predominantly the lack of interpretability and explainability (“black box” challenge) [33].”
BackgroundArtificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation
Pretis, F. D., Landes, J., & Peden, W. (2021) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13542 · Cited by 21
Federation Opacity and the Promise of Federated Learning in Healthcare
Hatherley, J. J., Søgaard, A., Ballantyne, A., & Pauwels, R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2637093 · Cited by 27
“In contrast, the ethical aspect refers to the ethical challenges and safety risks that these different types of opacity present in medical ML systems (see Hatherley 2020; Bjerring and Busch 2021; Smith 2021; Hatherley et al. 2024).”
“Does Black Box AI In Medicine Compromise Informed Consent?”
Director, S. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00860-1 · Cited by 25
Stroud, A. M., Anzabi, M. D., Wise, J. L., Barry, B. A., Malik, M. M., McGowan, M. L., & Sharp, R. R. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.100189 · Cited by 25
Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice
Grote, T., & Berens, P. (2023) · The Journal of Medicine and Philosophy A Forum for Bioethics and Philosophy of Medicine · doi:10.1093/jmp/jhac034 · Cited by 18
Artificial intelligence in paediatric endocrinology: conflict or cooperation
Dimitri, P., & Savage, M. O. (2024) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2023-0554 · Cited by 18
Responsible Use of AI in Military Systems
Schraagen, J. M. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003410379 · Cited by 20
eXplainable Artificial Intelligence (XAI) for improving organisational regility
Shafiabady, N., Hadjinicolaou, N., Hettikankanamage, N. D., MohammadiSavadkoohi, E., Wu, M., & Vakilian, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301429 · Cited by 19
An interdisciplinary perspective on AI-supported decision making in medicine
Ammeling, J., Aubreville, M., Fritz, A., Kießig, A., Krügel, S., & Uhl, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102791 · Cited by 20
Epistemic authority and medical AI: epistemological differences and challenges in medical practice
Kerasidou, A., & Kerasidou, C. (2025) · Medicine Health Care and Philosophy · doi:10.1007/s11019-025-10306-2 · Cited by 20
“In so far as AI tools can outperform doctors in their epistemic tasks (e.g. diagnosis), some have argued, human doctors have an obligation to defer to AI and treat it as an epistemic authority (Bjerring and Busch 2021).”
Popowicz, D. M. (2021) · Philosophy of Medicine · doi:10.5195/philmed.2021.49 · Cited by 16
Funer, F. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00505-7 · Cited by 18
“Therefore, they demand that ML applications provide explicable or interpretable processes and/or outcomes (cf. Bjerring & Busch, 2021; Heinrichs & Eickhoff, 2020; Holzinger et al., 2020; Rudin & Radin, 2019; Tsamados et al., 2021).”
BackgroundHolm, S. (2023) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180123000294 · Cited by 16
Artificial intelligence and identity: the rise of the statistical individual
Bjerring, J. C., & Busch, J. (2024) · AI & Society · doi:10.1007/s00146-024-01877-4 · Cited by 16
Self-citationSchmidt, E., Putora, P. M., & Fijten, R. (2025) · Philosophy & Technology · doi:10.1007/s13347-024-00834-9 · Cited by 14
“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.”
AI support for ethical decision-making around resuscitation: proceed with care
Biller-Andorno, N., Ferrario, A., Joebges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., & Krauthammer, M. (2020) · doi:10.1101/2020.08.17.20171769 · Cited by 67
Should AI-Enabled Medical Devices be Explainable?
Matulionytė, R., Nolan, P., Magrabi, F., & Beheshti, A. (2022) · International Journal of Law and Information Technology · doi:10.1093/ijlit/eaac015 · Cited by 14
Justice and the Normative Standards of Explainability in Healthcare
Kempt, H., Freyer, N., & Nagel, S. K. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00598-0 · Cited by 14
Algorithmic legitimacy in clinical decision-making
Holm, S. (2023) · Ethics and Information Technology · doi:10.1007/s10676-023-09709-7 · Cited by 16
Luu, V. P., Fiorini, M., Combes, S., Quemeneur, E., Bonneville, M., & Bousquet, P. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.09.3106 · Cited by 9
Underwater Animal Identification and Classification Using a Hybrid Classical-Quantum Algorithm
Pravin, S. C., Rohith, G., Kiruthika, V., Manikandan, E., Methelesh, S., & Manoj, A. L. (2023) · IEEE Access · doi:10.1109/access.2023.3343120 · Cited by 14
“Artificial Intelligence (AI) is increasingly employing Machine Learning (ML) techniques, including applications in medicine [12], [13], agriculture [14], industry [15], and marine ecology [5], [16], [17].”
BackgroundHerzog, C., Blank, S., & Stahl, B. C. (2024) · AI & Society · doi:10.1007/s00146-024-02082-z · Cited by 18
“…(2020) even seems to object to less direct human-to-human relationships in the medical context per se, which is not without merit, even from a consequen-tialist standpoint, as less intimate and patient-centric medicine is proven to be less effective, cf. (Bjerring and Busch 2021; Herzog 2022a).”
Exploring Patient Participation in AI-Supported Health Care: Qualitative Study
Ossa, L. A., Rost, M., Bont, N., Lorenzini, G., Shaw, D., & Elger, B. S. (2025) · JMIR AI · doi:10.2196/50781 · Cited by 13
“The formation of person-centered care requires that patients are (and feel like) equal partners in decision-making and that their knowledge, wants, and wishes are considered [36].”
Popowicz, D. M. (2021) · Philosophy of Medicine · doi:10.5195/pom.2021.49 · Cited by 13
Tokuoka, Y., Yamada, T., Mashiko, D., Ikeda, Z., Kobayashi, T., Yamagata, K., & Funahashi, A. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102432 · Cited by 12
Machine learning in healthcare and the methodological priority of epistemology over ethics
Grote, T. (2024) · Inquiry · doi:10.1080/0020174x.2024.2312207 · Cited by 14
Sperling, J. B., Welsh, W. E., Haseley, E., Quenstedt, S., Muhigaba, P. B., Brown, A., Ephraim, P. L., Shafi, T., Waitzkin, M. B., Casarett, D., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae255 · Cited by 15
Black-Box AI and Patient Autonomy
Prince, S., & Lim, J. (2025) · Minds and Machines · doi:10.1007/s11023-025-09729-w · Cited by 13
“Applying this criticism within the healthcare context, Bjerring and Busch (2021, p. 361) argue that: for patients to make decisions in autonomous and rational ways, it is a requirement that they have “the capacity to make sense of the [medical] information presented and can process it rationally to…”
InfluentialPossible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology
Aden, D., Zaheer, S., & Khan, S. Q. (2024) · Revista Española de Patología · doi:10.1016/j.patol.2024.04.003 · Cited by 10
Medical AI: is trust really the issue?
Mainz, J. T. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2023-109414 · Cited by 11
Grote, T. (2023) · Philosophy of Medicine · doi:10.5195/pom.2023.139 · Cited by 11
Swart, R., Boersma, L. J., Fijten, R., van Elmpt, W., Cremers, H., & Jacobs, M. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.24.00101 · Cited by 11
Davis, V. H., Qiang, J. R., Adekoya, I., Howse, D., Seshie, A. Z., Kosowan, L., Delahunty‐Pike, A., Abaga, E., Cooney, J., Robinson, M., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/52244 · Cited by 14
“Some articles have suggested restricting AI from decision-making [74,75] such as automatically deriving patient SDoH data and recording it into patient records.”
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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