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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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
The check wasn’t confident either way, so it gives no verdict — never disputed.
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Items 101–200 of 271
Ferrario, A. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2021-107482 · Cited by 11
Explainable online health information truthfulness in Consumer Health Search
Upadhyay, R., Knoth, P., Pasi, G., & Viviani, M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1184851 · Cited by 13
Datenreiche Medizin und das Problem der Einwilligung
Richter, G., Loh, W., Buyx, A., von Kielmansegg, S. G., & GmbH, S.-V. (2021) · Springer eBooks · doi:10.1007/978-3-662-62987-1 · Cited by 11
Allen, J. W., Wilkinson, D. J. C., & Savulescu, J. (2025) · Bioethics · doi:10.1111/bioe.70032 · Cited by 12
Alexandropoulou, C., Panagiotopoulos, I. E., Kleanthous, S., Dimitrakopoulos, G. J., Constantinou, I. P., Politi, E., Ntalaperas, D., Papageorgiou, X. S., Stylianides, C., Ioannides, N., et al. (2023) · IEEE International Conference on eScience · doi:10.1109/e-science58273.2023.10254863 · Cited by 12
“Recent work attempted to look into ethical implications of AI use in CDSS [35, 36, 37, 38, 39], highlighting a number of challenges that need to be taken into account.”
Hatherley, J. J. (2024) · Journal of Medical Ethics · doi:10.1136/jme-2024-109905 · Cited by 10
Explainable AI in Healthcare: Enhancing Transparency and Trust in Predictive Models
Yekollu, R. K., Ghuge, T. B., Biradar, S. S., Haldikar, S. V., & Kader, O. F. M. A. (2024) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc60852.2024.10690121 · Cited by 10
Deep learning models and the limits of explainable artificial intelligence
Bjerring, J. C., Mainz, J. T., & Munch, L. A. (2025) · Asian Journal of Philosophy · doi:10.1007/s44204-024-00238-8 · Cited by 16
“For further motivation of these points, see also Bjerring & Busch (2021). potentially leading to decreased adherence to treatment plans or reluctance to consent to certain procedures.”
Self-citationSteerling, E., Svedberg, P., Nilsén, P., Siira, E., & Nygren, J. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1565080 · Cited by 13
Shah, N., & Chen, H. H. (2024) · Preprints.org · doi:10.20944/preprints202402.1694.v1 · Cited by 9
Trustworthy artificial intelligence in healthcare
Subaşı, A., Özaltın, Ö., Mitra, A., Subasi, M. E., & Sarirete, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15299-3.00015-4 · Cited by 8
Pozzi, G., & Durán, J. M. (2024) · AI & Society · doi:10.1007/s00146-024-01875-6 · Cited by 14
“Similarly, Bjerring and Busch (2021) recognize in the black-box nature of AI systems the concrete possibility that it undermines the ethical ideal of patient-centered medicine.”
Ethical considerations for the application of artificial intelligence in pediatric surgery
Snyder, K. B., Stewart, R. A., & Hunter, C. J. (2024) · AI and Ethics · doi:10.1007/s43681-024-00525-3 · Cited by 8
Funer, F., Tinnemeyer, S., Liedtke, W., & Salloch, S. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01109-w · Cited by 9
“Some authors argue that “black-box medicine” “conflicts with core ideals of patient-centered medicine”, and it “is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient” [7].”
BackgroundKuppanda, P. M., Janda, M., Soyer, H. P., & Caffery, L. J. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.01.013 · Cited by 9
Arora, T., Muhammad-Kamal, H., & Beam, K. (2025) · Seminars in Perinatology · doi:10.1016/j.semperi.2025.152100 · Cited by 9
Relational Ethics and Structural Epistemic Injustice of AI in Medicine
Herzog, C., & Branford, J. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00987-1 · Cited by 11
“To be able to administer the best possible care, authors such as Bjerring and Busch (2021) have hence raised the possibility that utilizing AI may become an epistemic obligation, i.e., practitioners will be required to utilize AI as a source of knowledge, because it provides more reliable and…”
Technology solutions and challenges for innovations that will improve our lives in pandemic crisis
Vitliemov, P., & Stoycheva, B. (2022) · AIP conference proceedings · doi:10.1063/5.0090653 · Cited by 8
Machine learning, healthcare resource allocation, and patient consent
Webb, J. (2024) · The New Bioethics · doi:10.1080/20502877.2024.2416858 · Cited by 7
“As Bjerring and Busch (2021) argue in the context of diagnostic software, intrinsic algorithmic opacity is a barrier to providing answers to the ‘ why ’ questions patients may have about their care, an ability they consider central to promoting informed decision-making through the sharing of…”
InfluentialHarmful epistemic dependence on medical machine learning and its moral implications
Pozzi, G., Buijsman, S., & van den Hoven, J. (2025) · Journal of Medical Ethics · doi:10.1136/jme-2024-110552 · Cited by 7
“6 7 As Dennett 8 observed, refusing to use what is considered to be the best available technology, such as an ML application approved by the U.S. Food and Drug Administration (FDA) to diagnose cancer, would be as irresponsible as a doctor deciding to operate without previously considering the…”
Relational Ethics in the Administration of Healthcare Technology: AI, Automation and Proper Distance
Shaw, F., & McCosker, A. (2025) · Sociology of Health & Illness · doi:10.1111/1467-9566.70055 · Cited by 11
“These phrases reduce the direct responsibility of automated decision‐making or decision support tools for healthcare decisions (e.g., Bjerring and Busch 2021).”
Wang, R., Lyu, J., Yang, Y., Bi, S., Yu, H.-Y., Deng, H., Han, M., Ma, W., Wan, M., Peng, S., et al. (2025) · NEJM AI · doi:10.1056/aioa2401205 · Cited by 7
When is black-box AI justifiable to use in healthcare?
Prince, S., & Savulescu, J. (2025) · Big Data & Society · doi:10.1177/20539517251386037 · Cited by 8
“Explanations are generally viewed as both intrinsically valuable as an epistemic good and instrumentally valuable for achieving transparency (Bjerring and Busch, 2021; Ordish et al., 2020).”
InfluentialStudent voices: synergising artificial and human intelligence in health science education
Hu, P. Z., Qiu, C., Wang, A., Xu, S., Ye, J., Zou, J., Liu, S., Yuan, W., & Lv, J. (2026) · BMC Medical Education · doi:10.1186/s12909-025-08545-1 · Cited by 7
“Participants acknowledged that AI could not replicate the empathy, ethical judgement, or tacit knowledge embedded in human-clinician interactions, a view echoed by Akingbola et al. [17] and Bjerring and Busch [16].”
Epistemic Justice as a Condition for Meaningful Human Control Over Medical AI
Pozzi, G., & de Sio, F. S. (2026) · Minds and Machines · doi:10.1007/s11023-026-09762-3 · Cited by 10
“5 , the focus on epistemic justice as a precondition for control reveals the need for a more detailed analysis of patients’ participation beyond mere involvement in shared decision-making practices in AI-supported medical delivery (Bjerring & Busch, 2021; Salloch & Eriksen, 2024).”
González, R., Monterde, D., Papa, R., Kull, M. M., Anier, A., Balducci, F., Cano, I., Coca, M., Marco, M. D., Franceschini, G., et al. (2024) · International Journal of Integrated Care · doi:10.5334/ijic.7701 · Cited by 7
“…momentum propitiated by the continuous progress in digital technologies for data capture and management, artificial intelligence and the advances in medical sciences are shaping novel and stimulating scenarios for health promotion and care and positioning predictive medicine at the forefront [7–9].”
BackgroundHesjedal, M. B., Lysø, E. H., Solbjør, M., & Skolbekken, J. (2024) · Sociology of Health & Illness · doi:10.1111/1467-9566.13818 · Cited by 10
Raphael, M. W. (2022) · Sociology Compass · doi:10.1111/soc4.13047 · Cited by 8
Hsu, P.-J., Wu, C., Kuo, L.-C., Chen, M., Chen, Y., Huang, S.-F., Chuang, P., Jerng, J., & Chen, S. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020256 · Cited by 7
“The integration of artificial intelligence into PDA may also be a promising mechanism to enhance the effectiveness of SDM [26,27].”
BackgroundTriangle of Trust in Cancer Care? The physician, the patient, and artificial intelligence chatbot
Turner, J. H. (2023) · Cancer Biotherapy and Radiopharmaceuticals · doi:10.1089/cbr.2023.0112 · Cited by 5
Hatherley, J. J. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09829-2 · Cited by 9
“The implications of this are discussed further in section 4.”
AI‐Driven Innovations in Healthcare: Balancing Technology With Ethics
Garno, Z., Elkari, B., & Ourabah, L. (2025) · World Medical & Health Policy · doi:10.1002/wmh3.70031 · Cited by 6
Being Pragmatic About Reliance and Trust in Artificial Intelligence
Ferrario, A. (2025) · Minds and Machines · doi:10.1007/s11023-025-09754-9 · Cited by 7
“Further, Bjerring and Busch discuss the role of trust in ‘black-box’ medicine, namely, medical practice mediated by AI systems (Bjerring & Busch, 2021), while Alvarado argues that epistemic trust is the only legitimate kind of trust to allocate to AI technology (Alvarado, 2023).”
Hong, H., & Shin, S. (2024) · Journal of Medicine and Life Science · doi:10.22730/jmls.2024.21.3.92 · Cited by 8
The 100 most influential papers in medical artificial intelligence; a bibliometric analysis
Zahoor, F., Abdullah, M., Tahir, M. W., & Islam, A. M. (2024) · Journal of the Pakistan Medical Association · doi:10.47391/jpma.10064 · Cited by 8
Trocin, C., Skogås, J. G., Langø, T., & Kiss, G. H. (2022) · Springer eBooks · doi:10.1007/978-3-031-05276-7_9 · Cited by 7
Relational & culture-sensitive AI innovation
Herzog, C. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100135 · Cited by 5
Reeder, S. C., Howard, M., Sparrow, R., Foster, E., Ge, Z., & Kwan, P. (2025) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2025.110720 · Cited by 5
Health Equity in Clinical Research Informatics
Maurud, S., Henni, S. H., & Moen, A. (2023) · Yearbook of Medical Informatics · doi:10.1055/s-0043-1768720 · Cited by 8
“In other words, “black box” approaches to decision-making in patient care may incur significant risk, as neither practitioner nor patient can fully comprehend the steps leading to recommendations [3, 19].”
BackgroundBansal, S., & Sindhi, V. (2025) · doi:10.1002/9781394269969.ch14 · Cited by 5
Auf, H., Nygren, J., Lundgren, L. E., Petersson, L., & Svedberg, P. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1588759 · Cited by 5
“This could shift the dynamic away from human-centered approaches and toward paternalistic AI systems, ultimately reducing trust and collaboration (24, 27).”
Critical Engagement: The Value of Transparency of AI in Healthcare
Lim, J., Schaefer, G. O., & Savulescu, J. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-01009-w · Cited by 4
“These include concerns that transparency is necessary for accountability (Kemper & Kolk-man, 2019), legitimacy (Chesterman, 2021; Selbst & Barocas, 2018 ; Vainio-Pekka et al., 2023), or patient autonomy/informed consent (Amann et al., 2020 ; Bjerring & Busch, 2021 ; Vaassen, 2022).”
Dullabh, P., Zott, C., Gauthreaux, N. A., Peterson, C., Aronoff, A., Monkhouse, K., & Sittig, D. F. (2026) · Journal of Medical Internet Research · doi:10.2196/81628 · Cited by 7
“…GenAI’s short-term impact on clinicians and health care systems, such as relieving clinician workload and improving operational efficiency, to our knowledge, relatively few focus on the decision-making tools that support patient-centered care or on tools where the patient is the user [9-11].”
Ethical Implications of Artificial Intelligence: Ensuring Patient Data Security
Ali, A. (2024) · Approaches to global sustainability, markets, and governance · doi:10.1007/978-981-97-9555-0_8 · Cited by 8
Diabetes diagnosis system using modified Naive Bayes classifier
Alwan, J. K., Jaafar, D. S., & Ali, I. R. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i3.pp1766-1774 · Cited by 4
“It is important the entire dataset be searched when the most similar instances are searched for by the KNN algorithms [20]-[22] based on (1).”
Facial profiling technology and discrimination: a new threat to civil rights in liberal democracies
Gentzel, M. (2024) · Philosophical Studies · doi:10.1007/s11098-024-02156-0 · Cited by 5
“Face recognition technology (FRT) is a type of artificial intelligence (AI) that has become increasingly utilized in modern liberal democracies (Bjerring & Busch, 2021; Crawford et al., 2016 ; Helbing et al., 2017 ; OECD, 2019 ; West & Allen, 2018 ).”
The Epistemic Role of AI Decision Support Systems: Neither Superiors, Nor Inferiors, Nor Peers
Hirmiz, R. (2024) · Philosophy & Technology · doi:10.1007/s13347-024-00819-8 · Cited by 6
Why algorithmic speed can be more important than algorithmic accuracy
Mainz, J. T., Munch, L. A., Bjerring, J. C., & Godtfredsen, S. J. (2022) · Clinical Ethics · doi:10.1177/14777509221138750 · Cited by 4
Self-citationTracking Person-Centred Care Experiences Alongside Other Success Measures in Hearing Rehabilitation
Priday, G., Pedell, S., Vitkovic, J., & Story, L. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-11154-9_10 · Cited by 4
Artificial intelligence in nursing: From speculation to science
Nilsén, P. (2024) · Worldviews on Evidence-Based Nursing · doi:10.1111/wvn.12706 · Cited by 4
Reimagining Autonomy: Agentic AI in the Age of Human-Centric Innovation
Poornima, G., & Ahamed, N. N. (2025) · Springer eBooks · doi:10.1007/978-3-031-89424-4_19 · Cited by 4
Continuous learning as a threat to care
Sparrow, R., Daus, Z., Howard, M., Hatherley, J. J., & Kwan, P. (2025) · Health and Technology · doi:10.1007/s12553-025-01030-1 · Cited by 2
In Defense of Post Hoc Explanations in Medical AI
Hatherley, J. J., Munch, L. A., & Bjerring, J. C. (2026) · The Hastings Center Report · doi:10.1002/hast.4971 · Cited by 13
“In medicine, this has prompted a range concerns relating to patient safety, trust, health equity, and patient autonomy, among other things (Bjerring and Busch 2021; Hatherley 2020; Yoon et al. 2022).”
Self-citationThe Ethics and Epistemology of Clinician-AI Disagreement in Medicine: Beyond Opposition
Pozzi, G., Sand, M., & Jongsma, K. R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2632008 · Cited by 3
“In this respect, let us note that Bjerring and Busch (2021) admit that clinicians’ tacit knowledge may, on occasion, override the AI’s output.”
Clinical judgment: an essential method in medicine
Fava, G. A., Sonino, N., & Guidi, J. (2026) · Postgraduate Medical Journal · doi:10.1093/postmj/qgag030 · Cited by 3
Nurses’ experiences regarding role boundaries in collaborative AI-Assisted nursing: A meta-synthesis
Zhang, Z., Hou, J., Xu, C., Li, H., & Wan, H. (2026) · Social Science & Medicine · doi:10.1016/j.socscimed.2026.119385 · Cited by 3
Hirosawa, T., Yokose, M., Sakamoto, T., Hayashi, A., Harada, Y., Tokumasu, K., Mizuta, K., & Shimizu, T. (2026) · JMIR Formative Research · doi:10.2196/92016 · Cited by 3
Zoellick, J. C., Drexler, H., & Drexler, K. (2024) · TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis · doi:10.14512/tatup.33.1.48 · Cited by 3
Problematic aspects of medical artificial intelligence. Part 1
Бердутин, В. А., Romanova, T. E., Romanov, S. V., & Abaeva, O. P. (2023) · Sociology of Medicine · doi:10.17816/socm619132 · Cited by 3
Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic
de Sá, Â. A. R., Carvalho, J. D., & Naves, E. L. M. (2021) · AI & Society · doi:10.1007/s00146-021-01315-9 · Cited by 3
“However, Bjerring and Busch (2020) warned that the overuse and dependence on these intelligent systems can have a high cost for health professionals: loss of understanding and medical explanation (Bjerring and Busch 2020).”
BackgroundInfluentialSonar, A., & Weber, K. (2022) · Franz Steiner Verlag eBooks · doi:10.25162/9783515129770 · Cited by 5
Inexplicable AI in Medicine as a Form of Epistemic Oppression
Herzog, C. (2022) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas55053.2022.10227139 · Cited by 4
“[3], which may yield effective medical decisions, but it cannot be explained how it does so and why the decisions themselves are meaningful.”
BackgroundAlhur, A., Alhur, A. A., Alghamdi, S. J. H., Asiri, Z. I., Alzamil, S. K. S., Alfateih, S., Fataih, S. S. M. A., Alfahad, A. H., Alzmanan, S. M. M., Alghamdi, M. M. F., et al. (2023) · Journal of Population Therapeutics and Clinical Pharmacology · doi:10.53555/jptcp.v30i19.4744 · Cited by 3
AlphaGo's Move 37 and Its Implications for AI-Supported Military Decision-Making
Simpson, T. W. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003410379-15 · Cited by 3
Big Data and the Threat to Moral Responsibility in Healthcare
Tigard, D. W. (2021) · Springer eBooks · doi:10.1007/978-3-662-62987-1_2 · Cited by 3
Explainable Machine Learning, Patient Autonomy, and Clinical Reasoning
Keeling, G., & Nyrup, R. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198857815.013.27 · Cited by 3
Osmanodja, B., Samhammer, D., Roller, R., Beck, S., Burchardt, A., Duettmann, W., Dabrock, P., Gerndt, S., Möller, S., Ronicke, S., et al. (2023) · medRxiv · doi:10.1101/2023.01.12.23284468 · Cited by 3
Why we should talk about institutional (dis)trustworthiness and medical machine learning
Proost, M. D., & Pozzi, G. (2024) · Medicine Health Care and Philosophy · doi:10.1007/s11019-024-10235-6 · Cited by 2
The Ethics of Artificial Intelligence in Medicine: Preliminary Remarks
Gouveia, S. S. (2024) · Global Philosophy · doi:10.1007/s10516-024-09737-y · Cited by 3
“…that would be applied to both the patient (by accepting the output of the black-box that is complex and incompressible) and the medical expert (that will not be able to explain the “why’s” to the patient, since she/he is relaying on the “expertise” of the black-box) (cf. Bjerring and Busch 2020).”
InfluentialArtificial Intelligence in Pediatric Endocrinology
Pillai, S. S., & Ashraf, A. P. (2025) · Advances in Pediatrics · doi:10.1016/j.yapd.2024.12.003 · Cited by 2
Silencing in data science practices
Lassen, I. M. S., Bjerring, J. C., & Nielbo, K. L. (2025) · Big Data & Society · doi:10.1177/20539517251365228 · Cited by 4
“…systems takes its point of departure in various functionalities and characteristics of these systems – such as, notably, their opacity (e.g. Bjerring and Busch, 2021; Symons and Alvarado, 2022; Pozzi, 2023; Burrell, 2016) – we focus on the practices and activities of data scientists and…”
Self-citationKirkland‐Kyhn, H., Şengül, T., Karadağ, A., Akyaz, D. Y., Cevizci, T., & Teleten, O. (2025) · Advances in Skin & Wound Care · doi:10.1097/asw.0000000000000348 · Cited by 2
Ariso, J. M., & Bannister, P. (2025) · AI & Society · doi:10.1007/s00146-025-02757-1 · Cited by 8
Maximizing impact of explainable artificial intelligence in radiotherapy: a critical review
Heising, L. M., Wolfs, C. J. A., Ou, C. X. J., Hoebers, F. J. P., van Limbergen, E. J., Verhaegen, F., & Jacobs, M. J. G. (2026) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ae25b2 · Cited by 2
“Bjerring and Busch (2021) argue that using black-box AI in clinical practice conflicts with core elements of patient-centered care since the black-box AI decisions leave no room for exchange of information, explanation, and understanding.”
PatientSafeBench: Evaluating the Safety of Medical LLMs for Patient Use
Kim, M., Park, H., Kim, W., Choi, S. H., Kim, H. E., Sohn, H., Park, J., Kim, S., Yu, S.-C., & Oh, Y. (2025) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi67747.2025.11269553 · Cited by 3
“While legal compliance, transparency, and fairness are essential for trust-worthy medical LLMs [11], practical evaluations are lacking, risking disparities and undermining autonomy [12], [13].”
Reflexivity and positionality statements: a way to tackle “second-level arbitrariness” bias in AI?
Conti, L. G., & Seele, P. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06208-6 · Cited by 3
“…becoming increasingly ubiquitous, revolutio-nising entire sectors (Binns, 2018; Crawford & Calo, 2016; de Laat, 2018; Peters, 2022), from healthcare (Bjerring & Busch, 2021; Gichoya et al., 2023) to education (Bellas et al., 2024; X. Wang et al., 2024; Zhao et al., 2024), from HR management…”
Enhancing dental clinical education through AI: insights into post-clinical summaries
Luo, F., Li, J., Zhang, T., Huang, B., Jiang, L., Hua, C., & Hu, P. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08774-y · Cited by 2
“A promising approach to further enhance this capability would be to refine AI algorithms to simulate more diverse, dynamic clinical scenarios that closely resemble real-world complexities [15].”
Milota, M., Drogt, J., & Jongsma, K. R. (2023) · Digital Society · doi:10.1007/s44206-023-00072-0 · Cited by 2
“It is still unclear to what extent these systems will impact clinicians’ autonomy (Bjerring & Busch, 2021; Sambasivan et al., 2012; Strohm et al., 2020), under what circumstances these systems should be trusted (Asan et al., 2020; Durán & Jongsma 2021; Hatherley, 2020), and how their use will…”
BackgroundSchneider, D., & Weber, K. (2024) · TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis · doi:10.14512/tatup.33.1.08 · Cited by 3
“Otherwise, the system becomes a threat towards the ideal of human-centered reproducible and understandable science as well as patient-centered care (Bjerring and Busch 2021).”
BackgroundInfluentialGerdes, A., Fasterholdt, I., & Rasmussen, B. S. (2024) · Ugeskrift for Læger · doi:10.61409/v09230600 · Cited by 2
Neurosurgery, Explainable AI, and Legal Liability
Matulionytė, R., Molina, E. S., & Ieva, A. D. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-64892-2_34 · Cited by 2
Marr, C., & Tsang, Y. M. (2025) · Technical Innovations & Patient Support in Radiation Oncology · doi:10.1016/j.tipsro.2025.100300 · Cited by 3
Dale, R., Cheng, M., Pines, K. C., & Currie, M. E. (2025) · medRxiv · doi:10.1101/2025.05.20.25328009 · Cited by 2
Increasing the Patient-Centeredness of Predictive Analytics Tools
Crossnohere, N. L., Childerhose, J. E., & Bose‐Brill, S. (2022) · Patient · doi:10.1007/s40271-022-00595-7 · Cited by 1
“For most patient and clinician shareholders, predictive analytics powered by AI/ML remains a black box [5].”
BackgroundArtificial Intelligence in the System of “Physician-Patient”: Anthropocentrism vs Technocentrism
Izutkin, D. A. (2024) · Humanitarian Vector · doi:10.21209/1996-7853-2024-19-1-149-157 · Cited by 1
Intelligent Medicine Box Monitoring System Based on ZigBee Technology
Diao, J., Sun, P., Chen, J. B., Zhang, Z., & Chen, Z. (2024) · International Conference on Power Electronics and Artificial Intelligence (PEAI) · doi:10.1145/3674225.3674391 · Cited by 1
The role of humans in the future of medicine: Completing the cycle
Kenig, N., & Vives, A. M. (2025) · Metaverse · doi:10.54517/m3129 · Cited by 2
AI-Driven Predictive Analytics for Early Detection of High-Functioning Anxiety Symptoms
Alqaidoom, M. M., Ateeq, A. A., & Aljazrawi, Z. (2025) · Studies in big data · doi:10.1007/978-3-031-83915-3_60 · Cited by 1
Wee, A. K. J., Lin, M., Jenkins, M. A., & Lee, S. E. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-5837-5.ch004 · Cited by 2
Mahmood, T., Wang, Y., Khan, A. R., & Ayesha, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23979-3.00013-0 · Cited by 1
Managing High-Risk Surgery Using Artificial Intelligence
Kumar, A., & Singh, D. (2025) · Transactions on computer systems and networks · doi:10.1007/978-981-96-6703-1_10 · Cited by 1
Dedivitis, R. A., de Castro, M. A. F., Matos, L. L., Ribeiro, D. A., Duarte, B. P., & Kowalski, L. P. (2025) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.70296 · Cited by 1
Behdadi, D., Vanfleteren, L. E. G. W., & Sundemo, D. S. (2025) · AI & Society · doi:10.1007/s00146-025-02806-9 · Cited by 1
“This shift may reinforce reductive understandings of COPD by sidelining factors like patient testimony and lived experience, patient values and preferences, and psychosocial and environmental conditions (Bjerring and Busch 2021; Kenis 2025).”
Artificial Intelligence in Clinical Trials: From Protocol Design to Pharmacovigilance
Schulman, A., Fernández‐Torras, A., Gadiya, Y., Saeed, K., Mestres, J., & Tanoli, Z. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_24 · Cited by 1
Beyond (Relational) Autonomy: AI Healthcare Through the Lens of Care Ethics
Boldrini, M. (2026) · Integrated science · doi:10.1007/978-3-032-14537-6_9 · Cited by 1
Ameliorative medical AI: feminist hermeneutics and algorithmic normativity in healthcare
Kenis, D. (2026) · AI & Society · doi:10.1007/s00146-026-02890-5 · Cited by 1
“The implication of such accounts is normative: given their characterization as expert systems, physicians might have an epistemic obligation to follow CDSS recommendations (Bjerring and Busch 2021).”
Forecasting AI-induced technostress in healthcare: identifying techno-paralysis in decision-making
Jabbouri, R., Issa, H., & Jaber, J. (2026) · DECISION · doi:10.1007/s40622-025-00455-x · Cited by 1
Jecker, N. S., Ozeki-Hayashi, R., Nakazawa, E., & Fujita, T. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01073-w · Cited by 1
“Bjerring and Busch ( 2021, p. 359) claim that “in patient centered medicine… the ultimate goal of treatment is not necessarily identified with the eradication of a disease but rather with the creation of an informed decision space in which a patient’s specific values and preferences are represented…”
An actor-network-theory informed normative framework for the governance of AI-systems in medicine
Hahn, M., Samhammer, D., Tretter, M., & Dabrock, P. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-07957-8 · Cited by 2
“This opaque character then leads to outcomes that remain only partially comprehensible, which contradicts the principle of informed decision-making (Bjerring and Busch, 2021).”
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
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
