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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Many AI systems used in medicine are highly reliable and sometimes have greater precision than human doctors.
“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)
The Introduction reports indications that some medical AI systems match experts’ diagnostic accuracy, and §2, “Black-box medicine,” gives examples that outperform specialists on particular tasks. But the paper cautions that evidence of reliability is preliminary and difficult to reproduce; it does not establish that many systems used in medicine are highly reliable. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFor specific medical treatments, the studies establishing their effectiveness are available in journals or online, so doctors can use that information to justify their belief that a treatment is effective.
“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
AI can help patients with diabetes manage their blood glucose levels, diet, and exercise using a chatbot that provides education and coaching based on natural language processing and machine learning.
“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]
The Introduction (page unavailable) mentions machine learning, natural-language processing, and an AI device for diabetic-retinopathy screening. Neither it nor the searched passages addresses a chatbot that coaches patients with diabetes on blood glucose, diet, or exercise (searched: diabetes chatbot, self-management, education, and coaching). (LLM, conf=0.76)
Experimental — accuracy not yet measuredBecause clinicians cannot fully comprehend inner workings of opaque decision aids, they cannot explain to patients how outcomes or recommendations were derived.
“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]
Black-box medicine conflicts with core ideals of patient-centered medicine.
“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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Interpretable, not black-box, artificial intelligence should be used for embryo selection
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“Opaque AI models compromise shared decision-making due to the inability of the clinician and patient to understand the model’s decision (Bjerring and Busch, 2020), for example, information as to why a particular embryo is selected or is not selected (such as, the number and symmetry of the cells, or if the cells are f…”
BackgroundEthics of AI in Radiology: A Review of Ethical and Societal Implications
Goisauf, M., & Abadía, M. C. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.850383 · Cited by 119 · see citing works
“This type of medicine cannot be described as “patient-centeredmedicine” (Bjerring and Busch, 2021), andmay have negative effects on the relationship of trust that is established between the patient and clinician.”
BackgroundAmann, J., Vayena, E., Ormond, K. E., Frey, D., Madai, V. I., & Blasimme, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279088 · Cited by 109 · see citing works
“Contrary to our participants’ views on decision-making power and responsibility, some authors have argued that in case of its superior performance in clinical tasks, clinicians would have an epistemic obligation to defer to the AI system’s recommendations or align their judgements [41,60].”
Evidence, ethics and the promise of artificial intelligence in psychiatry
McCradden, M., Hui, K., & Buchman, D. Z. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2022-108447 · Cited by 97 · see citing works
“12 31–33 Furthermore, some clinicians may have liability concerns if they do not follow the recommendation of an algorithmic system that contradicts their clinical judgement, a pressure that may increase should the use of AI tools become the standard practice in the future.”
BackgroundCordeiro, J. V. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.647897 · Cited by 92 · see citing works
From clinical decision support to clinical reasoning support systems
van Baalen, S., Boon, M., & Verhoef, P. (2021) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13541 · Cited by 84 · see citing works
“5 A similar point is made in a critical article on AI in clinical decision making and consequences for patients by Bjerring & Busch (2020).(28) 6 Surely, individual clinicians cannot be asked to review the datasets that have been used to train the system in detail.”
BackgroundKempt, H., & Nagel, S. K. (2022) · Journal of Medical Ethics · doi:10.1136/medethics-2021-107440 · Cited by 87 · see citing works
Black-box assisted medical decisions: AI power vs. ethical physician care
Chan, B. (2023) · Medicine Health Care and Philosophy · doi:10.1007/s11019-023-10153-z · Cited by 80 · see citing works
“This would also take away some significant element of choice, at least informed choice, since the doctor would not be able to substantively advise the patient which treatment to choose (Grote and Berens 2020, 208 and Bjerring and Busch 2021, 363).”
BackgroundGuiding principles for the responsible development of artificial intelligence tools for healthcare
Badal, K., Lee, C. M., & Esserman, L. J. (2023) · Communications Medicine · doi:10.1038/s43856-023-00279-9 · Cited by 77 · see citing works
Ethics of artificial intelligence in medicine
Savulescu, J., Giubilini, A., Vandersluis, R., & Mishra, A. (2024) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2023-279 · Cited by 68 · see citing works
“(22) On the basis of these arguments, some have argued that patients have a right to refuse diagnostics and treatment planning by AI systems in favour of a human-only alternative.”
What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach
Hildt, E. (2025) · Bioengineering · doi:10.3390/bioengineering12040375 · Cited by 70 · see citing works
“…have been characterized as an additional component, a “third authority”, that is difficult for medical professionals and patients to ignore and thus may negatively influence clinical decision making, doctor–patient relationships, and informed consent [12,16,17,48,49,78–80]; see also Section 3.7.”
InfluentialAI 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. (2022) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106786 · Cited by 67 · see citing works
“However, performance comparisons between AI systems and human experts suffer from the difficulty in reproducing and comparing results due to the lack of a unified approach [11,13,14].”
BackgroundFerrario, A., Facchini, A., & Termine, A. (2024) · Minds and Machines · doi:10.1007/s11023-024-09681-1 · Cited by 64 · see citing works
“Fueled by an increasing amount of high quality data and recent advances in ML methodologies (namely, deep learning), medical AI has demonstrated performance that often matches or exceeds that of human experts in various tasks (Bjerring & Busch, 2021; Esteva et al., 2017; Liu et al., 2019).”
InfluentialWhat kind of trust does AI deserve, if any?
Alvarado, R. (2023) · AI and Ethics · doi:10.1007/s43681-022-00224-x · Cited by 60 · see citing works
When Doctors and AI Interact: on Human Responsibility for Artificial Risks
Verdicchio, M., & Perin, A. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00506-6 · Cited by 66 · see citing works
Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space
Fallahpoor, M., Chakraborty, S., Pradhan, B., Faust, O., Barua, P. D., Chegeni, H., & Acharya, U. R. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107880 · Cited by 59 · see citing works
Möllmann, N. R. J., Mirbabaie, M., & Stieglitz, S. (2021) · Health Informatics Journal · doi:10.1177/14604582211052391 · Cited by 61 · see citing works
Sarno, L. D., Caroselli, A., Tonin, G., Graglia, B., Pansini, V., Causio, F. A., Gatto, A., & Chiaretti, A. (2024) · Biomedicines · doi:10.3390/biomedicines12061220 · Cited by 58 · see citing works
“A statistical analysis could provide some items to prove a correlation but, in many cases, this is not sufficient [113].”
Abbas, S. R., Seol, H., Abbas, Z., & Lee, S. W. (2025) · Healthcare · doi:10.3390/healthcare13141642 · Cited by 55 · see citing works
“This intersection marks the transitional zone from utility-focused AI to patient-centered AI, where the system’s intelligence is not just in its accuracy but also in its ability to engage and collaborate [105,106].”
An Assessment of the Barriers Impacting Responsible Artificial Intelligence
Merhi, M. I. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10276-3 · Cited by 58 · see citing works
“Binns et al (2018) and Bjerring and Busch (2020) argue that the obligation of explanations of the models and the outputs of AI may increase the level of trust toward AI and thus will increase its usage.”
BackgroundClinicians risk becoming “liability sinks” for artificial intelligence
Lawton, T., Morgan, P., Porter, Z., Hickey, S., Cunningham, A., Hughes, N., Iacovides, I., Jia, Y., Sharma, V., & Habli, I. (2024) · Future Healthcare Journal · doi:10.1016/j.fhj.2024.100007 · Cited by 60 · see citing works
“This is at odds with the goal of patient-centred decision-making, 23 as the AI cannot easily incorporate patient context and ideas, concerns, and expectations itself - this context is only added by the clinician choosing to accept or replace the AI’s output.”
BackgroundExplanatory pragmatism: a context-sensitive framework for explainable medical AI
Nyrup, R., & Robinson, D. A. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09632-3 · Cited by 50 · see citing works
“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
López-Vázquez, V., López-Guede, J. M., Chatzievangelou, D., & Aguzzi, J. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00711-w · Cited by 47 · see citing works
“The use of Machine Learning (ML) techniques by Artificial Intelligence (AI) is growing at a constant pace in several scientific fields, with applications in medicine [15], [55], agriculture [23], industry [53], and marine ecology [6, 7, 59, 81].”
BackgroundEnabling Fairness in Healthcare Through Machine Learning
Grote, T., & Keeling, G. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09658-7 · Cited by 44 · see citing works
“…experts, with the former being at least as good or strictly better than the latter, presume that the role of ML algorithms is akin to that of a peer (Gulshan et al., 2016; Esteva et al., 2017; De Fauw et al., 2018; see also Bjerring and Busch, 2021 ; Grote & Berens 2020; Grote & Berens, 2021).”
ResultGoisauf, M., Abadía, M. C., Akyüz, K., Bobowicz, M., Buyx, A., Colussi, I. A., Fritzsche, M.-C., Lekadir, K., Marttinen, P., Mayrhofer, M. T., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/71236 · Cited by 47 · see citing works
“For AI, providing comprehensive explanations of internal processes may often be unrealistic [75].”
Hindocha, S., & Badea, C. (2021) · AI and Ethics · doi:10.1007/s43681-021-00089-6 · Cited by 40 · see citing works
Biased Face Recognition Technology Used by Government: A Problem for Liberal Democracy
Gentzel, M. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00478-z · Cited by 38 · see citing works
Funer, F., Liedtke, W., Tinnemeyer, S., Klausen, A. D., Schneider, D., Zacharias, H. U., Langanke, M., & Salloch, S. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2022-108814 · Cited by 37 · see citing works
Clinical Interviewing: An Essential but Neglected Method of Medicine
Fava, G. A., Sonino, N., Aron, D. C., Balon, R., Montiel, C. B., Cao, J., Concato, J., Eőry, A., Horwitz, R. I., Rafanelli, C., et al. (2024) · Psychotherapy and Psychosomatics · doi:10.1159/000536490 · Cited by 36 · see citing works
The role of explainability in AI-supported medical decision-making
Gerdes, A. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00119-2 · Cited by 34 · see citing works
“However, it is difficult to imagine, and also well-known from the above-mentioned studies [10, 11], that physicians and patients would consider it legitimate if AI systems recommend a treatment solely based on the validation and reliability of the AI system [32].”
Relative explainability and double standards in medical decision-making
Kempt, H., Heilinger, J., & Nagel, S. K. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09646-x · Cited by 37 · see citing works
“(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.”
BackgroundFuner, F. (2022) · Medicine Health Care and Philosophy · doi:10.1007/s11019-022-10076-1 · Cited by 34 · see citing works
“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
Mikalef, P., & Parmiggiani, E. (2022) · Springer eBooks · doi:10.1007/978-3-031-05276-7 · Cited by 44 · see citing works
Randomized Controlled Trials in Medical AI
Genin, K., & Grote, T. (2021) · Philosophy of Medicine · doi:10.5195/philmed.2021.27 · Cited by 40 · see citing works
Ethics of artificial intelligence in embryo assessment: mapping the terrain
Koplin, J. J., Johnston, M., Webb, A. N. S., Whittaker, A. M., & Mills, C. (2024) · Human Reproduction · doi:10.1093/humrep/deae264 · Cited by 30 · see citing works
On the Ethical and Epistemological Utility of Explicable AI in Medicine
Herzog, C. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00546-y · Cited by 30 · see citing works
“As illustrated by Bjerring and Busch (2021), reports are increasingly published about medical AI surpassing the power of even experienced clinicians, leading to the likely state that medical personnel does in fact have an epistemic obligation to follow AI-based advice, perhaps even irrespective of…”
BackgroundInfluentialShould the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?
Sparrow, R., Hatherley, J. J., Oakley, J. G., & Bain, C. (2024) · The American Journal of Bioethics · doi:10.1080/15265161.2024.2337429 · Cited by 29 · see citing works
“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).”
BackgroundGould, D. J., Dowsey, M. M., Glanville-Hearst, M., Spelman, T. D., Bailey, J., Choong, P., & Bunzli, S. (2023) · Journal of Medical Internet Research · doi:10.2196/43632 · Cited by 27 · see citing works
“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].”
Weidener, L., & Fischer, M. (2024) · JMIR AI · doi:10.2196/51204 · Cited by 31 · see citing works
“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].”
BackgroundRandomised controlled trials in medical AI: ethical considerations
Grote, T. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107166 · Cited by 29 · see citing works
“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.”
BackgroundMaris, M. T., Koçar, A., Willems, D. L., Pols, J. J., Tan, H. L., Lindinger, G. L., & Bak, M. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01042-y · Cited by 30 · see citing works
“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].”
BackgroundKahraman, F., Aktaş, A., Bayrakceken, S., Çakar, T. E., Tarcan, H. S., Bayram, M. B., Durak, B., & Ülman, Y. I. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1428396 · Cited by 36 · see citing works
Begley, K., Begley, C., & Smith, V. A. (2020) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13515 · Cited by 27 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
“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 30 · see citing works
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 · see citing works
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 · see citing works
“) [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 · see citing works
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 25 · see citing works
“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 21 · see citing works
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 19 · see citing works
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 17 · see citing works
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 · see citing works
Responsible Use of AI in Military Systems
Schraagen, J. M. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003410379 · Cited by 19 · see citing works
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 · see citing works
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 17 · see citing works
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 16 · see citing works
“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 · see citing works
Funer, F. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00505-7 · Cited by 17 · see citing works
“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 · see citing works
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 · see citing works
Self-citationSchmidt, E., Putora, P. M., & Fijten, R. (2025) · Philosophy & Technology · doi:10.1007/s13347-024-00834-9 · Cited by 14 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
Algorithmic legitimacy in clinical decision-making
Holm, S. (2023) · Ethics and Information Technology · doi:10.1007/s10676-023-09709-7 · Cited by 14 · see citing works
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 · see citing works
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 · see citing works
“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 17 · see citing works
“…(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).”
Randomized Controlled Trials in Medical AI
Genin, K., & Grote, T. (2021) · Philosophy of Medicine · doi:10.5195/pom.2021.27 · Cited by 14 · see citing works
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 · see citing works
“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 · see citing works
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 13 · see citing works
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 · see citing works
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 14 · see citing works
Black-Box AI and Patient Autonomy
Prince, S., & Lim, J. (2025) · Minds and Machines · doi:10.1007/s11023-025-09729-w · Cited by 13 · see citing works
“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 · see citing works
Medical AI: is trust really the issue?
Mainz, J. T. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2023-109414 · Cited by 11 · see citing works
Grote, T. (2023) · Philosophy of Medicine · doi:10.5195/pom.2023.139 · Cited by 11 · see citing works
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 · see citing works
- 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
- Social Sciences › Safety Research › 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 ›
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