Artificial Intelligence and Patient-Centered Decision-Making

被引:1
作者
Bjerring J.C. [1 ]
Busch J. [1 ]
机构
[1] Department of Philosophy, Aarhus University, Jens Chr. Skous Vej 7, Aarhus C
关键词
Artificial intelligence and medicine; Black-box medicine; Evidence-based medicine; Medical decision-making; Patient-centered medicine;
D O I
10.1007/s13347-019-00391-6
中图分类号
学科分类号
摘要
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. © 2020, Springer Nature B.V.
引用
收藏
页码:349 / 371
页数:22
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共 70 条
[11]  
Di Nucci N., Should we be afraid of medical AI?, Journal of Medical Ethics, 45, 8, pp. 556-558, (2019)
[12]  
Doran D., Schulz S., Besold T.R., What does explainable AI really mean?, A New Conceptualization of Perspectives. Arxiv Preprint Arxiv, 1710, (2017)
[13]  
Epstein R.M., Fiscella K., Lesser C.S., Stange K.C., Why the nation needs a policy push on patient-centered health care, Health affairs, 29, 8, pp. 1489-1495, (2010)
[14]  
Esteva A., Robicquet A., Ramsundar B., Kuleshov V., DePristo M., Chou K., Cui C., Corrado G., Thrun S., Dean J., A guide to deep learning in healthcare, Nature medicine, 25, 1, pp. 24-29, (2019)
[15]  
Esteva A., Kuprel B., Novoa R.A., Ko J., Swetter S.M., Blau H.M., Thrun S., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 7639, pp. 115-118, (2017)
[16]  
Faes L., Liu X., Kale A., Bruynseels A., Shamdas M., Moraes G., Fu D.J., Wagner S.K., Kern C., Ledsam J.R., Schmid M.K., Deep learning under scrutiny: Performance against health care professionals in detecting diseases from medical imaging-systematic review and meta, Analysis, (2019)
[17]  
Ferroni P., Zanzotto F., Riondino S., Scarpato N., Guadagni F., Roselli M., Breast cancer prognosis using a machine learning approach, Cancers, 11, 3, (2019)
[18]  
Floridi L., Cowls J., Beltrametti M., Chatila R., Chazerand P., Dignum V., Et al., AI4People—An ethical framework for a good AI society: opportunities, risks, principles, and recommendations, Minds and Machines, 28, 4, pp. 689-707, (2018)
[19]  
Floridi L., The informational nature of personal identity, Minds & Machines, 21, pp. 549-566, (2011)
[20]  
Forssbaeck J., Oxelheim L., The multifaceted concept of transparency, The Oxford handbook of economic and institutional transparency, pp. 3-31, (2014)