Professor and Medical Ethicist, Center for Ethics and Department of Rehabilitation Medicine, Emory University, Atlanta, Georgia.
President, Advanced Radiology Services Foundation, Clinical Assistant Professor, Michigan State University; Advanced Radiology Services, P.C., Grand Rapids, Michigan.
J Am Coll Radiol. 2022 Jul;19(7):816-820. doi: 10.1016/j.jacr.2021.11.014. Epub 2022 Feb 1.
It seems inevitable that diagnostic and recommender artificial intelligence models will ultimately reach a point when they outperform human clinicians. Just as antibiotics displaced a host of medicinals for treating infections, the superior performance of such models will force their adoption. This article contemplates certain ethical and legal implications bearing on that adoption, especially because they involve a clinician's exposure to allegations of malpractice. The article discusses four relevant considerations: (1) the imperative of using explainable artificial intelligence models in clinical care, (2) specific strategies for diminishing liability when a clinician agrees or disagrees with a model's findings or recommendations but the patient nevertheless experiences a poor outcome, (3) relieving liability through legislation or regulation, and (4) comprehending such models as "persons" and therefore as potential defendants in legal proceedings. We conclude with observations on clinician-vendor relationships and argue that, although advanced artificial intelligence models have not yet arrived, clinicians must begin considering their implications now.
诊断和推荐人工智能模型最终似乎将不可避免地达到超越人类临床医生的地步。正如抗生素取代了大量治疗感染的药物一样,此类模型的卓越性能将迫使它们被采用。本文考虑了与这种采用相关的某些伦理和法律含义,尤其是因为它们涉及到临床医生面临医疗事故指控的情况。文章讨论了四个相关的考虑因素:(1)在临床护理中使用可解释的人工智能模型的必要性;(2)当临床医生同意或不同意模型的发现或建议,但患者仍然出现不良后果时,减轻责任的具体策略;(3)通过立法或监管来减轻责任;(4)将此类模型理解为“人”,因此作为法律程序中的潜在被告。最后,我们对临床医生-供应商关系进行了观察,并认为,尽管先进的人工智能模型尚未出现,但临床医生现在就必须开始考虑它们的影响。
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