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In principle obstacles for empathic AI: why we can't replace human empathy in healthcare.
Montemayor, Carlos; Halpern, Jodi; Fairweather, Abrol.
Afiliação
  • Montemayor C; San Francisco State University, San Francisco, CA USA.
  • Halpern J; University of California, Berkeley, Berkeley, CA USA.
  • Fairweather A; San Francisco State University, San Francisco, CA USA.
AI Soc ; 37(4): 1353-1359, 2022.
Article em En | MEDLINE | ID: mdl-34054228
ABSTRACT
What are the limits of the use of artificial intelligence (AI) in the relational aspects of medical and nursing care? There has been a lot of recent work and applications showing the promise and efficiency of AI in clinical medicine, both at the research and treatment levels. Many of the obstacles discussed in the literature are technical in character, regarding how to improve and optimize current practices in clinical medicine and also how to develop better data bases for optimal parameter adjustments and predictive algorithms. This paper argues that there are also in principle obstacles to the application of AI in clinical medicine and care where empathy is important, and that these problems cannot be solved with any of the technical and theoretical approaches that shape the current application of AI in specific areas of clinical medicine in which care for patients is fundamental. This is important, because it generates specific risks that may be overlooked otherwise, and it justifies the necessity of human monitoring and emotional intervention in clinical medicine. Consequently, difficult issues concerning moral and legal responsibility may ensue if these in principle problems are ignored.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article