The development and implementation of new technologies in healthcare has always been a complex task to undertake. Clinical settings are spaces where multiple convergences occur. There is a multiplicity of stakeholders with a plurality of values and potentially conflicting interests, intricate regulatory protocols, and frameworks, financial constraints, and incentives from public and private actors, and the sensitive matter of dealing with human health and life, which adds a particularly strong normative relevance to existing and emerging challenges.

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Machine Learning in Medical Diagnosis: The Chances

  • Leslye Denisse Dias Duran

摘要

The development and implementation of new technologies in healthcare has always been a complex task to undertake. Clinical settings are spaces where multiple convergences occur. There is a multiplicity of stakeholders with a plurality of values and potentially conflicting interests, intricate regulatory protocols, and frameworks, financial constraints, and incentives from public and private actors, and the sensitive matter of dealing with human health and life, which adds a particularly strong normative relevance to existing and emerging challenges.