Machine Learning in Medical Diagnosis
A Framework for a Normative Evaluation of Benefits and Risks
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This book seeks to navigate between the optimism that has arisen from the promise of the potential of machine learning (ML) in healthcare, and the lack of clarity about what realistic risks and benefits we can foresee. Its main aim is to develop a relational, rights-based normative approach to evaluating the distribution of burdens and benefits of implementing ML in medical diagnosis. This framework, called the "Ecosystem of Moral Constellations", assumes that every person has an equal claim to the fundamental rights necessary to lead one s life, but recognizes that there may be conflicting in...
This book seeks to navigate between the optimism that has arisen from the promise of the potential of machine learning (ML) in healthcare, and the lack of clarity about what realistic risks and benefits we can foresee. Its main aim is to develop a relational, rights-based normative approach to evaluating the distribution of burdens and benefits of implementing ML in medical diagnosis. This framework, called the "Ecosystem of Moral Constellations", assumes that every person has an equal claim to the fundamental rights necessary to lead one s life, but recognizes that there may be conflicting interests that risk violating or infringing the rights of an individual or individuals, and that therefore an assessment of these tensions requires a situational prioritization of certain rights over others. This framework proposes to consider the normative relevance of relationships at different points of moral engagement to assess the potential tensions between these burdens and benefitsof these technologies. The author argues that decisions about the implementation of AI systems require more than an assessment of technical feasibility. Instead, it is imperative to consider the different normative goals and interests of the actors involved, the material capabilities of the tools, and the role they should play in the clinical workflow.
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