Literature DB >> 30815459

Societal Issues Concerning the Application of Artificial Intelligence in Medicine.

Alfredo Vellido1.   

Abstract

BACKGROUND: Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the "2nd Meeting of Science and Dialysis: Artificial Intelligence," organized in the Bellvitge University Hospital, Barcelona, Spain. SUMMARY AND KEY MESSAGES: AI and ML are attracting much interest from the medical community as key approaches to knowledge extraction from data. These approaches are increasingly colonizing ambits of social impact, such as medicine and healthcare. Issues of social relevance with an impact on medicine and healthcare include (although they are not limited to) fairness, explainability, privacy, ethics and legislation.

Entities:  

Keywords:  Artificial intelligence in medicine; Machine learning; Social impact

Year:  2018        PMID: 30815459      PMCID: PMC6388581          DOI: 10.1159/000492428

Source DB:  PubMed          Journal:  Kidney Dis (Basel)        ISSN: 2296-9357


  15 in total

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6.  Confidentiality issues for medical data miners.

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5.  A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare.

Authors:  Amie J Barda; Christopher M Horvat; Harry Hochheiser
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6.  The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice.

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7.  Acceptance of the Use of Artificial Intelligence in Medicine Among Japan's Doctors and the Public: A Questionnaire Survey.

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  8 in total

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