AI in Healthcare practice: opportunities and challenges for clinical decision support systems

  • David R. Millen IBM Watson Health

Resumo


In the past few years there has been great optimism about the potential benefits of incorporating AI (cognitive) capabilities into healthcare products and services. Indeed, progress in Natural Language Processing (NLP) has made electronic health records both more accessible and comprehensible, advances in image processing algorithms has helped to early identify tumors, and large datasets with new discovery services can help with breakthrough insights in life sciences and drug discovery. Importantly, new AI-based solutions are embedded in the sociotechnical systems of clinical care and within complex regulatory environments and globally diverse cultural frameworks. In this talk, I will present several case studies of novel AI – based healthcare applications that have been introduced in recent years and share lessons learned along the way. Particular focus will be on design research challenges for healthcare products, including understanding complex workflows within clinical settings and highly specialized and diverse mental modals, and understanding multiple stakeholders and interdependent participants. Design considerations and emerging opportunities for AI-based clinical decision support systems will also be shared.
Palavras-chave: Artificial Intelligence, Healthcare, Clinical Decision
Publicado
11/10/2019
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MILLEN, David R. . AI in Healthcare practice: opportunities and challenges for clinical decision support systems. In: PLENÁRIAS - SIMPÓSIO BRASILEIRO DE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 18. , 2019, Vitória. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 2-2. ISSN 2177-9384. DOI: https://doi.org/10.5753/ihc.2019.8372.