Antiviral Probiotics: A New Concept in Medical Sciences
Ontology highlight
ABSTRACT:
SUBMITTER: AL KASSAA I
PROVIDER: S-EPMC7122849 | biostudies-literature | 2016 Dec
REPOSITORIES: biostudies-literature
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Project description:Summary Rapid advances in artificial intelligence (AI) and availability of biological, medical, and healthcare data have enabled the development of a wide variety of models. Significant success has been achieved in a wide range of fields, such as genomics, protein folding, disease diagnosis, imaging, and clinical tasks. Although widely used, the inherent opacity of deep AI models has brought criticism from the research field and little adoption in clinical practice. Concurrently, there has been a significant amount of research focused on making such methods more interpretable, reviewed here, but inherent critiques of such explainability in AI (XAI), its requirements, and concerns with fairness/robustness have hampered their real-world adoption. We here discuss how user-driven XAI can be made more useful for different healthcare stakeholders through the definition of three key personas—data scientists, clinical researchers, and clinicians—and present an overview of how different XAI approaches can address their needs. For illustration, we also walk through several research and clinical examples that take advantage of XAI open-source tools, including those that help enhance the explanation of the results through visualization. This perspective thus aims to provide a guidance tool for developing explainability solutions for healthcare by empowering both subject matter experts, providing them with a survey of available tools, and explainability developers, by providing examples of how such methods can influence in practice adoption of solutions. The bigger picture Adoption of AI tools in practical settings, such as for research/clinical tasks, has been hampered by a lack of transparency/interpretability of the models. After performing a review of different types of AI explainability (XAI) methods developed to better understand the predictions made by a model, we also develop a taxonomy to better classify the different approaches. We think that these XAI techniques are not sufficient to enhance practical implementations and illustrate via an example how user-driven XAI can be useful for different stakeholders in the healthcare domain. We identify and define three key personas involved in healthcare—data scientists, clinical researchers, and clinicians—and present an overview of the different approaches that can address their needs. The ultimate goal of adopting AI in medical practice and patient care goes beyond explainability and will need the development of extra layers of security and confidence, in particular regarding AI trustworthiness, as XAI transparent systems become prone to attacks that may reveal confidential information, and AI fairness, as systems developed and tested in diverse environments need to be expanded to real-world situations. A significant amount of research has focused on making artificial intelligence (AI) techniques more interpretable and post hoc explanations more accessible. This has been achieved through the development of a wide variety of so-called explainability methods. We here propose a taxonomy and review the most popular AI explainability (XAI) methods, illustrated with examples from healthcare and life sciences. We also propose that user-centered explainability is necessary for further practical adoption of AI approaches and illustrate this using healthcare-centered user examples.
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