AI in Health: State of the Art, Challenges, and Future Directions

Fei Wang; Anita Preininger

doi:10.1055/s-0039-1677908

Yearbook of Medical Informatics, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 016-026
DOI: 10.1055/s-0039-1677908

Special Section: Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications

Survey

Georg Thieme Verlag KG Stuttgart

AI in Health: State of the Art, Challenges, and Future Directions

Fei Wang

¹Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, NY, USA

,

Anita Preininger∗

²IBM Watson Health, Cambridge, MA, USA

› Institutsangaben

Abstract

Summary

Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health.

Objective: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future.

Methods: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed.

Conclusion: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

Keywords

AI - health - deep learning - machine learning - natural language processing - federated learning

Volltext

Referenzen

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