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Artificial Intelligence (AI) is poised to revolutionize numerous aspects of human life, with healthcare among the most critical fields set to benefit from this transformation. Medicine remains one of the most challenging, expensive, and impactful sectors, with challenges such as information retrieval, data organization, diagnostic accuracy, and cost reduction. AI is uniquely suited to address these challenges, ultimately improving the quality of life and reducing healthcare costs for patients worldwide. Despite its potential, the adoption of AI in healthcare has been slower compared to other industries, highlighting the need to understand the specific obstacles hindering its progress. This review identifies the current shortcomings of AI in healthcare and explores its possibilities, realities, and frontiers to provide a roadmap for future advancements.
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This content will become publicly available on March 7, 2026
Tensors in High-Dimensional Data Analysis: Methodological Opportunities and Theoretical Challenges
Large amounts of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content from them, both statistically and computationally, presents unique and fundamental challenges. Addressing these challenges requires an interdisciplinary approach that brings together tools and insights from statistics, optimization, and numerical linear algebra, among other fields. Despite these hurdles, significant progress has been made in the past decade. This review seeks to examine some of the key advancements and identify common threads among them, under a number of different statistical settings.
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- PAR ID:
- 10633006
- Publisher / Repository:
- Annual Reviews
- Date Published:
- Journal Name:
- Annual Review of Statistics and Its Application
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2326-8298
- Page Range / eLocation ID:
- 527 to 551
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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