Applications and Challenges of AI and Microscopy in Life Science Research: A Review

• URL: http://arxiv.org/abs/2501.13135v1
• Date: Wed, 22 Jan 2025 08:32:36 GMT
• Title: Applications and Challenges of AI and Microscopy in Life Science Research: A Review
• Authors: Himanshu Buckchash, Gyanendra Kumar Verma, Dilip K. Prasad,
• Abstract summary: This paper explores the intersection of AI and microscopy in life sciences, emphasizing their potential applications and associated challenges. We provide a detailed review of how various biological systems can benefit from AI, highlighting the types of data and labeling requirements unique to this domain. Specifically attention is given to microscopy data, exploring the specific AI techniques required to process and interpret this information.
• Score: 7.771558261139913
• License:
• Abstract: The complexity of human biology and its intricate systems holds immense potential for advancing human health, disease treatment, and scientific discovery. However, traditional manual methods for studying biological interactions are often constrained by the sheer volume and complexity of biological data. Artificial Intelligence (AI), with its proven ability to analyze vast datasets, offers a transformative approach to addressing these challenges. This paper explores the intersection of AI and microscopy in life sciences, emphasizing their potential applications and associated challenges. We provide a detailed review of how various biological systems can benefit from AI, highlighting the types of data and labeling requirements unique to this domain. Particular attention is given to microscopy data, exploring the specific AI techniques required to process and interpret this information. By addressing challenges such as data heterogeneity and annotation scarcity, we outline potential solutions and emerging trends in the field. Written primarily from an AI perspective, this paper aims to serve as a valuable resource for researchers working at the intersection of AI, microscopy, and biology. It summarizes current advancements, key insights, and open problems, fostering an understanding that encourages interdisciplinary collaborations. By offering a comprehensive yet concise synthesis of the field, this paper aspires to catalyze innovation, promote cross-disciplinary engagement, and accelerate the adoption of AI in life science research.

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