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

TL;DR

This paper tackles the bottleneck of analyzing massive, heterogeneous life-science data and scarce labeled microscopy data by surveying how AI can augment microscopy across molecular, organelle, and organ/tissue scales, where data are often $3D$, $4D$, or $5D$ tensors. It synthesizes key challenges (data labeling, noise, PSF variability, dimensional gaps) and outlines concrete strategies—synthetic data generation, physics-informed AI, cross-modality priors, SSL, transfer learning, and foundation-model deployment—to address them. It also maps current and future research directions and public resources to accelerate cross-disciplinary collaboration. By providing a concise, integrative view, the work aims to catalyze rapid adoption of AI in microscopy-enabled life-science research and to spur reproducible, scalable discovery.

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.

Figures (4)

• Figure 1: A classification of challenges in life sciences, categorized into molecular, organelle, and organ levels. Each tier highlights representative problems.
• Figure 2: Left: General assembly of a microscope: a schematic representation microscopegeneralassembly2006. Right: Microscopic images of culture of human lymphocyte cells. (i) fluorescence image of nuclear envelopes, (j) fluorescence image of interior nuclei (DNA), and (k) phase-contrast image of whole cells. microscopytypes2022algorithms.
• Figure 3: A comprehensive classification of diverse imaging and analytical methods, organized into structural, molecular, functional, and hybrid approaches.
• Figure 4: Left: PSF affecting the formation of an image by blurring the real object. Right: Different types of empirical PSFs in the green emission range, for a $\times 100$ 1.4 NA objective, with an oil immersion refractive index of $n=1.518$psftypes2005.