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

2025-01-22

AI Summary

Biological microscopic imaging data exhibit high diversity, severe annotation scarcity, and heterogeneous modalities, posing fundamental challenges for AI-driven analysis. Method: This study establishes the first AI-centric framework for computational microbiological image analysis, introducing a cross-modal learning paradigm and an explainability-aware evaluation system tailored to few-shot, multi-scale, and weakly supervised scenarios. The framework integrates CNNs and Transformers, self-supervised pretraining, weakly supervised segmentation, multimodal alignment, and eXplainable AI (XAI) techniques, and is embedded within a digital pathology workflow. Contribution/Results: We systematically categorize 12 representative application scenarios, identify 7 recurrent technical bottlenecks, and outline 5 forward-looking research trajectories. The framework standardizes interdisciplinary AI–microscopy–biology research practices and enables the development of reusable, modular toolchains for scalable, interpretable, and clinically translatable microscopic image analysis.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multimodal Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Artificial Intelligence in Biology
• Microscopy Data Processing
• Data Diversity and Annotation

Innovation

• AI in Bioinformatics
• Data Diversity Strategies
• Annotation Scarcity Solutions