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Artificial Intelligence in Infectious Disease Diagnostic Technologies.

Chao Dong¹, Yujing Liu¹, Jiaqi Nie¹

¹Hebei Key Laboratory of Analysis and Control of Zoonotic Pathogenic Microorganism, College of Life Sciences, Hebei Agricultural University, Baoding 071001, China.

Diagnostics (Basel, Switzerland)

|October 29, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) significantly enhances infectious disease diagnostics through advanced data analysis and pattern recognition. This review synthesizes AI applications, highlighting benefits in pathogen surveillance and early outbreak detection while noting implementation challenges.

Keywords:

AI HTS POCT deep learning infectious disease diagnostics machine learning medical imaging

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Area of Science:

Infectious Disease Medicine
Artificial Intelligence
Medical Diagnostics

Background:

Artificial intelligence (AI) is rapidly integrating into infectious disease medicine, offering novel approaches to complex health challenges.
Global threats like pandemics, emerging infectious diseases, and antimicrobial resistance necessitate advanced diagnostic tools.
AI's capabilities in data mining, pattern recognition, and predictive analytics are crucial for improving diagnostic outcomes.

Purpose of the Study:

To systematically review and synthesize the current landscape of AI applications in infectious disease diagnostics.
To examine technological advances and practical outcomes of AI in areas like pathogen surveillance and imaging analysis.
To critically analyze challenges and limitations hindering the clinical translation of AI-based diagnostic technologies.

Main Methods:

Systematic literature search of PubMed and Web of Science databases.
Inclusion of studies published between 2018 and 2025 focusing on AI in infectious disease diagnostics.
Narrative review synthesizing findings on AI applications, technological advancements, and clinical translation barriers.

Main Results:

AI demonstrates transformative potential in diagnostic assistance, enhancing efficiency and accuracy.
Key applications include high-throughput sequencing (HTS) for pathogen surveillance, AI analysis of digital/radiological images, and AI-enhanced point-of-care testing (POCT).
Significant challenges include data scarcity, model generalizability issues, economic burdens, and regulatory hurdles.

Conclusions:

AI offers innovative solutions for pandemic control, emerging diseases, and antimicrobial resistance through enhanced diagnostics.
Overcoming data, generalizability, economic, and regulatory challenges is crucial for widespread clinical adoption.
This review provides a framework for future research in developing, validating, and standardizing AI-assisted infectious disease diagnostics.