Evidence map›Paper›PMID 42187463›Full record

ReviewBiosensors2026

Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.

Jiani Liu, Wang Gao, Chengxi Guo, Wenzhuo Cai, Ziyan Tang, Song Li, Yan Deng, Xiaoguang Qu, Zhu Chen

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jiani LiuMOE Key Laboratory of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang 421001, China.
Wang GaoMOE Key Laboratory of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang 421001, China.
Chengxi GuoState Key Laboratory of Advanced Fiber Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China.
Wenzhuo CaiMOE Key Laboratory of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang 421001, China.
Ziyan TangMOE Key Laboratory of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang 421001, China.
Song LiMOE Key Laboratory of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang 421001, China.
Yan DengMOE Key Laboratory of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang 421001, China.
Xiaoguang QuDepartment of Medical Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China.
Zhu ChenMOE Key Laboratory of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang 421001, China.ORCID 0000-0002-1246-3390

Funding

Hunan Provincial College Students' Innovation Training Program 202510555338Natural Science Foundation of Hunan Province 2024JJ7650Natural Science Foundation of Hunan Province 2026JJ50050Science and Technology Innovation Program of Hunan Province 2025RC3190
6 · The paper itself

Abstract

Rapid and accurate pathogen detection serves as a core component in infectious disease prevention and control, clinical diagnosis and treatment, and public health surveillance systems. Although traditional detection methods have been widely adopted in clinical practice, they still exhibit significant limitations in terms of detection speed, throughput, automation levels, and adaptability to complex samples. In recent years, artificial intelligence (AI) technology has provided novel technical pathways for pathogen detection by leveraging its strengths in feature learning, pattern recognition, and multidimensional data modeling. The core contribution of this review lies in providing a novel, integrated analytical framework that overcomes the limitations of existing reviews, which often focus on a single modality (such as imaging alone or molecular diagnostics alone). Based on this framework, this paper systematically reviews AI research progress in pathogen detection, focusing on typical applications of machine learning and deep learning algorithms in analyzing imaging data, molecular diagnostic data, sensor signals, microscopic images, and multimodal data. It summarizes AI's enabling value in enhancing detection sensitivity, specificity, automation, and point-of-care capabilities. Concurrently, this paper delves into key challenges facing AI-assisted pathogen detection, including data standardization, model generalization, interpretability, and clinical translation. It also outlines future trends toward intelligent, integrated, and clinically deployable applications. This paper aims to provide researchers and clinicians in the interdisciplinary field of artificial intelligence, biosensing, and clinical medicine with a comprehensive reference and roadmap for future development.

Indexed as

Artificial IntelligenceBiosensing TechniquesAlgorithmsHumansMachine Learningartificial intelligencedeep learningmachine learningpathogen detectionpoint-of-care testing

Identifiers

PMID42187463
PMCPMC13204018

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.