ReviewACS omega2025
Advancements in Breathomics: Special Focus on Electrochemical Sensing and AI for Chronic Disease Diagnosis and Monitoring.
Review in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Artificial intelligence-driven food and nutrition systems: from smart food production to personalized nutrition.Frontiers in nutrition · 2026Article
- Respiratory omics for early detection of COPD-to-lung cancer transition: mechanisms, biomarkers and clinical translation.Frontiers in cell and developmental biology · 2026Review
- Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions.Frontiers in nutrition · 2025Review
- Artificial intelligence in chronic kidney disease: Bibliometric and visual analysis of trends and future directions.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This Review examines the potential of breathomics in enhancing disease monitoring and diagnostic precision when integrated with artificial intelligence (AI) and electrochemical sensing techniques. It discusses breathomics' potential for early and noninvasive disease diagnosis with a focus on chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and lung cancer, which have been well studied in the context of VOC association with diseases. The noninvasive nature of exhaled breath analysis can be advantageous compared to traditional diagnostic methods for CKD, which often rely on blood and urine testing. VOC analysis can enhance spirometry and imaging methods used in COPD diagnosis, providing a more comprehensive picture of the disease's progression. Breathomics could also provide a less intrusive and potentially earlier diagnostic approach for lung cancer, which is now dependent on imaging and biopsy. The combination of breathomics, electrochemical sensing, and AI could lead to more personalized and successful treatment plans for chronic illnesses using AI algorithms to decipher complicated VOC patterns. This Review assesses the viability and effectiveness of combining breathomics with electrochemical sensors and artificial intelligence by synthesizing recent research findings and technological developments.
Identifiers
What OpenQuestion holds
Registered trials
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.