ReviewFrontiers in public health2026
Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Rapid diagnostics innovations for urinary tract infections using molecular biology, artificial intelligence and antimicrobial resistance surveillance: a comprehensive review.Molecular biology reports · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance and emerging infectious diseases remain significant challenges for global health, driving a need for advanced technological solutions. Artificial Intelligence (AI) expanded opportunities in clinical microbiology, infectious diseases, and public health by harnessing vast, structured datasets. Despite impressive analytical capabilities, the clinical integration of AI-based applications is hindered by its opacity. The "black-box" aspect undermines adoption into healthcare workflows. Explainable AI (XAI) methods, including intrinsically interpretable models and post-hoc interpretability tools, such as SHAP, LIME, and Grad-CAM, can address these transparency challenges. This narrative review is intended to be a primer for the interested clinician. It systematically evaluates recent advancements in XAI in the context of clinical applications for clinical microbiology, infectious diseases, and public health. We further discuss the ethical and regulatory landscape shaping AI adoption, including the critical role of open, quality-controlled data, robust performance metrics, and clear interpretability to ensure safe and effective clinical implementation. Lastly, we propose future directions, emphasizing interdisciplinary collaboration, international data-sharing initiatives, and tailored AI literacy training to facilitate trustworthy, equitable, and impactful use of AI in clinical microbiology and infectious diseases.
Indexed as
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.