ReviewInfectious diseases & clinical microbiology2026
Artificial Intelligence in Infectious Diseases and Clinical Microbiology.
Review in Infectious diseases & clinical microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is increasingly influencing healthcare, with growing relevance in infectious diseases and clinical microbiology. This review summarizes current applications of machine learning (ML), deep learning (DL), and large language models (LLMs) in these areas while critically evaluating their strengths and limitations. Although AI has shown promising performance in image analysis, molecular diagnostics, risk prediction, and clinical decision support, its routine implementation remains constrained by limited external validation, heterogeneous datasets, algorithmic bias, and challenges in generalizability and explainability. The review also discusses AI's emerging role in scientific writing and the ethical and practical considerations associated with its clinical adoption. Overall, current evidence suggests that AI should be regarded as a complementary decision-support technology whose safe and effective integration depends on rigorous validation, high-quality data, and appropriate clinical oversight.
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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.