SynthesisFrontiers in digital health2026
AI-based clinical decision support systems for antibiotic therapy in common adult infections: a systematic review from development to clinical implementation.
Synthesis in Frontiers in digital health, 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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Antimicrobial resistance is among the leading causes of global mortality, and artificial intelligence (AI)-based clinical decision support systems (CDSS) have been proposed to guide antibiotic prescribing. Several AI models for antibiotic decision-making have been developed and validated, but their actual deployment in clinical practice and effect on patient outcomes have not been systematically characterized. Objective: To identify, classify, and appraise studies evaluating AI-based CDSS for antibiotic therapy in hospitalized adults with common infections, stratified by level of clinical implementation. Methods: Search-strategy development began on June 7, 2026; definitive searches of PubMed/MEDLINE, Scopus, Web of Science Core Collection, and Embase were executed on June 28, 2026, after submission of the PROSPERO record. Four reviewers independently screened records and assessed reports. Studies were classified as deployed in clinical practice (Level 1), evaluated against physicians' decisions without deployment (Level 2), or tested through retrospective simulation (Level 3). Risk of bias was assessed with RoB 2, ROBINS-I, or PROBAST, as appropriate. Owing to clinical and methodological heterogeneity, findings were synthesized narratively, with an outcome-specific GRADE assessment. Results: Of 1,157 records identified, 10 studies met the inclusion criteria: three Level 1 (30%), four Level 2 (40%), and three Level 3 (30%). The three deployed studies evaluated pneumonia, predominantly pulmonary Conclusions: Three of 10 included studies reached clinical deployment, whereas seven remained non-deployed. Deployed systems showed favorable outcome and process signals, but the evidence was heterogeneous and of low to very-low certainty, depending on the outcome. Prospective multicenter evaluations of deployed systems are required before computational performance can be interpreted as clinical effectiveness. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261435593, identifier CRD420261435593.
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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.