ArticlePLOS digital health2023
Informing antimicrobial stewardship with explainable AI.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Bias Mitigation Across the Healthcare Artificial Intelligence Lifecycle: A Structured Narrative Review.Cureus · 2026Review
- Artificial Intelligence in Infectious Diseases and Clinical Microbiology.Infectious diseases & clinical microbiology · 2026Review
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- An AI-Driven Antimicrobial Resistance Surveillance Framework for Low-Resource Settings: A Pilot Study from Mogadishu, Somalia.Infection and drug resistance · 2026Article
- Artificial intelligence applications in biofilm research: computational approaches for biofilm analysis and antimicrobial prediction.Frontiers in microbiology · 2026Review
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Exploring opportunities to improve health equity with machine learning and artificial intelligence in healthcare epidemiology.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Review
- Unlocking antimicrobial resistance with multiomics and machine learning.Trends in microbiology · 2025Article
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
- Minimum Inhibitory Concentrations of Anidulafungin as a Potential Predictor of Biocide Susceptibility of Clade 1 Candidozyma (Candida) auris Isolates.MicrobiologyOpen · 2025Article
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- FromFrontiers in medicine · 2025Article
- Fighting antimicrobial resistance in Brazil: strengthening diagnostic stewardship, antimicrobial stewardship, and policies for a healthier future.Frontiers in public health · 2025Review
- Not forgetting the humanitarian contexts in the fight against antimicrobial resistance: Operational-driven reflection on knowledge and research gaps by Médecins Sans Frontières.PLOS global public health · 2025Review
- Predicting future hospital antimicrobial resistance prevalence using machine learning.Communications medicine · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Inflammaging in Multidrug-Resistant Sepsis of Geriatric ICU Patients and Healthcare Challenges.Geriatrics (Basel, Switzerland) · 2024Review
- Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It.Open forum infectious diseases · 2024Review
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
Abstract
The accuracy and flexibility of artificial intelligence (AI) systems often comes at the cost of a decreased ability to offer an intuitive explanation of their predictions. This hinders trust and discourage adoption of AI in healthcare, exacerbated by concerns over liabilities and risks to patients' health in case of misdiagnosis. Providing an explanation for a model's prediction is possible due to recent advances in the field of interpretable machine learning. We considered a data set of hospital admissions linked to records of antibiotic prescriptions and susceptibilities of bacterial isolates. An appropriately trained gradient boosted decision tree algorithm, supplemented by a Shapley explanation model, predicts the likely antimicrobial drug resistance, with the odds of resistance informed by characteristics of the patient, admission data, and historical drug treatments and culture test results. Applying this AI-based system, we found that it substantially reduces the risk of mismatched treatment compared with the observed prescriptions. The Shapley values provide an intuitive association between observations/data and outcomes; the associations identified are broadly consistent with expectations based on prior knowledge from health specialists. The results, and the ability to attribute confidence and explanations, support the wider adoption of AI in healthcare.
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.