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Artificial Intelligence in Healthcare and Education / Sepsis Diagnosis and Treatment · Journal article
Diagnostics · August 25, 2026
Encouraging direction, but not yet definitive.
This cross-sectional concordance study found moderate overall agreement between physicians and a machine-learning clinical decision support system (OneChoice®) for empirical UTI therapy selection (62.5% general concordance), with substantially higher concordance among infectious disease specialists (72.9% vs. 57.8%). When physician and CDSS recommendations diverged, an independent expert panel favoured the CDSS recommendation in 90.5–93.2% of cases, suggesting alignment with expert reasoning; however, the study did not evaluate clinical outcomes or comparative effectiveness.
Cross-sectional concordance survey with independent blinded expert adjudication. Verified physicians in Lima, Peru: 70 infectious disease specialists and 154 non-ID physicians evaluating 42 real UTI cases with complete culture and antimicrobial susceptibility data.. Intervention: Machine-learning-with-human-in-the-loop CDSS (OneChoice®) antimicrobial recommendations for empirical UTI therapy. Compared with: Physician-selected antimicrobial regimens, adjudicated against independent expert reference standard. n = 224. Lima, Peru.
First-choice concordance: 50.9%; alternative concordance: 40.6%; general concordance: 62.5% ID specialists showed higher concordance than non-ID physicians: 65.7% vs. 44.2% (first-choice), 51.4% vs. 35.7% (alternative), 72.9% vs. 57.8% (general); all p ≤ 0.034 ID specialty independently associated with concordance: adjusted OR 2.19–2.68 (p ≤ 0.016)
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The findings suggest that a machine-learning CDSS may support appropriate antimicrobial selection for UTIs in high-resistance settings, particularly where ID expertise is limited. However, the lack of patient outcome data means the clinical value remains uncertain, and implementation should be paired with outcome monitoring and physician education.
A sound cross-sectional concordance study with independent expert adjudication showing moderate physician–CDSS agreement and favourable expert validation of CDSS recommendations in discordant cases, but lacking comparative clinical effectiveness or patient outcome data.
As stated by the source record.
Quoted from the source exactly as published.
The findings suggest that a machine-learning CDSS may support appropriate antimicrobial selection for UTIs in high-resistance settings, particularly where ID expertise is limited. However, the lack of patient outcome data means the clinical value remains uncertain, and implementation should be paired with outcome monitoring and physician education.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Background: Antimicrobial resistance complicates the selection of appropriate regimens for urinary tract infections (UTIs), even when susceptibility data are available, particularly where infectious disease (ID) expertise is scarce. Machine learning clinical decision support systems (CDSS) may support prescribing, but evidence from Latin America is limited. The goal of this study was to evaluate the concordance between antimicrobial regimens selected by physicians and those recommended by a machine-learning-with-human-in-the-loop (ML-HITL) CDSS (OneChoice®), and to assess CDSS appropriateness against an independent, blinded expert reference standard. Methods: In this cross-sectional, survey-based concordance study conducted in Lima, Peru, 194 verified physicians contributed 224 eligible evaluations across 42 real UTI case codes with complete culture and antimicrobial susceptibility data. Of the 224 evaluations, 70 were contributed by infectious disease specialists and 154 by non-ID physicians. Participants selected OneChoice® and alternative antimicrobial regimens. Responses were compared with CDSS recommendations under three concordance definitions. Discordances were adjudicated by an external panel blinded to the source of the recommendation. Non-independence was addressed using cluster-robust methods. Results: First-choice, alternative, and general concordance were 50.9%, 40.6%, and 62.5%, respectively. ID specialists showed higher concordance than non-ID physicians (65.7% vs. 44.2%; 51.4% vs. 35.7%; 72.9% vs. 57.8%; all p ≤ 0.034). ID specialty was independently associated with concordance (adjusted OR 2.19–2.68; p ≤ 0.016). Among discordant evaluations, the external panel judged the CDSS recommendation to be preferable in 90.5–93.2% of cases. Physician–CDSS concordance was moderate and higher among ID specialists. The external adjudication findings indicate that the CDSS recommendations were frequently aligned with expert assessment when physician and CDSS recommendations differed; however, the study did not evaluate comparative clinical effectiveness or patient outcomes. Conclusions: Physician–CDSS concordance was moderate and higher among ID specialists, yet discordances overwhelmingly favored the CDSS on independent adjudication. These findings suggest the CDSS aligns with expert reasoning and may support antimicrobial selection in high-resistance settings.
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