Dear Dr. Hueda-Zavaleta,

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ACADEMIC EDITOR'S COMMENTS:-->--> -->-->Line 62-64: The authors stated "Traditional diagnostic methods, such as blood cultures and phenotypic antimicrobial susceptibility testing (AST), remain the gold standard but suffer from long turnaround times, typically requiring 24 to 72 h for actionable results. There are no references. More references should be cited, with this one (Zeng X, et al. 2025. Identification and characterization of an invasive, hyper-aerotolerant Campylobacter jejuni strain causing bacteremia in a pediatric leukemia patient. ASM Case Rep 1:e00060-24.

https://doi.org/10.1128/asmcr.00060-24) as an example (citing is optional).

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Academic Editor

PLOS One

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2. Thank you for stating the following in the Competing Interests section:

“Ari Frenkel is Chief Science Officer of Arkstone Medical Solutions, the company that produces the OneChoice report evaluated in this study. JC Gómez de la Torre works as the Director of Molecular Informatics at Arkstone Medical Solutions and as the Medical Director at Roe Lab in Perú. At the same time, Alicia Rendon and Miguel Hueda Zavaleta serve as Quality Assurance Managers at Arkstone Medical Solutions. These affiliations may be perceived as potential conflicts of interest. However, the study's design, data collection, analysis, interpretation, manuscript preparation, and the decision to publish the results were conducted independently, with no undue influence from the authors’ affiliations or roles within the company.”

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This paper presents a valuable contribution to the growing field of artificial intelligence-based clinical decision support systems (CDSS), focusing on the OneChoice® platform and its ability to guide antimicrobial therapy for bacteremia in a resource-limited setting. By comparing the recommendations of the CDSS with those of infectious disease (ID) specialists and non-specialists, the authors shed light on the potential of AI to standardize care and enhance stewardship however limited to the context of bloodstream infections.

Strengths of the Study

• Robust Methodology: The study employs a cross-sectional, observational design across tertiary healthcare facilities, integrating both molecular and phenotypic diagnostic data to ensure comprehensive evaluation.

• Expert Comparison: By including both ID specialists and non-specialists, the analysis highlights the impact of clinical expertise on concordance with CDSS recommendations, revealing substantial agreement, particularly among specialists (κ = 0.78).

• Quantitative Rigor: Statistical tools such as Cohen’s Kappa and logistic regression enrich the interpretation, and the use of a sizeable dataset (366 cases and 94 physicians) strengthens the findings.

• Emphasis on Antimicrobial Stewardship: The study demonstrates that the CDSS can help reduce inappropriate antibiotic use, especially carbapenem over-prescription, which is central to combating antimicrobial resistance.

Limitations and Critical Perspective

The following points could be addressed in the discussion:

• Narrow Clinical Focus: While bacteremia is a serious and high-impact condition, the paper’s exclusive focus on positive bloodstream infections means its findings apply only to a small minority of infectious disease patients. In clinical practice, most infections are not bacteremic, and the ability to act on blood culture results is relevant to a limited subset. Thus, the real-world impact of CDSS in guiding therapy for the broader population of infectious diseases may be overstated if conclusions are drawn solely from bacteremia cases.

• Scope of Clinical Decision Support: Effective decision support in infectious diseases must transcend the management of positive blood cultures. The spectrum of infectious syndromes—ranging from respiratory, urinary tract, and intra-abdominal infections to undifferentiated sepsis—requires nuanced recommendations that incorporate clinical presentation, local epidemiology, host factors, and dynamic changes in patient status. A system tailored only to bacteremia risks missing opportunities for intervention in far larger patient groups.

• Outcome Measures: The study centers on concordance rates rather than patient-centered outcomes, such as mortality, length of stay, or clinical improvement. While agreement with specialists is an important process metric, it does not guarantee improved patient outcomes. Future studies should measure the downstream effects of CDSS recommendations on clinical endpoints and stewardship goals.

• Generalizability: The context—a Peruvian tertiary care setting—limits extrapolation to different healthcare systems, especially where diagnostic infrastructure, stewardship programs, and clinical workflows differ.

• Potential for Overreliance: The authors rightly emphasize that CDSS should serve as supervised aids rather than autonomous systems. However, the risk remains that non-specialists may lean too heavily on AI recommendations without integrating broader clinical reasoning, especially in complex cases or where data inputs are incomplete.

• A glaring omission in the paper is a clear statement regarding the regulatory approval of the OneChoice® software tool. Nowhere in the manuscript is it made explicit whether the CDSS is authorized as a medical device under major regulatory frameworks such as the U.S. Food and Drug Administration (FDA) or has received a CE mark under European legislation.

Recommendations for Future Research

• Expand the scope of CDSS validation to include a wider array of infectious syndromes, incorporating real-world clinical scenarios beyond positive blood cultures.

• Assess the impact of CDSS on patient outcomes, antimicrobial resistance trends, and overall healthcare utilization, not merely process measures.

• Integrate dynamic clinical data streams and local epidemiological inputs, enabling the system to support nuanced decision-making across diverse infectious presentations.

• Promote multidisciplinary implementation and training to ensure that CDSS complements, rather than supplants, expert clinical judgment.

Conclusion

While the OneChoice® CDSS demonstrates substantial agreement with expert physicians in guiding therapy for bacteremia, its clinical effect is intrinsically limited by the small proportion of patients with positive blood cultures. The future of AI-driven decision support in infectious disease care lies in expanding beyond the narrow focus of bacteremia management to address the full complexity of infectious syndromes. Decision support platforms should be developed and validated to facilitate holistic, patient-centered, and context-sensitive care, ultimately enhancing outcomes for the broader population affected by infectious diseases.

Reviewer #2: AI-based clinical decision support systems (CDSS) have demonstrated significant potential in real-world applications for infectious disease management, particularly in addressing bacterial resistance.

The study assessed consistency by comparing CDSS recommendations with treatment decisions made by infectious disease specialists and non-specialists, using clinical data from bloodstream infection cases in healthcare institutions within a specific region. These data and analyses demonstrate the practical value of machine learning-based systems in infectious disease management. Such systems hold potential for enhancing early pathogen identification and optimizing antimicrobial treatment regimens, particularly improving the safety and rationality of antibiotic use in intensive care settings.

The paper features clear logic, well-organized methodology, transparent data and results, and explicit conclusions. There are no significant issues in the formulation of the scientific question, data analysis, or argumentation and discussion. It is already in a highly standard research paper format.

From a peer review perspective, if one were to be overly strict and nitpick, it might be possible to find some issues.

1. Data throughout the paper, tables and figures must maintain a consistent number of decimal places. While this may not pose a problem for researchers with some training, it could create unnecessary reading and comparison difficulties for cross-disciplinary readers—such as those engaged in interdisciplinary AI research within traditional biomedical fields, public health policymakers, patients, or the general public.

2. The truly appropriate conclusion for this paper is essentially the single sentence on lines 44–46. Or, to put it another way, lines 451–466, further condensed. Conversely, lines 468–474—the current conclusion of the paper—would be more suitable as an outlook within the discussion section rather than a conclusion directly derived from the research data presented.

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Reviewer #1: No

Reviewer #2: No

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Comparison of OneChoice® AI-based clinical decision support recommendations with infectious disease specialists and non-specialists for bacteremia treatment in Lima, Peru

PONE-D-25-43595R1

Dear Dr. Hueda-Zavaleta,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Benjamin M. Liu, MBBS, PhD, D(ABMM), MB(ASCP)

Academic Editor

PLOS One

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