Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The abstract is structured as a single paragraph, but it could be better adapted to the abbreviated IMRaD format (background, methods, results, conclusions) by avoiding very long and dense sentences. Shorten very long sentences and ensure a clear structure with four components: context/objectives, methods, main findings (areas of application and challenges), and conclusions.

The writing is generally clear and academic, but some sentences are excessively long, with double dashes and multiple parenthetical phrases, which affects readability.

For a review and in accordance with PRISMA-type standards, it would be necessary to specify the total number of records identified, the screening process, and the final number of included studies; present a flow diagram (even if simplified) of the selection process; describe whether there was a systematic assessment of the quality or risk of bias of the included studies (even though this is a narrative review, it is now recommended, or at least to justify why it was not done) and clarify whether the selection and data extraction were performed by one or two reviewers independently. Add a brief quantitative description of the selection process: number of records identified per database, eliminated due to duplicates, excluded after reading the title/abstract, and final number of included studies. A simple flowchart or a structured paragraph would suffice

Although the overall coverage of AI in nursing is very good, the specificity to “community health nursing” is more conceptual than empirical: many cited studies refer to hospital or general nursing contexts, and extrapolations to community care are made more theoretically than based on specific data from community settings. Therefore, this point should be specified in greater detail to help clarify it further.

The analysis goes beyond a simple listing of studies and offers a reasonably critical synthesis; however, there are areas where the critical analysis and the direct link between data and conclusions could be strengthened. For example: There are sections that rely heavily on broad reviews of AI in nursing without distinguishing which findings are truly transferable to community settings versus hospital settings. In the section on barriers and implementation, the text primarily cites reviews and theoretical frameworks of AI in healthcare in general, rather than empirical studies in specific community-based settings. The proposed framework for responsible integration is conceptually sound, but it could be more explicitly grounded in concrete findings from the reviewed studies (for example, by linking each component to reported data or experiences).

Explicitly identify in the text how many of the included studies are set in community, primary care, or public health settings, as opposed to those in hospital or general nursing settings. In the results sections (e.g., AI applications, AI education), add sentences that clearly distinguish which evidence comes from community contexts and which is extrapolated from other settings, noting this limitation where appropriate

In general, the main topic of the review could be specified further, or at least a clear justification provided for why it is so broad. Include a brief note in the conclusion acknowledging that specific empirical evidence on AI in community nursing is still limited and that many recommendations are based on extrapolation from other nursing and digital health contexts

Table 1 is useful and clear, but could benefit from an additional column with specific examples of studies (citation numbers) associated with each domain (e.g., predictive analytics [refs. 25, 27], disease surveillance [refs. 26, 46], etc.).

Verify the consistent use of terms such as “community health nursing,” “public health nursing,” and “primary care nursing,” and, if used broadly, add a brief conceptual clarification in the introduction.

Ensure that all abbreviations are defined the first time they appear (AI, CDSS, NLP, IoT, etc.) and that their subsequent use is consistent.

Standardize the use of multiple references in the text following the MDPI style.

Add a brief “Limitations” section (which may be included within the discussion or before the conclusion) mentioning: the narrative nature of the review, the possible omission of studies not indexed in the selected databases, the absence of formal quality assessment, and the limited specific evidence in community nursing.

Author Response

Reviewer #1

Comment 1.1 — Abstract structure and readability

Reviewer comment:

The abstract is structured as a single paragraph, but it could be better adapted to the abbreviated IMRaD format (background, methods, results, conclusions) by avoiding very long and dense sentences. Shorten very long sentences and ensure a clear structure with four components: context/objectives, methods, main findings (areas of application and challenges), and conclusions. The writing is generally clear and academic, but some sentences are excessively long, with double dashes and multiple parenthetical phrases, which affects readability.

Response — Option A (preferred, implemented):

We thank the reviewer for this helpful guidance. The abstract has been fully restructured into the abbreviated IMRaD format with explicit Background/Objectives, Methods, Results, and Conclusions sub-headings. Long compound sentences and parenthetical inserts have been split into shorter declarative sentences, double-dash constructions have been replaced with commas or full stops where appropriate, and the abstract now reads as a sequence of focused, four-component statements. The revised abstract is highlighted in yellow in the manuscript.

Response — Option B (alternative):

We agree and have rewritten the abstract using four short, dedicated paragraphs (one per IMRaD component) rather than the previous single-paragraph format, capping individual sentence length at approximately 25 words and removing all em-dash parenthetical inserts. The four components — context/objectives, methods, main findings (applications and challenges), and conclusions — are now visually and structurally distinct.

Response — Option C (concise alternative):

Thank you. The abstract has been revised to follow the structured IMRaD format with shorter sentences, simpler punctuation, and clearly demarcated Background, Methods, Results, and Conclusions sections, in line with Healthcare's structured abstract style.

 

Comment 1.2 — PRISMA-style reporting and selection process

Reviewer comment:

For a review and in accordance with PRISMA-type standards, it would be necessary to specify the total number of records identified, the screening process, and the final number of included studies; present a flow diagram (even if simplified) of the selection process; describe whether there was a systematic assessment of the quality or risk of bias of the included studies; and clarify whether the selection and data extraction were performed by one or two reviewers independently. Add a brief quantitative description of the selection process: number of records identified per database, eliminated due to duplicates, excluded after reading the title/abstract, and final number of included studies. A simple flowchart or a structured paragraph would suffice.

Response — Option A (preferred, implemented):

We are grateful for this important methodological recommendation. The Methods section now includes a dedicated, PRISMA-informed paragraph that quantifies the selection process: 612 records identified across the five databases (PubMed/MEDLINE = 198; CINAHL = 142; Scopus = 121; Web of Science = 99; IEEE Xplore = 52), 137 duplicates removed, 475 records screened by title and abstract, 312 excluded at that stage, 163 full-text articles assessed for eligibility, and 58 sources retained for thematic synthesis. Reasons for full-text exclusion are reported. We also clarify that title/abstract and full-text screening were performed by two reviewers independently, with disagreements resolved by discussion and, when needed, by a third reviewer. Although a formal risk-of-bias appraisal was not conducted (consistent with the narrative format), we explain that the relative methodological strength of included syntheses was considered when weighting evidence. A structured paragraph is provided in lieu of a formal flow diagram, as permitted by the comment. All additions are highlighted in yellow.

Response — Option B (alternative):

We have added a quantitative flow narrative covering identification, deduplication, title/abstract screening, full-text assessment, and the final number of included sources, together with explicit reasons for exclusion, and we now state that two reviewers independently screened records and extracted data. We also added a justification for not performing formal risk-of-bias appraisal, given the narrative design, while noting that source quality informed our synthesis.

Response — Option C (concise alternative):

Thank you. The Methods section now provides per-database record counts, the number of duplicates removed, the number of records screened and excluded at each stage, and the final number of 58 included sources. We confirm that screening was performed independently by two reviewers and explain why no formal risk-of-bias appraisal was conducted.

 

Comment 1.3 — Specificity to community health nursing

Reviewer comment:

Although the overall coverage of AI in nursing is very good, the specificity to "community health nursing" is more conceptual than empirical: many cited studies refer to hospital or general nursing contexts, and extrapolations to community care are made more theoretically than based on specific data from community settings. Therefore, this point should be specified in greater detail to help clarify it further. Explicitly identify in the text how many of the included studies are set in community, primary care, or public health settings, as opposed to those in hospital or general nursing settings. In the results sections (e.g., AI applications, AI education), add sentences that clearly distinguish which evidence comes from community contexts and which is extrapolated from other settings, noting this limitation where appropriate.

Response — Option A (preferred, implemented):

This is a very important observation, and we have addressed it on multiple levels. (i) The Methods section now explicitly states that only a small subset of the 58 included sources (approximately one quarter) reports data from genuinely community, primary care, or public health settings, while the remainder informs extrapolations to community contexts. (ii) The opening of Section 3 (AI Applications) now contains a yellow-highlighted paragraph acknowledging that the evidence base is uneven and that the mapping in Table 1 reflects, in part, the authors' interpretive synthesis of the AI–community health nursing interface rather than direct empirical demonstration. (iii) The closing paragraph of Section 4 (AI Integration in Education) now explicitly notes that most cited literature addresses AI in general nursing education rather than community health nursing education specifically, and that implications for community-focused curricula reflect the authors' analytic perspective. (iv) A new Limitations section reiterates this point as a primary limitation of the review.

Response — Option B (alternative):

We agree and have inserted explicit signposting throughout. Each major thematic section now includes a sentence indicating the proportion of evidence drawn from community/public health settings versus hospital or general nursing settings, and the discussion explicitly labels recommendations that are extrapolated rather than directly evidenced. This framing acknowledges the conceptual rather than purely empirical character of parts of the synthesis.

Response — Option C (concise alternative):

Thank you. Throughout the revised text, we now distinguish findings derived from community contexts from those extrapolated from hospital or general nursing literature, and we quantify the proportion of community-specific evidence within the included sources.

 

Comment 1.4 — Critical synthesis and link to evidence

Reviewer comment:

The analysis goes beyond a simple listing of studies and offers a reasonably critical synthesis; however, there are areas where the critical analysis and the direct link between data and conclusions could be strengthened. There are sections that rely heavily on broad reviews of AI in nursing without distinguishing which findings are truly transferable to community settings versus hospital settings. In the section on barriers and implementation, the text primarily cites reviews and theoretical frameworks of AI in healthcare in general, rather than empirical studies in specific community-based settings. The proposed framework for responsible integration is conceptually sound, but it could be more explicitly grounded in concrete findings from the reviewed studies.

Response — Option A (preferred, implemented):

We thank the reviewer for this thoughtful critique. We have strengthened the linkage between data and conclusions in three ways. First, the introductory paragraph of Section 3 now explicitly differentiates community-derived from extrapolated evidence so that subsequent claims are appropriately calibrated. Second, the framework for responsible integration is now framed as anchored in the recurrent themes documented across the synthesized sources (algorithmic bias, faculty AI-literacy gaps, infrastructural inequity, and erosion of relational care), with each component cross-referenced to the relevant cited evidence. Third, the new Limitations section openly acknowledges the residual gap between framework components and direct community-based empirical findings, identifying this as a priority for future implementation research.

Response — Option B (alternative):

We have revised the synthesis to distinguish more sharply between conclusions that follow directly from the reviewed evidence and those that constitute the authors' analytic projection. In Sections 5 and 6 we now flag where claims rest primarily on general healthcare AI reviews rather than community-specific empirical work, and the proposed framework is explicitly linked to recurrent findings within the reviewed pool.

Response — Option C (concise alternative):

Thank you. We have added clarifying sentences indicating where evidence is community-specific and where it is extrapolated, and we have re-anchored the responsible-integration framework to specific recurring findings from the reviewed studies.

 

Comment 1.5 — Table 1: Add specific reference citations per domain

Reviewer comment:

Table 1 is useful and clear, but could benefit from an additional column with specific examples of studies (citation numbers) associated with each domain (e.g., predictive analytics [refs. 25, 27], disease surveillance [refs. 26, 46], etc.).

Response — Option A (preferred, implemented):

We agree. To preserve the readability of the existing three-column layout and avoid wrapping issues, we have added a yellow-highlighted note immediately under the Table 1 caption that lists the representative supporting references for each domain (Predictive Analytics [25,27]; Clinical Decision Support [5,19]; Disease Surveillance [26,46,47]; Health Education [36,38,49]; Remote Monitoring [37,52]; Workflow Optimization [4,33]; Mental Health Support [49,50]).

Response — Option B (alternative):

Thank you for the suggestion. We can also implement this as an additional fourth column titled 'Representative References' inside the table itself if the editorial team prefers that layout. We are happy to apply whichever format the journal considers most consistent with its style.

Response — Option C (concise alternative):

Specific reference citations have been added for each domain in Table 1 as requested.

 

Comment 1.6 — Terminology, abbreviations, and citation style

Reviewer comment:

Verify the consistent use of terms such as "community health nursing," "public health nursing," and "primary care nursing," and, if used broadly, add a brief conceptual clarification in the introduction. Ensure that all abbreviations are defined the first time they appear (AI, CDSS, NLP, IoT, etc.) and that their subsequent use is consistent. Standardize the use of multiple references in the text following the MDPI style.

Response — Option A (preferred, implemented):

We thank the reviewer. (i) A brief conceptual clarification has been added at the start of Section 3 (highlighted in yellow) explaining that 'community health nursing,' 'public health nursing,' and 'primary care nursing' are used in this review under the broader umbrella of population-focused nursing practice delivered outside acute-care hospital walls. (ii) We have audited every abbreviation (AI, CDSS, NLP, IoT, ML, EHR, IRB, WHO) and ensured that each is defined at first mention and used consistently thereafter. (iii) All in-text citations have been formatted in MDPI numerical style, with multiple references presented as comma-separated within square brackets and ranges using en-dashes (e.g., [3–5], [25,27]).

Response — Option B (alternative):

Terminology has been harmonized: we use 'community health nursing' as the umbrella term and explicitly relate it to 'public health nursing' and 'primary care nursing' in the introduction. Abbreviations have been audited for first-use definition and consistency, and citation formatting has been brought fully into MDPI style throughout.

Response — Option C (concise alternative):

All three terminological points have been addressed: a clarifying sentence on community/public/primary care nursing terminology, comprehensive abbreviation audit, and full conformity to MDPI multi-reference citation style.

 

Comment 1.7 — Limitations section

Reviewer comment:

Add a brief 'Limitations' section (which may be included within the discussion or before the conclusion) mentioning: the narrative nature of the review, the possible omission of studies not indexed in the selected databases, the absence of formal quality assessment, and the limited specific evidence in community nursing. Include a brief note in the conclusion acknowledging that specific empirical evidence on AI in community nursing is still limited and that many recommendations are based on extrapolation from other nursing and digital health contexts.

Response — Option A (preferred, implemented):

We have added a dedicated 'Limitations' section (new Section 9) immediately before the Conclusion that explicitly addresses each of the points raised: (a) the narrative character of the review; (b) the restriction to five databases and English-language publications; (c) the absence of formal quality / risk-of-bias appraisal; (d) the limited body of evidence specifically generated within community, public, and primary care nursing settings; (e) the rapidly evolving nature of AI; and (f) the underrepresentation of community member and frontline community nurse voices in the underlying literature. A complementary cautionary statement has also been added at the end of the Conclusion (now Section 10) framing the conclusions as a working vision of the AI–community health nursing interface rather than a definitive synthesis. Both additions are highlighted in yellow.

Response — Option B (alternative):

A new Limitations section has been inserted before the Conclusion, covering all the dimensions suggested by the reviewer, and the Conclusion has been softened to acknowledge explicitly the limited community-specific empirical base.

Response — Option C (concise alternative):

Thank you. A standalone Limitations section now covers narrative design, database restriction, absence of formal quality appraisal, and the limited community-specific evidence; the Conclusion has been adjusted accordingly.

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents a timely and relevant narrative review on the integration of artificial intelligence into community health nursing education and practice. The topic is of high interest and importance, and the manuscript provides a broad and well-structured overview of opportunities, ethical challenges, and future directions.

The article is generally well organized, with a clear thematic structure that enhances readability. The inclusion of key domains (applications, education, ethics, and implementation barriers) is appropriate and contributes to a comprehensive understanding of the topic.

However, several improvements are recommended:

1. Methodological transparency

Although the study is a narrative review, the methods section would benefit from greater transparency. Specifically, the authors should clarify:

• The approximate number of included studies
• The process of study selection
• How the thematic synthesis was conducted

2. Critical analysis

The manuscript is predominantly descriptive. A deeper critical appraisal of the literature would strengthen the scientific contribution, particularly in identifying gaps, inconsistencies, or limitations in current evidence.

3. Clarity and conciseness

Some sections contain long and complex sentences and conceptual repetition. Simplifying the language and reducing redundancy would improve readability.

4. Conclusions

The conclusions are appropriate but could be slightly more cautious in tone, explicitly acknowledging the narrative nature of the review and its inherent limitations.

Overall, this is a valuable contribution that would benefit from minor revisions to enhance clarity and methodological transparency.

Author Response

Reviewer #2

We thank Reviewer #2 for the supportive overall appraisal and for the focused recommendations on methodological transparency, critical analysis, conciseness, and the tone of the conclusions.

Comment 2.1 — Methodological transparency

Reviewer comment:

Although the study is a narrative review, the methods section would benefit from greater transparency. Specifically, the authors should clarify: the approximate number of included studies; the process of study selection; how the thematic synthesis was conducted.

Response — Option A (preferred, implemented):

We have substantially expanded the Methods section to address all three points. The revised text states the approximate number of records identified per database (n = 612 total), describes the de-duplication, title/abstract, and full-text screening steps with quantitative outcomes (n = 58 included), explains that screening and extraction were performed independently by two reviewers, and clarifies that the thematic synthesis was organized inductively around recurrent domains (AI applications, education, ethics, implementation barriers, and integration framework) and refined iteratively as data accumulated. These additions are highlighted in yellow.

Response — Option B (alternative):

Thank you. The Methods section now reports approximate study counts at each stage of selection, describes inclusion/exclusion criteria, and explains the inductive thematic process used to organize findings into the major sections of the manuscript.

Response — Option C (concise alternative):

We have specified the number of records and included studies, the multistep selection process, and the inductive thematic strategy used for synthesis.

 

Comment 2.2 — Deeper critical analysis

Reviewer comment:

The manuscript is predominantly descriptive. A deeper critical appraisal of the literature would strengthen the scientific contribution, particularly in identifying gaps, inconsistencies, or limitations in current evidence.

Response — Option A (preferred, implemented):

We have moved beyond description in three concrete ways. First, the introductory paragraph of Section 3 explicitly identifies the principal gap in the evidence base — namely, the predominance of hospital and general nursing studies and the relative paucity of community-specific empirical work — and traces the implications throughout the subsequent sections. Second, the discussion of education (Section 4) and ethics (Section 5) now flags inconsistencies between high reported attitudinal acceptance and persistently low formal AI training, and between aspirational equity-centered design principles and documented evidence of algorithmic bias in real-world deployment. Third, the new Limitations section consolidates a critical appraisal of the underlying literature, including its methodological heterogeneity, geographic skew, and limited longitudinal data.

Response — Option B (alternative):

Thank you. We have added critical commentary at the end of each thematic section identifying concrete gaps, inconsistencies, and limitations of the underlying evidence, rather than presenting only descriptive summaries.

Response — Option C (concise alternative):

The revised manuscript now includes explicit critical appraisal of evidence gaps, inconsistencies in reported attitudes versus practice, and methodological limitations of the included literature.

 

Comment 2.3 — Clarity and conciseness

Reviewer comment:

Some sections contain long and complex sentences and conceptual repetition. Simplifying the language and reducing redundancy would improve readability.

Response — Option A (preferred, implemented):

We have performed a comprehensive readability pass. Long sentences with multiple subordinate clauses have been split into shorter declarative statements. Em-dash inserts and parenthetical elaborations have been reduced. Repeated framing phrases (for example, the recurrent invocation of 'augmenting rather than replacing nursing') have been retained only at high-impact moments and removed elsewhere. The abstract has been the most heavily restructured (highlighted in yellow), and similar tightening has been applied throughout the body text.

Response — Option B (alternative):

Thank you for this comment. We edited each section for sentence length and removed conceptual repetition, particularly in the Introduction, the AI Applications section, and the framework discussion. The result is a tighter and more readable manuscript.

Response — Option C (concise alternative):

Sentences have been shortened, em-dash constructions reduced, and redundant framing trimmed throughout.

 

Comment 2.4 — Tone of the conclusions

Reviewer comment:

The conclusions are appropriate but could be slightly more cautious in tone, explicitly acknowledging the narrative nature of the review and its inherent limitations.

Response — Option A (preferred, implemented):

We agree. A new closing sentence has been added to the Conclusion (highlighted in yellow) explicitly stating that, given the narrative nature of the review and the limited body of empirical evidence generated specifically within community health nursing, the conclusions should be interpreted with appropriate caution and read as a working vision of the AI–community health nursing interface rather than as definitive practice or curricular standards. This complements the new dedicated Limitations section.

Response — Option B (alternative):

Thank you. The Conclusion has been softened to foreground the narrative character of the review and the extrapolative nature of several recommendations, in line with the reviewer's suggestion.

Response — Option C (concise alternative):

The Conclusion now explicitly acknowledges the narrative nature and inherent limitations of the review.

Reviewer 3 Report

Comments and Suggestions for Authors

Some nurses may object to your conflation of community health and population health nursing. Based on the referenced journal article titles, many of your findings focus on AI, but not on AI in community health.  The manuscript reads as if the literature has already applied its findings to community health nursing.  I might consider rewriting the manuscript to describe your vision of the interface between AI and community health nursing. While a PRISm is not required for a narrative review, including the number of articles would help provide perspective on the gap.

The most insightful section was the application of AI to the domains of community health nursing practice, and here again, I would cite or include the specific related publications for each domain and a quote from the original research that sparked your synthesis.

The section on integration and community health nursing education and barriers to implementation was again focused entirely on AI and general nursing education. Your perspective and analysis could be given, but acknowledgment that the summary of current literature is not specific to community nursing education.

The section on ethical challenges and considerations could also be more clearly described as potential ethical challenges for consideration. Overall, I found the article interesting, but it felt like a stretch to connect it specifically to community health nursing

Author Response

Reviewer #3

We are grateful to Reviewer #3 for an especially careful and conceptually rich critique. The comments have been pivotal in helping us reframe the manuscript more honestly as a vision of the AI–community health nursing interface rather than as a settled empirical synthesis.

Comment 3.1 — Conflation of community and population health nursing; reframing as authors' vision

Reviewer comment:

Some nurses may object to your conflation of community health and population health nursing. Based on the referenced journal article titles, many of your findings focus on AI, but not on AI in community health. The manuscript reads as if the literature has already applied its findings to community health nursing. I might consider rewriting the manuscript to describe your vision of the interface between AI and community health nursing. While a PRISMA is not required for a narrative review, including the number of articles would help provide perspective on the gap.

Response — Option A (preferred, implemented):

This comment was instrumental in reshaping the manuscript. We have made four changes. (i) The Methods section now includes a quantitative selection narrative (612 records identified, 58 included) so that the reader can see the size and shape of the relevant literature and, importantly, the proportion that is genuinely community-specific (approximately one quarter). (ii) A new highlighted paragraph at the start of Section 3 explicitly states that the mapping in Table 1 and in the subsequent analysis represents, in part, the authors' vision of the AI–community health nursing interface, informed by adjacent evidence rather than by direct empirical demonstration. (iii) Terminology has been clarified: we use 'community health nursing' as the umbrella term for population-focused nursing delivered outside acute-care hospital walls, while acknowledging that 'public health nursing' and 'primary care nursing' carry overlapping but jurisdictionally variable meanings; we have not collapsed community and population health nursing into a single construct. (iv) The Conclusion and the new Limitations section both reframe the synthesis as a working vision intended to stimulate targeted empirical inquiry.

Response — Option B (alternative):

We have rewritten key transitional passages so that, where the underlying evidence is general rather than community-specific, the text now openly attributes the application to community settings as the authors' analytic projection rather than implying that the literature has already done so. We have also disambiguated 'community' and 'population' health nursing in the terminology paragraph.

Response — Option C (concise alternative):

Thank you. The manuscript now distinguishes 'community' from 'population' health nursing, quantifies the underlying literature, and is reframed throughout as the authors' vision of the AI–community health nursing interface.

 

Comment 3.2 — Citations and original-source anchoring for AI domains

Reviewer comment:

The most insightful section was the application of AI to the domains of community health nursing practice, and here again, I would cite or include the specific related publications for each domain and a quote from the original research that sparked your synthesis.

Response — Option A (preferred, implemented):

We thank the reviewer for highlighting Section 3 as the most insightful contribution of the paper, and we have strengthened it as suggested. A yellow-highlighted note immediately under the Table 1 caption now lists representative supporting references for each AI application domain (e.g., Predictive Analytics [25,27]; Disease Surveillance [26,46,47]; Health Education [36,38,49]). Within the body of Section 3, each domain paragraph cites at least one anchor study; where relevant, we include a brief paraphrased restatement of the central finding from the original work that informed our synthesis (in lieu of direct verbatim quotation, which we have used sparingly in keeping with MDPI style).

Response — Option B (alternative):

Thank you. We have added domain-specific citations both in a Table 1 footnote and in the narrative, and we now reference the seminal source that informed each domain (for example, Obermeyer et al. on algorithmic bias, Topol on AI augmentation, and Giri on AI-driven surveillance), so that readers can trace each synthesized claim back to its empirical or conceptual origin.

Response — Option C (concise alternative):

Domain-level citations have been added, and the seminal sources informing each domain are now identified within the narrative.

 

Comment 3.3 — Education and barriers sections: explicit acknowledgement of non-community focus

Reviewer comment:

The section on integration and community health nursing education and barriers to implementation was again focused entirely on AI and general nursing education. Your perspective and analysis could be given, but acknowledgment that the summary of current literature is not specific to community nursing education.

Response — Option A (preferred, implemented):

We agree fully. A yellow-highlighted clarifying paragraph has been added at the end of Section 4 (Education) explicitly stating that most of the summarized literature addresses AI in general nursing education rather than community health nursing education specifically, and that the implications drawn for community-focused curricula reflect the authors' analytic perspective rather than a body of evidence directly generated within community health nursing programs. A parallel acknowledgement now also frames Section 6 (Barriers to Implementation), where we note that infrastructural and regulatory barriers are documented across healthcare AI broadly and that their community-specific manifestation requires further empirical investigation.

Response — Option B (alternative):

Thank you. Both Sections 4 and 6 now carry an explicit caveat that they synthesize literature on AI in general nursing education and general healthcare implementation, respectively, with extensions to community health nursing presented as the authors' interpretive analysis.

Response — Option C (concise alternative):

Sections 4 and 6 now openly acknowledge that the underlying evidence is largely from general nursing/healthcare contexts; community-specific implications are presented as the authors' analytic extension.

 

Comment 3.4 — Ethical considerations framing

Reviewer comment:

The section on ethical challenges and considerations could also be more clearly described as potential ethical challenges for consideration. Overall, I found the article interesting, but it felt like a stretch to connect it specifically to community health nursing.

Response — Option A (preferred, implemented):

We have made two changes. First, the heading of Section 5 now reads 'Potential Ethical Challenges and Considerations' (highlighted in yellow), making explicit that these are anticipated rather than uniformly observed challenges in community health nursing settings. Second, throughout Section 5 we frame the issues (algorithmic bias, privacy, erosion of relational care) as risks that may arise when AI is deployed in community contexts, rather than as documented harms specific to community health nursing — the latter remains an under-investigated empirical area, as we now note. Combined with the new Limitations section and the reframed Conclusion, we hope these changes address the reviewer's broader concern that the manuscript previously read as overclaiming a community health nursing focus.

Response — Option B (alternative):

Thank you. Section 5 has been retitled 'Potential Ethical Challenges and Considerations' and the language within the section has been moderated to describe anticipated rather than established community-specific harms, in line with the reviewer's reframing.

Response — Option C (concise alternative):

Section 5 has been retitled and reframed to emphasize potential ethical challenges; the broader manuscript now presents the AI–community health nursing connection as a vision rather than an established synthesis.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I consider that most of the requested changes have been addressed appropriately. However, there are still a few points that could be strengthened before the manuscript is fully aligned with the original review recommendations.

In particular, regarding Table 1, my previous comment suggested adding an additional column with specific references associated with each domain. In the current version, this has been handled by adding a note beneath the table caption, which is helpful, but it does not fully correspond to the proposed in-table column format. If journal style permits, I would recommend considering the inclusion of that column to improve traceability of the evidence and facilitate readability.

Likewise, although the manuscript now includes a quantitative description of the study selection process in the Methods section, it is presented as a narrative paragraph rather than a formal PRISMA flow diagram, as originally suggested. I understand that the review is narrative in nature; however, a simple visual flow chart would further strengthen methodological transparency.

Otherwise, I appreciate the addition of a dedicated Limitations section, the conceptual clarification regarding the use of community health nursing-related terms, and the explicit acknowledgement that part of the evidence comes from general or hospital-based contexts and is extrapolated to community settings. These revisions respond well to the concerns previously raised and substantially improve the manuscript.

thanks

Author Response

11 May 2026

The Editor-in-Chief

Healthcare (MDPI)

Re: Manuscript ID healthcare-4285118 — "Integrating Artificial Intelligence into Community Health Nursing Education and Practice: Opportunities, Ethical Challenges, and Future Directions"

 

Dear Editor,

We thank you and the Reviewer for the additional thoughtful comments on our manuscript. We are very grateful for the Reviewer's positive overall assessment and for the recognition that the previous revision had addressed most of the original recommendations appropriately. In this second round of revision, we have carefully addressed the three remaining points raised by the Reviewer in order to align the manuscript fully with the original review recommendations.

All new revisions in this round are highlighted in yellow in the revised manuscript and have also been entered as tracked changes for ease of identification. A point-by-point response to each of the Reviewer's comments is provided below.

We hope that the revised version now meets the journal's standards for publication and we remain at your disposal for any further clarifications.

Yours sincerely,

Bandar Alhumaidi, RN, PhD

On behalf of all co-authors

Department of Community Health Nursing, College of Nursing, Taibah University, Medina, Saudi Arabia

Email: bhumide@taibahu.edu.sa

Point-by-point response to Reviewer's comments

We have reproduced the Reviewer's comments verbatim in italics below and have followed each comment with a detailed authors' response describing the corresponding revisions made to the manuscript.

Reviewer's overall assessment

"I consider that most of the requested changes have been addressed appropriately. However, there are still a few points that could be strengthened before the manuscript is fully aligned with the original review recommendations."

Authors' response: We sincerely thank the Reviewer for this constructive overall assessment and for confirming that the majority of the previously requested changes have been addressed appropriately. We have now also addressed each of the three remaining points, as detailed below.

Comment 1 — Inclusion of a dedicated references column in Table 1

"In particular, regarding Table 1, my previous comment suggested adding an additional column with specific references associated with each domain. In the current version, this has been handled by adding a note beneath the table caption, which is helpful, but it does not fully correspond to the proposed in-table column format. If journal style permits, I would recommend considering the inclusion of that column to improve traceability of the evidence and facilitate readability."

Authors' response: We fully agree with the Reviewer and thank them for re-emphasising this important point. We have now added a dedicated fourth column to Table 1 titled "References," which lists the specific supporting reference(s) associated with each AI application domain. The new column appears alongside the existing "Application Domain," "Description," and "Relevance to Community Health Nursing" columns, ensuring that the evidence base for each domain is now directly traceable within the table itself rather than only in the caption note. The new column entries are highlighted in yellow in the revised manuscript so that they can be readily identified during this round of review.

Specifically, the new column maps as follows: Predictive Analytics [25,27]; Clinical Decision Support [5,19]; Disease Surveillance [26,46,47]; Health Education [36,38,49]; Remote Monitoring [37,52]; Workflow Optimization [4,33]; and Mental Health Support [49,50].

The caption-level note that was added during the previous round has been retained in order to preserve continuity with the earlier revision; however, the in-table column is now the primary mechanism by which readers can trace each domain to its supporting evidence.

Comment 2 — Addition of a formal PRISMA flow diagram

"Likewise, although the manuscript now includes a quantitative description of the study selection process in the Methods section, it is presented as a narrative paragraph rather than a formal PRISMA flow diagram, as originally suggested. I understand that the review is narrative in nature; however, a simple visual flow chart would further strengthen methodological transparency."

Authors' response: We thank the Reviewer for this constructive suggestion, with which we fully agree. We have now added a formal PRISMA-style flow diagram as Figure 1 in the Methods section. The diagram visually summarises the four standard stages of the selection process — Identification, Screening, Eligibility, and Inclusion — and reports, for each stage, the corresponding numbers of records and the reasons for exclusion (137 duplicates removed; 312 records excluded at title/abstract screening; and 105 full-text articles excluded with itemised reasons), culminating in the final pool of 58 sources retained for thematic synthesis. The Reviewer's point that a visual flow chart substantially strengthens methodological transparency is well taken, and we believe the addition meaningfully improves the methodological presentation of the review.

In conjunction with the new figure, the concluding sentence of the Methods section has been revised to refer the reader to Figure 1 (previously the sentence stated that a narrative description was provided "in lieu of a formal PRISMA flow diagram"; this phrasing has been updated to indicate that the full study selection process is now also depicted visually as a PRISMA-style flow diagram in Figure 1). The revised sentence and the new figure together replace what was previously a narrative-only account. Both the new figure and the revised sentence are highlighted in yellow in the manuscript.

Comment 3 — Positive acknowledgement of previous revisions

"Otherwise, I appreciate the addition of a dedicated Limitations section, the conceptual clarification regarding the use of community health nursing-related terms, and the explicit acknowledgement that part of the evidence comes from general or hospital-based contexts and is extrapolated to community settings. These revisions respond well to the concerns previously raised and substantially improve the manuscript."

Authors' response: We are deeply grateful to the Reviewer for these positive observations and for the careful engagement with the previous revision. We have retained, unchanged, the dedicated Limitations section, the conceptual clarification of the use of community health nursing-related terms, and the explicit acknowledgement that a substantial portion of the cited evidence originates from hospital-based or general nursing contexts and is therefore extrapolated rather than directly demonstrated for the community setting. No further changes have been made to these elements in the current round of revision, as the Reviewer indicated that they already respond well to the concerns previously raised.

Closing remarks

We thank the Reviewer once again for the rigorous and constructive engagement with our manuscript across both rounds of review. The addition of the in-table References column to Table 1 and the inclusion of a formal PRISMA-style flow diagram as Figure 1 directly address the two remaining methodological-transparency concerns. We believe that the revised manuscript is now fully aligned with the original review recommendations and that the additions meaningfully strengthen the traceability of the evidence and the transparency of the study selection process.

We remain at the disposal of the Editor and the Reviewer for any further clarifications or revisions.

Sincerely,

Bandar Alhumaidi, RN, PhD (Corresponding author)

Talal Ali F. Alharbi, RN, PhD