Review Reports - SARS-CoV-2 Point-of-Care Testing Modalities: Integrating Molecular, Immunological, Biosensor, and AI Approaches

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

Diagnostic detection of SARS-CoV-2 remains an important field, particularly in the context of home testing, decentralized diagnostics, regulatory oversight, and preparedness for future pandemics. The manuscript covers a broad range of approaches, including molecular platforms, antigen- and antibody-based point-of-care tests (POCT), CRISPR-based systems, biosensors, AI-assisted diagnostics, regulatory aspects, and practical implementation. This structure may be useful for readers seeking a general overview of the field.

Major concerns

Non-reproducible review methodology. The authors provide only the databases searched and broad keywords, without specifying the search time frame, full search syntax, number of records retrieved, duplicate removal procedure, number of excluded publications, or reasons for exclusion.

The manuscript largely compiles already known information on POCT, but it does not provide a new conceptual framework, evidence map, decision algorithm, grading of evidence, or robust comparative matrix.

Limited analytical added value. The manuscript lists technologies and selected sensitivity and specificity metrics, but it does not critically assess the quality of the underlying evidence. For example, the platform comparisons in the tables are accompanied by a disclaimer stating that performance metrics depend on study design, population, and reference standard; however, the main text still uses broad generalizations such as “high concordance,” “high sensitivity,” and “faster turnaround” without clearly defining the specific conditions under which these statements are valid. The tables compare clinically implemented tests with research prototypes, across different specimen types, different LOD units, and different levels of validation. This distorts the reader’s perception of technological maturity.

The tables do not report confidence intervals, sample size, reference standard, specimen type, symptomatic/asymptomatic status, days from symptom onset, Ct value or viral-load strata, variant period, clinical setting, operator type, or prospective versus retrospective study design. Without these parameters, sensitivity and specificity values are of limited interpretability.

Another important problem is the mixing of clinically deployed POCT products with research prototypes. In the biosensor section, many devices are classified as “experimental” or “research,” yet they are included in the same comparative framework as clinically used molecular and antigen tests. This may mislead readers: an analytical LOD obtained using synthetic samples or buffer is not equivalent to clinical diagnostic accuracy. The authors describe a technological progression from plasmonic sensing to electrochemical and FET-based platforms. However, the table includes synthetic samples, isolated RNA, human serum, saliva, sputum, exhaled breath, and clinical samples without a unified comparison standard. It is not methodologically appropriate to compare femtogram/mL, copies/mL, pM, and ng/mL as though they represented a single scale of clinical diagnostic effectiveness.

The AI section appears methodologically weak and is only partially aligned with the topic of SARS-CoV-2 POCT. The authors discuss voice-based models, CT, and X-ray approaches, but do not clearly distinguish between diagnosis of infection, diagnosis of pneumonia or lung involvement, triage, and point-of-care testing. A voice-based AI model with “89% accuracy” is presented as comparable to lateral flow tests, but accuracy depends strongly on disease prevalence and sample composition. Without external validation, confidence intervals, calibration, bias assessment, and prospective validation, such a conclusion is insufficient.

The final conclusion contains a colloquial and speculative statement regarding future tests that may provide results “in just seconds,” without sufficient evidentiary support.

Minor concerns

The numbering of tables is inconsistent: “Table 4” is used for the biosensor section and then again for the comparative overview of POCT modalities. The tables also contain empty fields, dashes, non-standardized units, and incomplete availability status information.

The graphical abstract and figures appear too generic and do not convey the central scientific message of the review.

The authors do not explain the criteria used to select commercial tests for inclusion in the tables. Why were these particular products included rather than other FDA- or CE-authorized tests?

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Reviewer 1

Comment: Diagnostic detection of SARS-CoV-2 remains an important field, particularly in the context of home testing, decentralized diagnostics, regulatory oversight, and preparedness for future pandemics. The manuscript covers a broad range of approaches, including molecular platforms, antigen- and antibody-based point-of-care tests (POCT), CRISPR-based systems, biosensors, AI-assisted diagnostics, regulatory aspects, and practical implementation. This structure may be useful for readers seeking a general overview of the field.

Response:

We sincerely thank the reviewer for their careful evaluation and constructive comments on our manuscript.

Major concerns

Comment: Non-reproducible review methodology. The authors provide only the databases searched and broad keywords, without specifying the search time frame, full search syntax, number of records retrieved, duplicate removal procedure, number of excluded publications, or reasons for exclusion.

Response: We thank the reviewer for this important observation. The methodology section has been substantially expanded to include the full description of the screening process.

Comment: The manuscript largely compiles already known information on POCT, but it does not provide a new conceptual framework, evidence map, decision algorithm, grading of evidence, or robust comparative matrix.

Response: We appreciate this constructive comment and have revised the manuscript accordingly by strengthening the comparative synthesis and overall analytical framework.

Comment: Limited analytical added value. The manuscript lists technologies and selected sensitivity and specificity metrics, but it does not critically assess the quality of the underlying evidence. For example, the platform comparisons in the tables are accompanied by a disclaimer stating that performance metrics depend on study design, population, and reference standard; however, the main text still uses broad generalizations such as “high concordance,” “high sensitivity,” and “faster turnaround” without clearly defining the specific conditions under which these statements are valid. The tables compare clinically implemented tests with research prototypes, across different specimen types, different LOD units, and different levels of validation. This distorts the reader’s perception of technological maturity.

Response: We thank the reviewer for this valuable comment and have revised the manuscript accordingly.

Comment: The tables do not report confidence intervals, sample size, reference standard, specimen type, symptomatic/asymptomatic status, days from symptom onset, Ct value or viral-load strata, variant period, clinical setting, operator type, or prospective versus retrospective study design. Without these parameters, sensitivity and specificity values are of limited interpretability.

Response: We thank the reviewer for this valuable comment and have acknowledged this limitation in the revised manuscript.

Comment: Another important problem is the mixing of clinically deployed POCT products with research prototypes. In the biosensor section, many devices are classified as “experimental” or “research,” yet they are included in the same comparative framework as clinically used molecular and antigen tests. This may mislead readers: an analytical LOD obtained using synthetic samples or buffer is not equivalent to clinical diagnostic accuracy.

Response: We thank the reviewer for this valuable comment and have addressed this point in the revised manuscript.

Comment: The authors describe a technological progression from plasmonic sensing to electrochemical and FET-based platforms. However, the table includes synthetic samples, isolated RNA, human serum, saliva, sputum, exhaled breath, and clinical samples without a unified comparison standard. It is not methodologically appropriate to compare femtogram/mL, copies/mL, pM, and ng/mL as though they represented a single scale of clinical diagnostic effectiveness.

Response: We thank the reviewer for this important comment and have revised the table to better stratify sample types and clarify that LOD values across different units and matrices are not directly comparable.

Comment: The AI section appears methodologically weak and is only partially aligned with the topic of SARS-CoV-2 POCT. The authors discuss voice-based models, CT, and X-ray approaches, but do not clearly distinguish between diagnosis of infection, diagnosis of pneumonia or lung involvement, triage, and point-of-care testing. A voice-based AI model with “89% accuracy” is presented as comparable to lateral flow tests, but accuracy depends strongly on disease prevalence and sample composition. Without external validation, confidence intervals, calibration, bias assessment, and prospective validation, such a conclusion is insufficient.

Response: We thank the reviewer for this important critique. The AI section has been substantially revised. We now clearly distinguish between AI tools for infection diagnosis, pneumonia detection, triage, and true point-of-care applications. The voice-based AI model discussion has been reframed to acknowledge its preliminary nature, the absence of external validation, and the strong dependence of accuracy on disease prevalence and sample composition. No direct comparison to lateral flow tests is made in the revised text.

Comment: The final conclusion contains a colloquial and speculative statement regarding future tests that may provide results “in just seconds,” without sufficient evidentiary support.

Response: We agree this language was inappropriate for a scientific review. The speculative statement has been removed and replaced with a measured, evidence-grounded discussion of near-term technological directions supported by the reviewed literature.

Minor concerns

Comment: The numbering of tables is inconsistent: “Table 4” is used for the biosensor section and then again for the comparative overview of POCT modalities. The tables also contain empty fields, dashes, non-standardized units, and incomplete availability status information.

Response: We apologize for this oversight. Table numbering has been corrected throughout the manuscript to ensure sequential consistency. Empty fields, dashes, and non-standardized units in the tables have also been addressed.

Comment: The graphical abstract and figures appear too generic and do not convey the central scientific message of the review.

Response: We appreciate this feedback. The graphical abstract has been revised to more clearly reflect the four-tier comparative framework of the review.

Comment: The authors do not explain the criteria used to select commercial tests for inclusion in the tables. Why were these particular products included rather than other FDA- or CE-authorized tests?

Response: We thank the reviewer for this important comment and have added a clarification in the methodology section regarding the criteria used for selecting commercial tests included in the tables.

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

Figure 1. The diagram is pertinent and adequate. However, I am not sure that AI-CT, AI-Chest X-ray, or AI-POCUS fits into the general classification of POCT. Please reconsider the instruments that this figure includes.

In the conclusions, you mention that rapid immunoassays detect N, S, and M proteins. I would suggest not including the M protein since, to our knowledge, it is not included in the FDA-approved tests.

In section 3, methodology, you mention as criteria for searching scientific papers: clinical validation studies. Please be aware that, at least, in the section 3.4 Biosensors, most of the data come from experimental and lab prototype studies. I consider you should increase the number of really clinical studies

In table 4 the units that you include are absolutely heterogeneous, without mentioning that in the 3D electrochemical sensor the exponent is absent ( femtomolar)

I would definitely suggest to work on the conclusions section. In the figure 1 you show RT-PCR as part of the rapid POC diagnostic techniques and in the conclusions (L538) you consider it as long-time procedure technique. In general, the conclusions were limited to summarize the temporality of the assays but lacking of critical depth.

Author Response

Reviewer 2:

Comment: Figure 1. The diagram is pertinent and adequate. However, I am not sure that AI-CT, AI-Chest X-ray, or AI-POCUS fits into the general classification of POCT. Please reconsider the instruments that this figure includes.

Response: We thank the reviewer for this valid observation. Figure 1 has been revised to remove AI-CT and AI-Chest X-ray from the POCT classification, as these rely on centralized imaging infrastructure. AI-POCUS has been retained and repositioned with a clarifying note acknowledging that handheld ultrasound represents the closest approximation to a true point-of-care modality within AI-assisted imaging. The accompanying text has been revised accordingly.

Comment: In the conclusions, you mention that rapid immunoassays detect N, S, and M proteins. I would suggest not including the M protein since, to our knowledge, it is not included in the FDA-approved tests.

Response: We thank the reviewer for this correction. The reference to M protein detection has been removed from the conclusions. The text now accurately states that FDA-approved rapid antigen tests predominantly target the nucleocapsid (N) protein, with some platforms also detecting the spike (S) protein.

Comment: In section 3, methodology, you mention as criteria for searching scientific papers: clinical validation studies. Please be aware that, at least, in the section 3.4 Biosensors, most of the data come from experimental and lab prototype studies. I consider you should increase the number of really clinical studies

Response: We thank the reviewer for this important comment; the methodology has been clarified to state that while clinical validation studies were prioritized, biosensor-based and AI-assisted technologies also included relevant proof-of-concept and laboratory prototype studies due to the limited availability of clinically validated evidence in these emerging fields.

Comment: In table 4 the units that you include are absolutely heterogeneous, without mentioning that in the 3D electrochemical sensor the exponent is absent (2.8×10 femtomolar)

Response: We apologize for this typographical error. The value has been corrected to 2.8 × 10⁻¹⁵ mol/L (2.8 fM) and the LOD units throughout Table 4 have been standardized where possible, with footnotes clarifying where direct cross-unit comparison is not appropriate.

Comment: I would definitely suggest to work on the conclusions section. In the figure 1 you show RT-PCR as part of the rapid POC diagnostic techniques and in the conclusions (L538) you consider it as long-time procedure technique. In general, the conclusions were limited to summarize the temporality of the assays but lacking of critical depth.

Response: We thank the reviewer for this important comment and have revised both the figure and conclusions to improve consistency and provide a more critical, context-aware discussion of diagnostic modalities.

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

This manuscript provides a comprehensive review of SARS-CoV-2 point-of-care testing (POCT) modalities, covering molecular, immunological, biosensor-based, and AI-assisted approaches. While the review is well-structured and timely, several areas require further refinement to enhance its scientific impact and clarity.

>Executive Summary & Core Strengths
The review effectively categorizes the rapidly evolving landscape of POCT for SARS-CoV-2. The integration of emerging technologies like CRISPR-Cas, advanced biosensors, and AI distinguishes this work from earlier, more traditional reviews. The comparative tables (e.g., Table 1, 2, and 4) provide valuable summaries of commercial and experimental platforms.

>Introduction and Background
On line 68, the text uses "SARSCOV-2." Please ensure consistency by using "SARS-CoV-2" with a hyphen throughout the manuscript.
While the introduction mentions the rise of subtypes, it could be strengthened by explicitly discussing the "post-pandemic" context of 2025-2026. How has the shift from acute crisis management to long-term surveillance changed the requirements for POCT?

>Methods for Literature Review
Process Detail: The authors mention using PubMed, Scopus, and Web of Science. To improve transparency and reproducibility, consider adding a PRISMA flow diagram showing the number of papers identified, screened, and eventually included. This is standard practice for high-quality review articles in medical journals.

>Molecular POCT (Section 3)
Variant Impact: The manuscript correctly identifies that mutations can affect assay performance (lines 211–214). However, the molecular section should more specifically discuss how "primer/probe design" has evolved to target more conserved regions of the N or M genes to avoid "S-gene target failure" (SGTF) seen in earlier variants.
Isothermal vs. RT-PCR: In Table 1, clarify the trade-off between the high sensitivity of RT-PCR (e.g., VitaPCR) and the speed of isothermal methods like NEAR (e.g., IDNOW). A "Clinical Utility" column in the table could help readers decide which is best for specific settings (e.g., emergency room vs. home screening).

>Antigen-Based POCT (Section 3)
Mechanism Clarity: Figure 2 provides a good schematic of LFIA. However, the text should elaborate on "digital lateral flow assays"—those that use a reader to provide a semi-quantitative result—as these are bridge technologies between basic LFIAs and advanced biosensors.

>Biosensor and AI-Assisted Diagnostics (Section 3)
AI Integration: The review covers AI-assisted imaging (CT, X-ray) well. To make this more "Point-of-Care," the authors should emphasize AI-POCUS (Point-of-Care Ultrasound) more deeply, as handheld ultrasound is a true POCT modality compared to centralized CT scanners.
Deployment Barriers: For the biosensor section (Section 3.4), include a brief discussion on "stability and shelf-life". Many research-grade biosensors (Table 4) fail in the real world because the biological components (enzymes/antibodies) degrade without strict cold-chain requirements.

>Comparative Synthesis (Table 5)
The qualitative comparison (High/Moderate/Low) in Table 5 is useful. To make this more robust, add a row for "Ease of Interpretation." For example, molecular POCT often requires specialized training to interpret results, whereas antigen POCT is designed for the "naked eye".

>Strategic Recommendations to Enhance the Manuscript
- Future Outlook Subsection:
Add a dedicated subsection on "Multiplexing." The next generation of POCT is moving toward "Respiratory Panels" that detect SARS-CoV-2, Influenza A/B, and RSV simultaneously. Discussing this would make the review much more forward-looking.
- Connectivity & Data Privacy:
In the AI section, briefly mention the role of "Internet of Medical Things" (IoMT). How do these POCT devices securely transmit results to public health databases? This is a critical component of modern POCT that is currently missing.
- Reference Update:
Ensure references from 2024 and 2025 are prioritized to reflect the most current state of the art, especially regarding the performance of tests against the most recent Omicron sub-lineages.
Additional references:
>>> https://doi.org/10.33086/ijmlst.v6i1.5405
>>> https://doi.org/10.33086/ijmlst.v4i2.3027
>>> https://doi.org/10.33086/ijmlst.v4i1.2281
- Formatting Check:
Ensure all abbreviations used in the tables (e.g., NPS, OS, NS) are consistently defined in the "Abbreviations" list on pages 18-19.

Comments on the Quality of English Language

Author Response

Reviewer 3:

Comment: This manuscript provides a comprehensive review of SARS-CoV-2 point-of-care testing (POCT) modalities, covering molecular, immunological, biosensor-based, and AI-assisted approaches. While the review is well-structured and timely, several areas require further refinement to enhance its scientific impact and clarity.

Response: We thank the reviewer for the constructive overall assessment and for the helpful suggestions aimed at improving the clarity and scientific quality of the manuscript.

>Executive Summary & Core Strengths
Comment: The review effectively categorizes the rapidly evolving landscape of POCT for SARS-CoV-2. The integration of emerging technologies like CRISPR-Cas, advanced biosensors, and AI distinguishes this work from earlier, more traditional reviews. The comparative tables (e.g., Table 1, 2, and 4) provide valuable summaries of commercial and experimental platforms.

Response: We thank the reviewer for this positive summary and for recognizing the strengths and contributions of our work.

>Introduction and Background
Comment: On line 68, the text uses "SARSCOV-2." Please ensure consistency by using "SARS-CoV-2" with a hyphen throughout the manuscript.

Response: We thank the reviewer for catching this error. The typo on line 68 has been corrected and the entire manuscript has been checked to ensure consistent use of "SARS-CoV-2" throughout.

Comment: While the introduction mentions the rise of subtypes, it could be strengthened by explicitly discussing the "post-pandemic" context of 2025-2026. How has the shift from acute crisis management to long-term surveillance changed the requirements for POCT?

Response: We thank the reviewer for this valuable comment and have revised the introduction to include discussion of the post-pandemic transition and its implications for POCT requirements.

>Methods for Literature Review (methodology)
Comment: Process Detail: The authors mention using PubMed, Scopus, and Web of Science. To improve transparency and reproducibility, consider adding a PRISMA flow diagram showing the number of papers identified, screened, and eventually included. This is standard practice for high-quality review articles in medical journals.

Response: We thank the reviewer for this valuable suggestion. The methodology section has been updated in the revised manuscript.

>Molecular POCT (Section 3)
Comment: Variant Impact: The manuscript correctly identifies that mutations can affect assay performance (lines 211–214). However, the molecular section should more specifically discuss how "primer/probe design" has evolved to target more conserved regions of the N or M genes to avoid "S-gene target failure" (SGTF) seen in earlier variants.

Response: We thank the reviewer for this specific and valuable suggestion. A discussion of primer and probe design evolution, including the shift toward conserved N and ORF1ab gene targets in response to SGTF associated with Omicron variants, has been added to the molecular POCT section.

Comment: Isothermal vs. RT-PCR: In Table 1, clarify the trade-off between the high sensitivity of RT-PCR (e.g., VitaPCR) and the speed of isothermal methods like NEAR (e.g., IDNOW). A "Clinical Utility" column in the table could help readers decide which is best for specific settings (e.g., emergency room vs. home screening).

Response: We thank the reviewer for this valuable comment and have addressed it in the revised manuscript.

>Antigen-Based POCT (Section 3)
Comment: Mechanism Clarity: Figure 2 provides a good schematic of LFIA. However, the text should elaborate on "digital lateral flow assays"—those that use a reader to provide a semi-quantitative result—as these are bridge technologies between basic LFIAs and advanced biosensors.

Response: We thank the reviewer for this valuable comment and have expanded the manuscript to include a description of digital and reader-assisted lateral flow assays within the antigen-based POCT section.

>Biosensor and AI-Assisted Diagnostics (Section 3)
Comment: AI Integration: The review covers AI-assisted imaging (CT, X-ray) well. To make this more "Point-of-Care," the authors should emphasize AI-POCUS (Point-of-Care Ultrasound) more deeply, as handheld ultrasound is a true POCT modality compared to centralized CT scanners.

Response: We thank the reviewer for this valuable comment and have addressed this point in the revised manuscript.

Deployment Barriers: For the biosensor section (Section 3.4), include a brief discussion on "stability and shelf-life". Many research-grade biosensors (Table 4) fail in the real world because the biological components (enzymes/antibodies) degrade without strict cold-chain requirements.

Response: We thank the reviewer for this valuable comment and have addressed it in the revised manuscript.

>Comparative Synthesis (Table 5)
Comment: The qualitative comparison (High/Moderate/Low) in Table 5 is useful. To make this more robust, add a row for "Ease of Interpretation." For example, molecular POCT often requires specialized training to interpret results, whereas antigen POCT is designed for the "naked eye".

Response: We thank the reviewer for this valuable suggestion and have added an “Ease of Interpretation” row to Table 5 to further strengthen the comparative framework.

Response: We thank the reviewer for this valuable suggestion and have addressed it in the revised manuscript.

- Connectivity & Data Privacy:
In the AI section, briefly mention the role of "Internet of Medical Things" (IoMT). How do these POCT devices securely transmit results to public health databases? This is a critical component of modern POCT that is currently missing.
Response: A brief discussion of IoMT integration, including secure result transmission to public health databases and data privacy considerations, has been added to the AI-assisted diagnostics section.

- Reference Update:
Ensure references from 2024 and 2025 are prioritized to reflect the most current state of the art, especially regarding the performance of tests against the most recent Omicron sub-lineages.
Additional references:
>>> https://doi.org/10.33086/ijmlst.v6i1.5405
>>> https://doi.org/10.33086/ijmlst.v4i2.3027
>>> https://doi.org/10.33086/ijmlst.v4i1.2281

Response: We thank the reviewer for these helpful suggestions and have updated the reference list to include recent studies where appropriate.

- Formatting Check:
Comment: Ensure all abbreviations used in the tables (e.g., NPS, OS, NS) are consistently defined in the "Abbreviations" list on pages 18-19.

Response: We thank the reviewer for this observation. The Abbreviations list has been carefully reviewed and updated to ensure that all abbreviations used in Tables 1–5, including NPS, OS, and NS, are consistently defined and aligned with their usage throughout the manuscript.

Round 2

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

1. Comment: Figure 1. The diagram is pertinent and adequate. However, I am not sure that AI-CT, AI-Chest X-ray, or AI-POCUS fits into the general classification of POCT. Please reconsider the instruments that this figure includes. THE DIAGRAM WAS SUCCESSFULLY MODIFIED, MAKING IT MORE COHERENT WITH THE CONTENT OF THE PAPER

2. Comment: In the conclusions, you mention that rapid immunoassays detect N, S, and M proteins. I would suggest not including the M protein since, to our knowledge, it is not included in the FDA-approved tests. THE CORRECTION WAS PROPERLY APPLIED TO THE TEXT.

3. Comment: In section 3, methodology, you mention as criteria for searching scientific papers: clinical validation studies. Please be aware that, at least, in section 3.4 Biosensors, most of the data come from experimental and lab prototype studies. I consider that you should increase the number of really clinical studies. THE CORRECTION WAS PROPERLY APPLIED TO THE "METHODS" SECTION

4. Comment: In table 4, the units that you include are absolutely heterogeneous, without mentioning that in the 3D electrochemical sensor, the exponent is absent (2.8×10 femtomolar). THE TYPOGRAPHICAL ERRORS WERE ACCURATELY CORRECTED, AND OTHER UNITS WERE (MOSTLY) HOMOGENIZED.

5. Comment: I would definitely suggest working on the conclusions section. In Figure 1 you show RT-PCR as part of the rapid POC diagnostic techniques, and in the conclusions (L538) you consider it as a long-term procedure technique. In general, the conclusions were limited to summarizing the temporality of the assays but lacked critical depth. THE CONCLUSIONS WERE IMPROVED. THEREFORE A "FUTURE PERSPECTIVES" SECTION WAS ADDED WHICH GIVES A MUCH MORE COMPREHENSIVE CLOSE TO THE INFORMATION PRESENTED IN THIS ARTICLE.

SUMMARY: THE PAPER HAS BEEN SUBSTANTIALLY AND SUCCESSFULLY MODIFIED, THUS, IN MY OPINION, IT COULD BE READY TO BE ACCEPTED.

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The submitted manuscript contains critical methodological and technical flaws that preclude its publication, even following extensive revision. Most notably, there is a complete discrepancy between the reference list and the manuscript’s subject matter. While the text focuses on SARS-CoV-2 diagnostics, the bibliography is overwhelmingly composed of publications dedicated to human metapneumovirus. This strongly suggests that an entirely incorrect reference list was erroneously attached to the manuscript.

The authors position the work as a "comprehensive review"; however, it lacks a dedicated Methods section detailing the literature search strategy, inclusion/exclusion criteria, quality or risk-of-bias assessment, and data extraction protocol. This omission fails to meet the minimum standards for both systematic and narrative reviews in reputable journals.

Furthermore, the term "updated" in the title is inappropriately used and ill-defined: updated relative to which prior version, and what specific timeframe does the review cover?

The tables summarizing commercial test characteristics present performance metrics (sensitivity, specificity, limit of detection) devoid of essential contextual information, such as validation study design, sample size, reference standard, patient cohort characteristics, and timing relative to symptom onset.

Additionally, the manuscript fails to discuss critical aspects, including the impact of viral evolution on diagnostic performance, the comparative cost-effectiveness of different platforms, implementation barriers in resource-limited healthcare settings, and the inherent limitations of the review itself.

Given these deficiencies, the manuscript requires a complete overhaul from the ground up, encompassing the systematic selection of relevant literature, the development of a rigorous review methodology, and a critical appraisal of the presented data.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents a review of rapid point-of-care testing approaches for SARS-CoV-2, covering molecular, antigen, antibody, biosensor-based, and AI-assisted diagnostic strategies, with the stated aim of comparing platforms and highlighting advances in sensitivity, accessibility, and applicability. While the topic is timely, the current version reads as an overly extensive catalogue of technologies and not a critical and synthesised review of the field. Substantial revisions are required.

Sections 2.1.1 and 2.3 contain inappropriate non-standard subsections presented as bullet points. No bullet point sections should be present, and the overall hierarchy should be limited to a maximum of 3 levels.
The number of figures and tables is excessive and should be reduced substantially, ideally to no more than 5 total elements, as the current volume detracts from clarity and does not support synthesis.
The review predominantly describes technologies without appropriately and critically comparing them. As an example, the comparison between molecular, antigen-based, antibody-based, and biosensor-based methods remains superficial and would benefit from a more integrative and comparative framework.
There is insufficient discussion of clinical performance metrics such as sensitivity, specificity, and predictive values across different POCT platforms, particularly in real-world settings.
The manuscript does not adequately address how emerging SARS-CoV-2 variants impact the performance and reliability of existing diagnostic approaches.
The manuscript would benefit from a clearer distinction between laboratory-based assays and true point-of-care or at-home tests.
There is limited discussion of regulatory approval, quality control, and standardisation across different commercial SARS-CoV-2 POCT platforms.
The authors do not sufficiently address challenges related to implementation, including cost, accessibility, infrastructure, and user training in different healthcare settings.
The inclusion of numerous specific commercial products disrupts the narrative flow and should be streamlined or moved to summarised tables with clearer rationale for inclusion.
The authors should clearly state whether AI tools were used in the preparation of the manuscript.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf