Title: A Foundation Systematic Review of Natural Language Processing Applied to
Gastroenterology & Hepatology
Version: 2 Date: 18 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The introduction briefly mentions applications such as adenoma detection from colonoscopy
reports, there is limited detail on the specific clinical tasks where NLP could be applied
in gastroenterology (e.g., diagnostic decision support, prediction of disease progression,
personalized treatment planning). Providing a broader overview of potential use cases within
gastroenterology would make the introduction more comprehensive.
-While the introduction addresses general challenges in NLP (e.g., morphology and syntax),
it would benefit from a discussion of unique challenges in gastroenterology. For instance,
gastroenterology-specific language (e.g., medical terminology, abbreviations, jargon) or the
need to process complex procedural data (e.g., colonoscopy and endoscopy reports) could be
explored in more detail.
-While the review adheres to numerous guidelines and checklists (e.g., PRISMA, AMSTAR,
TRIPOD, SWiM, ROBINS-I), the sheer number of frameworks mentioned may be
overwhelming for some readers. A more concise explanation or a focused discussion on the
most relevant guidelines for this specific review could improve clarity. For example, a summary
table or flowchart of the reporting and quality appraisal process might help readers navigate this
complexity.
-While using well-established checklists is a strength, it may also lead to a somewhat formulaic
approach. Given the rapidly evolving nature of NLP in healthcare, there could be value in
addressing emerging or specific concerns (e.g., model interpretability in clinical settings) that
are not fully captured by traditional checklists. Some form of flexibility in evaluating NLP
studies beyond these standardized frameworks might be beneficial for capturing the unique
characteristics of the field.
- The review mentions the heterogeneity of methodologies, it could benefit from a more detailed
examination of why certain methods are favored over others. For instance, why are rule-based
methods still widely used, and how do they compare to machine learning (ML) approaches
in terms of clinical efficacy? A deeper dive into the strengths and limitations of different
approaches would provide readers with a clearer understanding of their practical implications.
-While the review touches on data protection and patient privacy when discussing model sharing,
it could delve deeper into the ethical considerations surrounding NLP in gastroenterology. For
example, how might biases in data affect model predictions, particularly regarding race, sex,
and socioeconomic status? The article would benefit from a more robust ethical analysis of the
implications of NLP in clinical settings.
Title: A Foundation Systematic Review of Natural Language Processing Applied to
Gastroenterology & Hepatology
Version: 2 Date: 18 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The introduction briefly mentions applications such as adenoma detection from colonoscopy
reports, there is limited detail on the specific clinical tasks where NLP could be applied
in gastroenterology (e.g., diagnostic decision support, prediction of disease progression,
personalized treatment planning). Providing a broader overview of potential use cases within
gastroenterology would make the introduction more comprehensive.
-While the introduction addresses general challenges in NLP (e.g., morphology and syntax),
it would benefit from a discussion of unique challenges in gastroenterology. For instance,
gastroenterology-specific language (e.g., medical terminology, abbreviations, jargon) or the
need to process complex procedural data (e.g., colonoscopy and endoscopy reports) could be
explored in more detail.
-While the review adheres to numerous guidelines and checklists (e.g., PRISMA, AMSTAR,
TRIPOD, SWiM, ROBINS-I), the sheer number of frameworks mentioned may be
overwhelming for some readers. A more concise explanation or a focused discussion on the
most relevant guidelines for this specific review could improve clarity. For example, a summary
table or flowchart of the reporting and quality appraisal process might help readers navigate this
complexity.
-While using well-established checklists is a strength, it may also lead to a somewhat formulaic
approach. Given the rapidly evolving nature of NLP in healthcare, there could be value in
addressing emerging or specific concerns (e.g., model interpretability in clinical settings) that
are not fully captured by traditional checklists. Some form of flexibility in evaluating NLP
studies beyond these standardized frameworks might be beneficial for capturing the unique
characteristics of the field.
- The review mentions the heterogeneity of methodologies, it could benefit from a more detailed
examination of why certain methods are favored over others. For instance, why are rule-based
methods still widely used, and how do they compare to machine learning (ML) approaches
in terms of clinical efficacy? A deeper dive into the strengths and limitations of different
approaches would provide readers with a clearer understanding of their practical implications.
-While the review touches on data protection and patient privacy when discussing model sharing,
it could delve deeper into the ethical considerations surrounding NLP in gastroenterology. For
example, how might biases in data affect model predictions, particularly regarding race, sex,
and socioeconomic status? The article would benefit from a more robust ethical analysis of the
implications of NLP in clinical settings.
Please review this paper.
user
Title: A Foundation Systematic Review of Natural Language Processing Applied to
Gastroenterology & Hepatology
Version: 2 Date: 18 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The introduction briefly mentions applications such as adenoma detection from colonoscopy
reports, there is limited detail on the specific clinical tasks where NLP could be applied
in gastroenterology (e.g., diagnostic decision support, prediction of disease progression,
personalized treatment planning). Providing a broader overview of potential use cases within
gastroenterology would make the introduction more comprehensive.
-While the introduction addresses general challenges in NLP (e.g., morphology and syntax),
it would benefit from a discussion of unique challenges in gastroenterology. For instance,
gastroenterology-specific language (e.g., medical terminology, abbreviations, jargon) or the
need to process complex procedural data (e.g., colonoscopy and endoscopy reports) could be
explored in more detail.
-While the review adheres to numerous guidelines and checklists (e.g., PRISMA, AMSTAR,
TRIPOD, SWiM, ROBINS-I), the sheer number of frameworks mentioned may be
overwhelming for some readers. A more concise explanation or a focused discussion on the
most relevant guidelines for this specific review could improve clarity. For example, a summary
table or flowchart of the reporting and quality appraisal process might help readers navigate this
complexity.
-While using well-established checklists is a strength, it may also lead to a somewhat formulaic
approach. Given the rapidly evolving nature of NLP in healthcare, there could be value in
addressing emerging or specific concerns (e.g., model interpretability in clinical settings) that
are not fully captured by traditional checklists. Some form of flexibility in evaluating NLP
studies beyond these standardized frameworks might be beneficial for capturing the unique
characteristics of the field.
- The review mentions the heterogeneity of methodologies, it could benefit from a more detailed
examination of why certain methods are favored over others. For instance, why are rule-based
methods still widely used, and how do they compare to machine learning (ML) approaches
in terms of clinical efficacy? A deeper dive into the strengths and limitations of different
approaches would provide readers with a clearer understanding of their practical implications.
-While the review touches on data protection and patient privacy when discussing model sharing,
it could delve deeper into the ethical considerations surrounding NLP in gastroenterology. For
example, how might biases in data affect model predictions, particularly regarding race, sex,
and socioeconomic status? The article would benefit from a more robust ethical analysis of the
implications of NLP in clinical settings.
assistant