Exploring the Potential of Chatbots in Critical Care Nephrology

Round 1

Reviewer 1 Report

The manuscript named " Harnessing the Power of Chatbots for Critical Care Nephrology" by authors  Supawadee Suppadungsuk et al is about chatbots as valuable tools for patients and healthcare professionals.

In the abstract, state precisely the advantages and disadvantages of chatbots and based on which you came to the conclusions.

In the introduction with research that has been done so far in intensive care and the significance of the results obtained.

In Understanding Chatbot Technology and Functionality, it should be stated how the chatbot came to be used and used in intensive care medicine, and then in nephrology. The first chapter of the paper does not correspond to the title of the paper, correct it with an application in nephrology or delete it

Move the Importance of Chatbots in Critical Care Nephrology section before the Importance of Chatbots in Critical Care Nephrology section

You mention the characteristics of chatbots in intensive care nephrology, and there is no current or previous experience in the use of this technology

Based on the conclusion of the manuscript the author's review paper is a futuristic consideration of some facts and more assumed possibilities about the use of advanced technology in the intensive care of nephrology

The work under the given title begins with the chapter Applications of Chatbots in Critical Care Nephrology. Who did the research from the mentioned segments and what were the results? 

Nice charts and graphs are missing.

I think that a lot of effort was put into the paper and that the authors should either change the title with the attached content or make two papers from the original content.

Thank you for the review invitation.

Author Response

Reviewer 1

Reviewer comment:

The manuscript named " Harnessing the Power of Chatbots for Critical Care Nephrology" by authors Supawadee Suppadungsuk et al is about chatbots as valuable tools for patients and healthcare professionals.

In the abstract, state precisely the advantages and disadvantages of chatbots and based on which you came to the conclusions.

In the introduction with research that has been done so far in intensive care and the significance of the results obtained.

In Understanding Chatbot Technology and Functionality, it should be stated how the chatbot came to be used and used in intensive care medicine, and then in nephrology. The first chapter of the paper does not correspond to the title of the paper, correct it with an application in nephrology or delete it

Response: We sincerely appreciate your valuable feedback on our manuscript. Your insights have greatly contributed to enhancing the clarity and focus of our work. In response to your comments, we have made significant revisions to the content of Chapter 1. Our aim was to align the introductory section with the specific focus of our paper, which is the utilization of chatbots in Critical Care Nephrology. We have removed the general nephrology context and streamlined the content to provide a more detailed and focused overview of chatbots and their potential applications in Critical Care Nephrology. This adjustment ensures that the title and content of our manuscript are in perfect alignment. Your feedback has been instrumental in improving the quality and relevance of our work, and we thank you once again for your valuable input.

Reviewer comment:

Move the Importance of Chatbots in Critical Care Nephrology section before the Importance of Chatbots in Critical Care Nephrology section

Response: Thank you for your thoughtful observation regarding the structure of our manuscript. We highly value your feedback and have carefully reviewed the placement of the sections based on your recommendation. As per your suggestion, we have reorganized the content by moving the "Importance of Chatbots in Critical Care Nephrology" section before the "Features of Chatbots in Critical Care Nephrology" section. This adjustment ensures a smoother flow of information and a more logical progression of ideas. Your input has been instrumental in refining the structure and coherence of our paper, and we truly appreciate your constructive feedback.

Reviewer comment:

You mention the characteristics of chatbots in intensive care nephrology, and there is no current or previous experience in the use of this technology. Based on the conclusion of the manuscript the author's review paper is a futuristic consideration of some facts and more assumed possibilities about the use of advanced technology in the intensive care of nephrology. The work under the given title begins with the chapter Applications of Chatbots in Critical Care Nephrology. Who did the research from the mentioned segments and what were the results? 

Response: Thank you for your astute comments and constructive feedback on our manuscript. We acknowledge the importance of providing tangible examples and drawing correlations between the theoretical and practical aspects of implementing chatbot technology in Intensive Care Medicine (ICM). As suggested, we have made extensive revisions to the manuscript to address your concerns. Here’s a breakdown of our revisions:

1. Inclusion of Current Literature on Chatbots in ICM: We have incorporated recent studies that delve into the potential advantages of integrating expansive language models like ChatGPT and GPT-4 in intensive care medicine. The study by Lu et al. [38] and Alhasan et al. [39] serves as pivotal references that showcase both the promise and the challenges posed by such technologies in a critical care setting.
2. Highlighting the Role of Critical Care Nephrologists: We have expanded on the role of critical care nephrologists, especially in the context of managing patients with acute kidney injury (AKI) in the ICU.
3. Title Modification for Balanced Representation: Heeding your advice, we have modified our title from the assertive “Harnessing the Power of Chatbots for Critical Care Nephrology” to the more exploratory "Exploring the Potential of Chatbots in Critical Care Nephrology." This change better reflects the tentative and investigational nature of our study, ensuring that the manuscript doesn't over-promise or mislead readers about the current state of technology in this domain.

The following text has been added to the manuscript as suggested:

“In recent years, emerging technological innovations have brought transformative changes to various domains of medicine, and Intensive Care Medicine (ICM) has been a prominent beneficiary of these advancements[37]. The relevance of innovative methods, particularly AI, in ICM has been underscored by the emergence of the COVID-19 pandemic, which has heightened the demand for ICU resources. Lu et al. [38] recently explored the potential advantages of incorporating expansive language models like ChatGPT and GPT-4 within the realm of intensive care medicine. By harnessing the capabilities of AI-driven chatbots, such as ChatGPT and GPT-4, medical specialists can enhance patient treatment through enriched knowledge, meticulous data analysis, and astute decision-making. The integration of human expertise and AI support holds the potential to revolutionize clinical methodologies, bolster resource allocation, and amplify patient outcomes within the context of intensive care medicine.

In another article by Alhasan et al. [39], the challenges posed by pediatric respiratory viruses like RSV and metapneumovirus following the COVID-19 pandemic are examined. The authors highlight the role of ChatGPT in providing guidance for specialists in pediatric intensive care. ChatGPT's advisories encompass adherence to established protocols, infection prevention strategies, vigilant patient monitoring, respiratory support, management of pre-existing conditions, accurate medication administration, holistic patient care, patient education, and self-care emphasis. While the study acknowledges the swiftness with which ChatGPT processes information and offers suggestions, it also emphasizes the importance of corroborating AI-generated guidelines with clinical discernment tailored to each patient. The potential of ChatGPT to offer guidance during evolving viral outbreaks is evident, though considerations regarding biases and inaccuracies warrant further exploration, highlighting the necessity for human specialists' validation.

We, as critical care nephrologists, play a pivotal role in managing patients who are critically ill, particularly within the intensive care unit (ICU) setting. Among the challenges we encounter, a significant portion involves patients suffering from acute kidney injury (AKI), a condition associated with elevated mortality rates. Given this context, it is crucial to explore the utilization of chatbots for critically ill patients in the realm of critical care nephrology, with the aim of enhancing patient outcomes. While the integration of chatbots into critical care nephrology is not yet widespread, the concept of incorporating these AI-driven tools into our specialized field holds significant importance. Our expertise lies in caring for acutely ill patients grappling with acute kidney injury, many of whom require immediate renal replacement therapy. In this context, the application of AI-driven chatbots could offer substantial advantages. These chatbots could serve as complementary tools, aiding us in navigating complex clinical scenarios, analyzing patient data, and providing real-time insights that contribute to informed decision-making.

As we navigate the evolving landscape of intensive care medicine, we recognize the potential applications of AI and their implications. Given our unique position as critical care nephrologists, it is vital to carefully consider and explore how chatbots can augment our ability to provide optimal care to critically ill patients with AKI. The integration of these AI tools has the potential to bridge gaps in knowledge, streamline processes, and ultimately lead to improved patient outcomes. By embracing innovation while upholding our expertise, we can harness the synergy between human insight and AI support to elevate the quality of care we provide in the ICU”

Revised Sentences: In Chapter 3.3 Personalized patient education

“The systematic review of randomized control trials on the effectiveness of chatbots in healthcare intervention demonstrated that implementing chatbots is feasible and positively affects physical functioning, healthy lifestyle, mental health, and psychosocial outcomes. Furthermore, chatbots could support patient health before or after medical treatment [78].”

In Chapter 4.1 Symptom Management and Palliative options

“Chatbots can be programmed to deliver personalized information and support for symptom management [70,128]. The result from the previous study, which investigated the interaction between cancer patients and chatbots in monitoring patients' symptoms and concerns, showed that providing medical reminders and giving information by chatbots positively impacted patient satisfaction by 94%. Additionally, the chatbots helped and supported patients to effectively follow their treatment, with a rate of 88% [129].”

Reviewer comment:

Nice charts and graphs are missing.

Response: We sincerely appreciate the thoughtful feedback provided by the reviewer regarding the visual presentation in our work. Recognizing the importance of clear and illustrative visual aids, we have taken your valuable input to heart and have made significant improvements. We are delighted to inform you that we have crafted four new figures that serve to enhance the understanding of the content and significance of our study. These additions are intended to not only complement the textual explanations but also to provide readers with a structured and visually appealing overview of our findings.

Figure 1: We have included a structured overview that succinctly encapsulates the profound significance of chatbots in shaping the future landscape of critical care nephrology. This figure aims to provide readers with a comprehensive snapshot of the pivotal role that chatbots play in advancing patient care and medical practices.

Figure 2: Addressing the need for clarification on chatbot features and their potential applications, we have designed an illustrative depiction that showcases the various capabilities and potential utilization scenarios of chatbots in critical care nephrology. This visual aid aims to elucidate the versatility and benefits of integrating chatbots within the field.

Figure 3: To better address the challenges and limitations associated with the implementation of chatbots in the medical domain, we have created a dedicated figure that highlights these aspects in a visually compelling manner. This figure offers readers an insightful view of the complexities involved while considering the potential benefits.

Figure 4: To provide a more nuanced understanding of the practical applications of chatbots in critical care nephrology, we have developed an additional figure that visually outlines these applications. This figure is designed to effectively convey the scope and impact of chatbots across various aspects of patient care and medical management.

We appreciate the reviewer’s suggestion and we are confident that these newly added figures will significantly enhance the visual clarity and comprehensibility of our work. Thank you for guiding us toward improvements that ultimately contribute to the overall quality of our study. Your constructive feedback is truly invaluable to us, and we are committed to continually refining our work to provide the most informative and impactful content possible.

Figure 1. Structured overview of the significance of chatbots in the future of critical care nephrology

Figure 2. Features and Potential Utilization of Chatbots in Critical Care Nephrology

Figure 3. Challenges and Limitations of Chatbot Implementation

Figure 4. Potential Applications of Chatbots in Critical Care Nephrology

Reviewer comment:

I think that a lot of effort was put into the paper and that the authors should either change the title with the attached content or make two papers from the original content. Thank you for the review invitation.

Response: We are genuinely grateful for your thoughtful consideration of our manuscript and for providing such insightful feedback. Your perspective has been instrumental in refining our work, and we deeply appreciate your dedication to maintaining the integrity and quality of the academic discourse.

In response to your suggestions, we have made several significant changes to the manuscript to ensure a balanced and thorough representation of the subject matter:

Title Modification for Balanced Representation: In response to your suggestion, we have taken the opportunity to adjust our title for a more accurate portrayal of the content. The original title, "Harnessing the Power of Chatbots for Critical Care Nephrology," has been transformed into the more exploratory and nuanced "Exploring the Potential of Chatbots in Critical Care Nephrology." This change aligns with the tentative nature of our study and ensures that readers are met with a title that accurately conveys the exploratory nature of our research.

Inclusion of Current Literature on Chatbots in ICM: We have taken your advice to heart and made substantial updates to incorporate recent and relevant studies in the field. We have included references to studies conducted by Lu et al. [38] and Alhasan et al. [39], which highlight the potential benefits and challenges associated with the integration of expansive language models like ChatGPT and GPT-4 in the context of intensive care medicine. These references not only enrich the content but also provide readers with a broader perspective on the current landscape of chatbot technology.

Highlighting the Role of Critical Care Nephrologists: In response to your suggestion, we have expanded on the crucial role played by critical care nephrologists, particularly in the management of patients with acute kidney injury (AKI) within the ICU setting. By delving deeper into the responsibilities and expertise of these specialists, we aim to provide a comprehensive understanding of how chatbots can augment their abilities and improve patient care outcomes.

Furthermore, we are pleased to inform you that we have incorporated new illustrative figures into the manuscript, which serve to enhance the visual representation and clarity of our findings. These figures aim to provide readers with a more comprehensive overview of the potential applications, challenges, and benefits of integrating chatbots in critical care nephrology.

Once again, we extend our heartfelt gratitude for your feedback, which has significantly contributed to the refinement of our work. Your insights have guided us in producing a more comprehensive and informative manuscript that we believe will contribute positively to the academic discourse in this field.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

Please find my comments.

Major comments:

1.     Please provide graphical summaries.

2.     Also, discuss other major limitations and potential challenges related to the use of AI chatbots, such as hallucinations and model drifting.

3.     Include illustrative examples in the supplementary material; these are not necessary for the main text.

4.     Incorporate a visual overview.

5.     Include clear diagrams for the key sections.

6.     Provide real-world examples of existing AI chatbots and how they're being applied in different aspects of healthcare.

7.     Include examples from other prominent AI Chatbots like Bard and Bing in the supplementary section.

Minor Points:

1.     To back up the statement "Artificial intelligence (AI) has made significant strides in recent decades, particularly in its applications within the healthcare sector," consider referencing the article (https://www.medrxiv.org/content/10.1101/2022.12.07.22283216v2.full-text) that highlights the rising trend of FDA-approved AI devices/CADs in healthcare settings.

2.     Regarding the statement "One area that shows great potential is using chatbots in critical care, specifically in nephrology," it might help to elaborate on why this area holds promise and discuss why psychiatry, for instance, might not be as well-suited.

3.     Line 35 needs adjustment. ChatGPT 3.5, ChatGPT 4.0, Bard AI, and Bing Chat aren't algorithms; rather, they're user interfaces and web interfaces. The algorithms driving them, GPT and PALM, are built on language models. Please make sure to rectify this.

4.     For statements like "Moreover, chatbots have the potential to assist healthcare providers in making well-informed decisions," adding specific references would enhance the credibility.

5.     It appears that line 87 and line 90 could be a bit confusing. Consider revising them for clarity.

6.     Don't forget to acknowledge other types of machine learning beyond the two primary ones you mentioned, like supervised, unsupervised, and semi-supervised learning.

7.     To improve coherence, consider refining the transition from line 98 to line 99.

Minor editing required.

Author Response

Reviewer 2

Dear Authors,

Please find my comments.

Response: We are sincerely grateful for taking your precious time to review our manuscript. Your invaluable insights and contributions are highly valued. In response, we have revised the manuscript as follows:

Major comments:

1. Please provide graphical summaries.

Response: Thank you for your remark about providing graphic summaries. We totally agree with the reviewer that visual summaries can improve our manuscript. Recognizing the importance of clear and illustrative visual aids, we have taken your valuable input to heart and have made significant improvements. We are delighted to inform you that we have crafted four new figures that serve to enhance the understanding of the content and significance of our study. These additions are intended to not only complement the textual explanations but also to provide readers with a structured and visually appealing overview of our findings.

Figure 1: We have included a structured overview that succinctly encapsulates the profound significance of chatbots in shaping the future landscape of critical care nephrology. This figure aims to provide readers with a comprehensive snapshot of the pivotal role that chatbots play in advancing patient care and medical practices.

Figure 2: Addressing the need for clarification on chatbot features and their potential applications, we have designed an illustrative depiction that showcases the various capabilities and potential utilization scenarios of chatbots in critical care nephrology. This visual aid aims to elucidate the versatility and benefits of integrating chatbots within the field.

Figure 3: To better address the challenges and limitations associated with the implementation of chatbots in the medical domain, we have created a dedicated figure that highlights these aspects in a visually compelling manner. This figure offers readers an insightful view of the complexities involved while considering the potential benefits.

Figure 4: To provide a more nuanced understanding of the practical applications of chatbots in critical care nephrology, we have developed an additional figure that visually outlines these applications. This figure is designed to effectively convey the scope and impact of chatbots across various aspects of patient care and medical management.

We appreciate the reviewer’s suggestion and we are confident that these newly added figures will significantly enhance the visual clarity and comprehensibility of our work. Thank you for guiding us toward improvements that ultimately contribute to the overall quality of our study. Your constructive feedback is truly invaluable to us, and we are committed to continually refining our work to provide the most informative and impactful content possible.

Figure 1. Structured overview of the significance of chatbots in the future of critical care nephrology

Figure 2. Features and Potential Utilization of Chatbots in Critical Care Nephrology

Figure 3. Challenges and Limitations of Chatbot Implementation

Figure 4. Potential Applications of Chatbots in Critical Care Nephrology

2. Also, discuss other major limitations and potential challenges related to the use of AI chatbots, such as hallucinations and model drifting.

Response: We deeply appreciate your thoughtful insights and valuable suggestions regarding the inclusion of major limitations and potential challenges related to the use of AI chatbots. Your input has indeed contributed to refining the comprehensiveness of our manuscript. We are in complete agreement with your assessment that addressing these challenges is pivotal for a well-rounded understanding of the implications of utilizing AI chatbots in critical care nephrology. As per your recommendations, we have meticulously incorporated the following passages into the revised manuscript as suggested.

“Model drift

Ensuring the accuracy and reliability of data when incorporating chatbots into patient care metrics can be challenging due to the phenomenon of model drifting. In the real world, data is constantly changing due to environmental factors and the growth of innovative input data over time. Model drift, or model decay, refers to the decline in machine learning performance over time. The data the model was trained on could have low accuracy, become outdated, or no longer be relevant to the current situation[89]. There are two types of model drift: 1) Concept drift happens when the input and target variable changes can be gradual, sudden, incremental, or recurring. 2) Data drift happens when input data change over time, such as the distribution of ages[89,90]. As a consequence of model drifting, there is a possibility that the algorithm powering the chatbot AI may generate inaccurate data. Implementing rigorous testing and validation protocols, as well as ongoing monitoring, retraining, and maintenance of data quality, can minimize the risks associated with these issues and continue to advance the field of chatbots in healthcare in a responsible and sustainable manner.”

Artificial Hallucination

                The phenomenon of AI or chatbots generating plausible information that is not aligned with reality has been described as artificial hallucination [91,92]. Hallucinations in natural language models can result from decoding errors, biases from previously generated data, or model encodes. It is crucial to address these sources to ensure accurate and relevant output. Previous studies demonstrated that chatbots generate fabricated references 30-40% [92,94]. Mitigating hallucinations in chatbots by ensuring the system is well-trained and tested using a diverse and representative data set is crucial. Furthermore, incorporating monitoring and using critical thinking before implementing data might alleviate this issue.”

3. Include illustrative examples in the supplementary material; these are not necessary for the main text.

Response: We genuinely appreciate your feedback and thoughtful consideration of our manuscript. Your suggestion to include illustrative examples in the supplementary material is duly noted, and we respect your perspective on this matter. We would like to share that Figures 5 and 6 are, indeed, illustrative examples that have been thoughtfully created using ChatGPT. These examples are a crucial part of showcasing the practical application of chatbot technology within the context of critical care nephrology. As our article revolves around highlighting the utilization of chatbots in this specialized field, we believe that including these examples in the main body of the article helps to provide a tangible and relatable insight into the potential scenarios where chatbots can play a pivotal role.

While we understand the suggestion to potentially move these examples to the supplementary material, we believe that keeping them within the article itself enhances the reader's comprehension of our research findings and contributes to the overall narrative.

We genuinely value your input and are grateful for your thorough review. If there are any further adjustments or modifications you recommend, please know that we are committed to ensuring the highest quality of our work while also considering the most effective way to communicate the essence of our study to the readers.

Thank you once again for your constructive feedback, and we are dedicated to addressing any other aspects you may deem necessary.

1. Incorporate a visual overview.

Response: We appreciate your thoughtful feedback and agree with your suggestion to incorporate a visual overview. This addition will undoubtedly enhance the quality and clarity of our content, making it more accessible and engaging for our readers. By integrating a well-designed visual representation, we aim to provide a comprehensive overview of the subject matter while maintaining the high standard of detail and accuracy that is important to us. The visual overview has been created as suggested.

5. Include clear diagrams for the key sections.

Response: Thank you for your valuable feedback. We wholeheartedly agree with your suggestion to include clear diagrams for the key sections of our content. Diagrams are indeed an effective way to visually represent complex concepts and relationships, enhancing the overall understanding and engagement of our readers. Your recommendation aligns perfectly with our commitment to providing detailed, informative, and well-organized materials. We have created diagrams for key sections as suggested.

6. Provide real-world examples of existing AI chatbots and how they're being applied in different aspects of healthcare.

Response: Thank you for your valuable suggestion to provide real-world examples of AI chatbots in healthcare applications. We have taken your feedback into consideration and have made the following revisions to our content to incorporate these examples:

Revised Sentences: In Chapter 3.3 Personalized patient education

“The systematic review of randomized control trials on the effectiveness of chatbots in healthcare intervention demonstrated that implementing chatbots is feasible and positively affects physical functioning, healthy lifestyle, mental health, and psychosocial outcomes. Furthermore, chatbots could support patient health before or after medical treatment [78].”

In Chapter 4.1 Symptom Management and Palliative options

“Chatbots can be programmed to deliver personalized information and support for symptom management [70,128]. The result from the previous study, which investigated the interaction between cancer patients and chatbots in monitoring patients' symptoms and concerns, showed that providing medical reminders and giving information by chatbots positively impacted patient satisfaction by 94%. Additionally, the chatbots helped and supported patients to effectively follow their treatment, with a rate of 88% [129].”

7. Include examples from other prominent AI Chatbots like Bard and Bing in the supplementary section.

Response: We genuinely appreciate your insight on this matter. We have additionally added the following example from Bing Chat and Bard AI in the supplementary material in revised manuscript as suggested

Minor Points:

1. To back up the statement "Artificial intelligence (AI) has made significant strides in recent decades, particularly in its applications within the healthcare sector," consider referencing the article (https://www.medrxiv.org/content/10.1101/2022.12.07.22283216v2.full-text) that highlights the rising trend of FDA-approved AI devices/CADs in healthcare settings.

Response:  Thank you for your valuable input on the importance reference. Your suggestion to include specific sources helps strengthen our assertion about the application of AI in health care. We agree and have additionally incorporated the new reference (#29) into our revised manuscript. This is an excellent addition that aligns with our commitment to accuracy and thoroughness. We found this reference helps solidify our assertion and enhances the overall quality of our content.

Original sentence: Artificial intelligence (AI) has made significant advancements in recent decades, particularly in its application within the healthcare sector [25-28].

Revised sentence: Artificial intelligence (AI) has made significant advancements in recent decades, particularly in its application within the healthcare sector [25-29].

2. Regarding the statement "One area that shows great potential is using chatbots in critical care, specifically in nephrology," it might help to elaborate on why this area holds promise and discuss why psychiatry, for instance, might not be as well-suited.

Response: We sincerely appreciate for your feedback. We have revised and added the information to elaborate the significance of critical care nephrology as follows:

Original Sentence: "One area that shows great potential is using chatbots in critical care, specifically in nephrology,"

Revised Sentence: In critical care settings, several multidisciplinary care teams play a crucial role in patient care. Nephrology has emerged as one of the most consulted in recent years. Acute kidney injury (AKI) incidence in critically ill patients has been reported, ranging from 30% to 57% [30-32]. AKI, renal complications, and dialysis intervention correlate with significantly higher morbidity and mortality. Therefore, it is essential to highlight the significance of critical care nephrology to improve patient outcomes. The integration of chatbots into this process could potentially enhance patient metrics.

3. Line 35 needs adjustment. ChatGPT 3.5, ChatGPT 4.0, Bard AI, and Bing Chat aren't algorithms; rather, they're user interfaces and web interfaces. The algorithms driving them, GPT and PALM, are built on language models. Please make sure to rectify this.

Response: We sincerely appreciate your thoughtful consideration in making the details of the chatbot example. Your feedback is valuable. To ensure it is precise and clear, we have revised the sentence as follows:

Original sentence: These chatbots, powered by AI algorithms like ChatGPT 3.5, ChatGPT 4.0, Bard AI, and Bing chat, have the potential to revolutionize patient care, enhance clinical decision-making, and improve overall healthcare outcomes.

Revise sentence: “AI chatbots employ deep learning and natural language processing (NLP) to generate human-like responses. Notable examples include OpenAI’s ChatGPT (powered by GPT-3.5 and 4.0), Microsoft’s Bing Chat (which utilizes GPT-4.0), and Google’s Bard AI, built on the PaLM (Pathway Language Model). These chatbots have the potential to revolutionize patient care, enhance clinical decision-making, and improve healthcare outcomes.”

4. For statements like "Moreover, chatbots have the potential to assist healthcare providers in making well-informed decisions," adding specific references would enhance the credibility.

Response: We acknowledge the importance of substantiating our claims with specific references.

Revise Sentence: Moreover, chatbots have the potential to assist healthcare providers in making well-informed decisions [37-41]

5. It appears that line 87 and line 90 could be a bit confusing. Consider revising them for clarity.

Response: We are grateful for the reviewer's insightful feedback.  We agree with the reviewer that the sentence in the paragraph can make readers confused and misunderstand. In order to improve the precision and clarity, we have revised the sentence as follows:

“Machine Learning plays a pivotal role in the development of chatbots, enabling them to learn from data and improve their performance [10,27,33]. The majority types of machine learning algorithms employed in chatbots are 1) supervised learning ….”

6. Don't forget to acknowledge other types of machine learning beyond the two primary ones you mentioned, like supervised, unsupervised, and semi-supervised learning.

Response: Thank you for your insightful feedback and for highlighting the importance of acknowledging the various types of machine learning methodologies beyond the primary ones we initially mentioned. We concur with your observation and recognize the significance of providing a comprehensive overview of machine learning techniques in the context of chatbot development.

In response to your comment, we have made the following revisions to our manuscript:

"The realm of machine learning is vast and encompasses a variety of methodologies, each with its unique advantages and applications. In the context of chatbot development, several machine learning algorithms play pivotal roles:

1. Supervised Learning: This approach involves training the chatbot using labeled datasets, where each input is paired with a predefined output. By learning from these examples, the algorithm can classify new inputs and generate appropriate responses. Supervised learning is particularly advantageous for tasks such as intent recognition and entity extraction.
2. Unsupervised Learning: Chatbots trained using this methodology rely on unlabeled datasets. The model is primarily built on self-learning derived from historical chat log data. This results in conversation interactions that are more diverse and natural compared to those generated through supervised learning. However, a potential drawback of unsupervised learning is the possibility of generating inaccurate results due to the absence of predefined outputs.
3. Semi-supervised Learning: This is a hybrid approach that combines elements of both supervised and unsupervised learning. It utilizes unlabeled data to cluster related information and subsequently labels this data to enhance the learning process.
4. Reinforcement Learning: This iterative methodology involves training the chatbot through a trial-and-error process. The chatbot interacts with users, receives feedback on the quality of its responses, and adjusts its behavior based on this feedback. By maximizing rewards received for desirable actions, reinforcement learning enables the chatbot to continuously refine and enhance its performance over time.”

7. To improve coherence, consider refining the transition from line 98 to line 99.

Response: We recognize the importance of coherence. We agree with your suggestion and have revised the manuscript more clearly by categorizing this into numbers based on the essential components of chatbot models: 1) Natural Language Processing (NLP), 2) Machine learning, and 3) Dialogue management.

Your feedback has greatly contributed to the precision and accuracy of our content, and we are grateful for your thoughtful guidance. Should you have any further insights, recommendations, or observations, please don't hesitate to share them. Your engagement plays a crucial role in elevating the quality of our work.

Author Response File: Author Response.pdf

Reviewer 3 Report

The topic of AI use in acute nephrology is very interesting and has a promising future, an update on the potential benefits of AI and chatbots in intensive nephrology is welcome. Such technologies can indeed benefit both clinician and patient. However, on reading the manuscript, it is not clear what has been achieved with chatbots in well-conducted studies in acute nephrology, and what is in the field of future progress. 

Unfortunately the review is difficult to read, numerous repetitions within the manuscript complicate reading. Especially in paragraph 1. I suggest to shorten and simplify this part.

The authors cite a lot of studies but the results of these studies are never detailed. If the authors want to convince people of the value of chatbots, the quantified results of these studies should be highlighted more clearly.

The authors are a little too enthusiastic when they say that chatbots are currently revolutionizing intensive care nephrology, in the absence of robust data showing an improvement in outcomes such as mortality, risk of AKI, dialysis dependence...

A substantial rewrite of the manuscript seems to me necessary to make it easier to understand. 

Author Response

Reviewer 3

The topic of AI use in acute nephrology is very interesting and has a promising future, an update on the potential benefits of AI and chatbots in intensive nephrology is welcome. Such technologies can indeed benefit both clinician and patient. However, on reading the manuscript, it is not clear what has been achieved with chatbots in well-conducted studies in acute nephrology, and what is in the field of future progress. 

Unfortunately the review is difficult to read, numerous repetitions within the manuscript complicate reading. Especially in paragraph 1. I suggest to shorten and simplify this part.

The authors cite a lot of studies but the results of these studies are never detailed. If the authors want to convince people of the value of chatbots, the quantified results of these studies should be highlighted more clearly.

Response: We extend our heartfelt gratitude for your meticulous review and thoughtful insights into our manuscript. Your expertise and guidance have been instrumental in shaping our revisions to address the concerns you raised. We want to assure you that we have dedicated our best efforts to incorporate your feedback in a manner that enhances the clarity, coherence, and depth of the content. Your valuable suggestions have guided us in redefining key aspects of the manuscript, ensuring that our work meets the highest standards of quality and relevance. Your commitment to helping us deliver the best possible outcomes is greatly appreciated, and we want to acknowledge the respect we hold for your expertise in this field.

We want to express our sincere appreciation for your guidance in refining our revised manuscript. We have additionally provided a comprehensive overview of the changes we have diligently implemented to address the reviewer’s comments:

1. Inclusion of Current Literature: We meticulously integrated recent studies, notably the works of Lu et al. [38] and Alhasan et al. [39], to amplify our discussion on the potential benefits and challenges tied to advanced language models such as ChatGPT and GPT-4 in the intensive care medicine context. These pivotal references are now pivotal anchors in our exploration of both the current landscape and the future prospects of AI chatbots within the realm of critical care nephrology.
2. Highlighting Critical Care Nephrologists' Role: We have significantly expanded upon the role of critical care nephrologists, particularly in the management of patients experiencing acute kidney injury (AKI) within the ICU environment. By providing a more comprehensive context, we intend to underscore the relevance and critical significance of AI chatbots in enhancing patient care within this highly specialized domain.
3. Balanced Title Representation: In direct response to your valuable advice, we have meticulously adjusted our title to aptly reflect the exploratory essence of our study. Our new title, "Exploring the Potential of Chatbots in Critical Care Nephrology," precisely captures the investigative nature of our research. This adjustment ensures that our portrayal of the current technological landscape maintains a balanced perspective, avoiding any overstatements or misleading depictions.
4. Visual Aids Inclusion: We are thrilled to inform you that we have thoughtfully crafted four new figures, as per your insightful suggestion. These visual aids seamlessly align with our textual explanations and are designed to offer readers a structured, visually engaging overview of our findings. Our intention is to provide an enhanced understanding of the content's significance through these visually impactful additions.
5. Enhanced Study Results Presentation: In the sections you thoughtfully pointed out, we have meticulously provided detailed information about the referenced studies. Our aim here is to provide readers with a comprehensive grasp of the quantified results and outcomes that these studies have brought to light, thereby enriching the depth of understanding.

The following text has been added in the manuscript as suggested:

“In recent years, emerging technological innovations have brought transformative changes to various domains of medicine, and Intensive Care Medicine (ICM) has been a prominent beneficiary of these advancements[37]. The relevance of innovative methods, particularly AI, in ICM has been underscored by the emergence of the COVID-19 pandemic, which has heightened the demand for ICU resources. Lu et al. [38] recently  explores the potential advantages of incorporating expansive language models like ChatGPT and GPT-4 within the realm of intensive care medicine. By harnessing the capabilities of AI-driven chatbots, such as ChatGPT and GPT-4, medical specialists can enhance patient treatment through enriched knowledge, meticulous data analysis, and astute decision-making. The integration of human expertise and AI support holds the potential to revolutionize clinical methodologies, bolster resource allocation, and amplify patient outcomes within the context of intensive care medicine.

In another article by Alhasan et al. [39], the challenges posed by pediatric respiratory viruses like RSV and metapneumovirus following the COVID-19 pandemic are examined. The authors highlight the role of ChatGPT in providing guidance for specialists in pediatric intensive care. ChatGPT's advisories encompass adherence to established protocols, infection prevention strategies, vigilant patient monitoring, respiratory support, management of pre-existing conditions, accurate medication administration, holistic patient care, patient education, and self-care emphasis. While the study acknowledges the swiftness with which ChatGPT processes information and offers suggestions, it also emphasizes the importance of corroborating AI-generated guidelines with clinical discernment tailored to each patient. The potential of ChatGPT to offer guidance during evolving viral outbreaks is evident, though considerations regarding biases and inaccuracies warrant further exploration, highlighting the necessity for human specialists' validation.

We, as critical care nephrologists, play a pivotal role in managing patients who are critically ill, particularly within the intensive care unit (ICU) setting. Among the challenges we encounter, a significant portion involves patients suffering from acute kidney injury (AKI), a condition associated with elevated mortality rates. Given this context, it is crucial to explore the utilization of chatbots for critically ill patients in the realm of critical care nephrology, with the aim of enhancing patient outcomes. While the integration of chatbots into critical care nephrology is not yet widespread, the concept of incorporating these AI-driven tools into our specialized field holds significant importance. Our expertise lies in caring for acutely ill patients grappling with acute kidney injury, many of whom require immediate renal replacement therapy. In this context, the application of AI-driven chatbots could offer substantial advantages. These chatbots could serve as complementary tools, aiding us in navigating complex clinical scenarios, analyzing patient data, and providing real-time insights that contribute to informed decision-making.

As we navigate the evolving landscape of intensive care medicine, we recognize the potential applications of AI and their implications. Given our unique position as critical care nephrologists, it is vital to carefully consider and explore how chatbots can augment our ability to provide optimal care to critically ill patients with AKI. The integration of these AI tools has the potential to bridge gaps in knowledge, streamline processes, and ultimately lead to improved patient outcomes. By embracing innovation while upholding our expertise, we can harness the synergy between human insight and AI support to elevate the quality of care we provide in the ICU”

Revised Sentences: In Chapter 3.3 Personalized patient education

“The systematic review of randomized control trials on the effectiveness of chatbots in healthcare intervention demonstrated that implementing chatbots is feasible and positively affects physical functioning, healthy lifestyle, mental health, and psychosocial outcomes. Furthermore, chatbots could support patient health before or after medical treatment [78].”

In Chapter 4.1 Symptom Management and Palliative options

“Chatbots can be programmed to deliver personalized information and support for symptom management [70,128]. The result from the previous study, which investigated the interaction between cancer patients and chatbots in monitoring patients' symptoms and concerns, showed that providing medical reminders and giving information by chatbots positively impacted patient satisfaction by 94%. Additionally, the chatbots helped and supported patients to effectively follow their treatment, with a rate of 88% [129].”

The authors are a little too enthusiastic when they say that chatbots are currently revolutionizing intensive care nephrology, in the absence of robust data showing an improvement in outcomes such as mortality, risk of AKI, dialysis dependence...

Response: We extend our sincere gratitude for your thoughtful assessment of our manuscript. Your perspective sheds valuable light on the enthusiasm we expressed regarding the potential revolution of intensive care nephrology through chatbots. We acknowledge the importance of grounded evidence in making such assertions, particularly when it comes to outcomes as significant as mortality rates, risk of AKI, and dialysis dependence. Our intention is to provide a nuanced view of the potential areas of development for AI chatbots that could be implemented to augment critical care nephrology. We want to emphasize that our manuscript has undergone substantial modifications to align with your valuable insights. One such adjustment includes the title modification, which reflects the exploratory and investigational nature of our study. Our new title, "Exploring the Potential of Chatbots in Critical Care Nephrology," meticulously captures the essence of our research and ensures a balanced portrayal of the current technological landscape. Furthermore, we have endeavored to adopt a more measured tone, avoiding overstatements or misleading portrayals of the potential applications. We understand that human oversight and intervention remain pivotal across all healthcare fields, including the implementation of AI chatbots.

Comment:

A substantial rewrite of the manuscript seems to me necessary to make it easier to understand. 

Response: We genuinely appreciate your feedback and thoughtful consideration of our manuscript. Your insight regarding the need for a substantial rewrite to enhance comprehension resonates deeply with us. We acknowledge the importance of presenting our research in a clear and accessible manner, and we want to assure you that we have taken your suggestions to heart.

To address the need for improved readability and understanding, we have undertaken a comprehensive revision of the manuscript. Our aim has been to streamline the content, simplify complex concepts, and enhance the overall coherence of the narrative. This revision seeks to create a smoother flow of information, ensuring that our work is more approachable and engaging for our readers.

In response to your suggestion, we are pleased to inform you that we have created visual summaries to accompany the textual explanations. We fully concur with your assessment that visual aids can significantly improve the clarity and impact of our manuscript. To this end, we have crafted four new figures that serve as structured and visually appealing overviews of our findings. These visual aids not only complement our explanations but also provide readers with an intuitive grasp of the content's significance.

In addition to comprehensively revise as suggested, we also additionally incorporated clear diagrams for the key sections and a visual overview is a testament to our commitment to creating a reader-friendly and informative manuscript. 

Figure 1. Structured overview of the significance of chatbots in the future of critical care nephrology

Figure 2. Features and Potential Utilization of Chatbots in Critical Care Nephrology

Figure 3. Challenges and Limitations of Chatbot Implementation

Figure 4. Potential Applications of Chatbots in Critical Care Nephrology

Your guidance has played a pivotal role in shaping the direction of our revisions, and we genuinely value your contribution. Should you have further insights, suggestions, or observations, please know that we are wholeheartedly receptive. Your continued engagement remains an integral part of our endeavor to create content that is both informative and accessible.

Thank you for your time and consideration.  We greatly appreciated the reviewer's and editor's time and comments to improve our manuscript. The manuscript has been improved considerably by the suggested revisions.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear authors,

I would like to thank the modifications to the manuscript and the improvement in its quality.

With kind regards 

Author Response

Reviewer 1.

Dear authors,

I would like to thank the modifications to the manuscript and the improvement in its quality.

With kind regards 

Response: We want to express our sincere gratitude for taking the time to review our manuscript. Your feedback has been instrumental in improving the quality of our work, and we greatly value your insights. Your contributions are invaluable.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

All the comments are addressed.

Revised manuscript looks better however please make sure to properly format the figures and tables.
Also try to increase resolution for the figures, whenever possible. Use professional software such as Adobe etc.

Minor editing of English language required

Author Response

Reviewer 2.

Dear Authors,

All the comments are addressed.

Revised manuscript looks better however please make sure to properly format the figures and tables.

Also try to increase resolution for the figures, whenever possible. Use professional software such as Adobe etc.

Response: We appreciate your thorough review of our manuscript and your constructive feedback. Your suggestion regarding the formatting of figures and tables has been duly noted, and we have additionally made revision as suggested to ensure that they are properly formatted and have increased resolution using professional software. We are committed to delivering the highest quality presentation of our findings. Thank you for your meticulous attention to detail.

Author Response File: Author Response.pdf

Reviewer 3 Report

I thank the authors for adressing my remarks and deeply modified on the manuscript. The manuscript is improved and much clearer now with the implementation of the figures.

However a focus on the results of the main published studies about chatbots in the field of critical care nephrology with details about the numbers and statistics, is missing. It is important in such a review in my opinion. I suggest the authors to add a paragraph detailing these results.

Author Response

Reviewer 3.

I thank the authors for addressing my remarks and deeply modified on the manuscript. The manuscript is improved and much clearer now with the implementation of the figures.

However a focus on the results of the main published studies about chatbots in the field of critical care nephrology with details about the numbers and statistics, is missing. It is important in such a review in my opinion. I suggest the authors to add a paragraph detailing these results.

Response: We would like to extend our gratitude for your insightful comments and suggestions. Your point about including a focus on the statistical data in the field of critical care and chatbot in nephrology is well-taken. We have incorporated this suggestion into the revised manuscript as suggested. We have also generated new Figure 7 summarizing performance metric comparisons between chatbots as suggested.

Chapter 4.2 Accuracy and Reliability Concern

Previous studies in medical content and nephrology demonstrated that chatbots generate fabricated references 30-40% [103-105]. The references given by the chatbot were only 20% authentic. Additionally, the chatbot provided nephrology references with incorrect digital object identifiers (DOIs) and links at 54% and 68%, respectively. Notably, 24% of references in AKI fields were authentic, while references on hemodialysis were fabricated 60%, and only 4% were genuine[104]. Mitigating hallucinations in chatbots by ensuring the system is well-trained and tested using a diverse and representative data set is crucial. Furthermore, incorporating monitoring and using critical thinking before implementing data might alleviate this issue. 

Chapter 5.3.2 Guiding Prescription and Anticoagulation Management

Chen et al. [44] showed the good performance and feasibility of using AI deep neural network model to detect and determine how to adjust the dose for regional citrate anticoagulant (RCA) of citric acid overdose in CRRT patients by analysis of patient-specific parameters such as coagulation profiles and bleeding risk factors. The overall accuracy of neural network models was 90.77%. Even in anticoagulant-free patients, Zhang et al. validated and used AI to guide and predict filter lifespan among CRRT patients [129]. The finding showed internal validation model and area under the curve was 0.8 (95% CI [0.74-0.87] and 0.82 (95% confidence interval [CI] [0.76–0.88]), respectively.

Chapter 5.4.1. Symptom Management and Palliative Treatment Options:

Chatbots can be programmed to deliver personalized information and support for symptom management[10,11]. The result from the previous study, one-year prospective cohort study which investigated the interaction between 958 cancer patients and chatbots in monitoring patients' symptoms and concerns, 132,970 messages interaction per month was observed. The result showed that providing medical reminders and giving information by chatbots positively impacted patient satisfaction by 94%. Additionally, the chatbots helped and supported patients to effectively follow their treatment, with a rate of 88% [132]. Another noninferiority RCT study compared the chatbot and the physician by giving patients information on 141 breast cancer patients. The finding revealed that the perceived quality of the answer by the chatbot was found to be non-inferior to the scores of the physicians. The success rates in the chatbot and physician group were 69% and 64%, respectively. Patients’ satisfaction was 85% vs. 81% compared to the chatbot versus physician group, and helpful answers were 85% vs. 83%, respectively. 62% of patients needed additional information, 65% in the physician group, and 59% in the chatbot group [133].

We have also generated Figure 7 summarizing performance metric comparisons between chatbots and physicians, as suggested.

Figure 7. Performance Metric Comparisons between chatbot and physician

Your input has been immensely helpful in enhancing the comprehensiveness of our manuscript.

With utmost respect and gratitude

Author Response File: Author Response.pdf