Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 

Review of the Article

“Embedding Fear in Medical AI: A Risk-Averse Framework for Safety and Ethics”

Abstract

• A final sentence should be added that clearly summarizes the expected impact of the proposed framework, emphasizing its practical contribution to enhancing safety in medical AI systems.

1. Introduction

• Redundancy is observed regarding the lack of instinct in AI and the role of "fear." It is suggested that these ideas be condensed to create a more dynamic and impactful introduction.
• It would be valuable to incorporate examples of recent medical incidents where a "fear module" could have helped prevent harm or errors.
• It is advised to differentiate further Sections 1.1 (Context and Motivation) and 1.2 (Aim and Scope), clearly reinforcing their specific purposes.

2. Conceptual Foundations

• When discussing "fear" in biological and artificial systems, it is recommended that the references be updated by including recent studies in computational neuroscience and affective robotics.
• Although the reference to Asimov is interesting, it should be immediately contrasted with current scientific frameworks to maintain academic rigor.
• It is important to improve the graphical quality of all figures, as they present issues with contrast and resolution that hinder proper interpretation.
• Regarding Table 1, it is recommended to introduce a brief explanation beforehand, clarifying its relevance for the reader.

3. Rationale for Embedding Fear

• Although it is stated that there is no direct evidence, it is advisable to link the proposal to related research on risk-aware AI and human-in-the-loop AI, where safety improvements have been demonstrated.
• It is suggested that absolute claims such as "there is no direct evidence" be avoided and that the statement be qualified by supporting it with relevant indirect literature.

4. Proposed Mechanism

• It is recommended to explicitly detail how penalties and risk thresholds would be integrated into real medical pipelines (e.g., imaging diagnosis workflows and clinical decision support systems).
• Although Figures 2 and 3 are pertinent, their understanding could be enhanced by adding brief real or fictional clinical examples alongside the illustrations.
• Expanding the discussion to include technical limitations, such as latency issues in critical medical operations or computational power constraints, is advisable.

5. Implementation Approaches

• Suggesting specific algorithms that could implement the "fear module," such as Proximal Policy Optimization (PPO) in reinforcement learning or Bayesian Deep Learning for uncertainty modeling, would be beneficial.
• It is recommended to explicitly discuss the trade-offs between a more conservative AI system (more excellent safety, reduced agility) and a more proactive one (higher efficiency, increased risk).

6. Counterarguments and Ethical Considerations

• Currently, counterarguments appear somewhat mixed. It is recommended to structure them into clearly separated subsections, such as:
• Risk of Overcautiousness
• Risk of Adversarial Manipulation
• Responsibility Dilemmas
• Adding brief, concrete examples to better illustrate the ethical risks discussed would be valuable.

7. Broader Implications and Future Directions

• Although applications in autonomous vehicles and disaster response are discussed, it is suggested that the scope be expanded by considering AI systems in judicial settings, where a "fear of injustice" could play a crucial regulatory role.
• It would be ideal to conclude this section by proposing open research questions to guide future interdisciplinary investigations.

8. Conclusions and Vision

• In conclusion, it is recommended to highlight this proposal's distinctive contribution compared to traditional "safe AI" approaches, such as classical AI alignment.
• It would be advisable to include an explicit call for interdisciplinary collaboration, emphasizing the need for joint efforts among technologists, healthcare professionals, ethicists, and policymakers.
• To avoid potential misunderstandings, it is necessary to consistently remind the reader that the term "fear-based AI" is a computational metaphor.

 

General Comment

The article is innovative, well-argued, and deeply interdisciplinary. However, its contribution could be further strengthened by:

• Consolidating ideas to avoid repetitions.
• Incorporating empirical examples or recent references to reinforce key arguments.
• Making the technical architecture more tangible by including real-world applications and specific implementation details.

Author Response

Author's Reply to the Review Report (Reviewer 1)   

Quality of English Language

(x) The English could be improved to more clearly express the research.
	Yes	Can be improved	Must be improved	Not applicable
Does the introduction provide sufficient background and include all relevant references?	( )	( )	(x)	( )
Is the research design appropriate?	( )	( )	(x)	( )
Are the methods adequately described?	( )	( )	(x)	( )
Are the results clearly presented?	( )	( )	(x)	( )
Are the conclusions supported by the results?	( )	( )	(x)	( )

Comments and Suggestions for Authors

 Review of the Article

“Embedding Fear in Medical AI: A Risk-Averse Framework for Safety and Ethics”

We thank Reviewer 1 for the detailed review and insightful suggestions, which have greatly helped us improve the clarity, depth, and organization of the manuscript. We address each comment in turn below.

Comment (Quality of English): “The English could be improved to more clearly express the research.”
Response: We appreciate this note. We have thoroughly proofread and edited the manuscript to improve the clarity and quality of the English language. All sentences have been reviewed for grammatical correctness and readability. In particular, we simplified complex sentences and corrected any awkward phrasing to ensure the research is communicated clearly.

Abstract

• A final sentence should be added that clearly summarizes the expected impact of the proposed framework, emphasizing its practical contribution to enhancing safety in medical AI systems.

Response: We agree with the reviewer’s suggestion. We have added a concluding sentence to the Abstract that highlights the practical impact of our framework on safety in medical AI. This new sentence succinctly summarizes how the proposed “fear module” can enhance patient safety and trust in AI systems.

1. Introduction

• Redundancy is observed regarding the lack of instinct in AI and the role of "fear." It is suggested that these ideas be condensed to create a more dynamic and impactful introduction.

Response: We understand the concern about repetitive statements in the Introduction. In response, we have condensed and streamlined the introductory paragraphs to eliminate redundancy. The points about AI lacking an instinctive caution (a “fear instinct”) and our proposal to embed a “fear” mechanism are now stated once clearly rather than repeated. This makes the introduction more concise and impactful.
The first paragraph was completely rewritten.

 

• It would be valuable to incorporate examples of recent medical incidents where a "fear module" could have helped prevent harm or errors.

Response: Yes, we agree that providing examples will strengthen the motivation. We have added a brief example of a real-world medical scenario where lack of an instinctive “caution” in AI led to an error, and noted how a fear module might have prevented it. This addition helps readers visualize the practical importance of our framework.

• It is advised to differentiate further Sections 1.1 (Context and Motivation) and 1.2 (Aim and Scope), clearly reinforcing their specific purposes.

Response: We have revised Sections 1.1 and 1.2 to ensure each has a distinct focus. Section 1.1 now strictly provides background context and the motivation for our approach (why a “fear” module is needed, referencing challenges and gaps). Section 1.2 now clearly states the aim of the paper and its scope without rehashing the context. We removed overlapping content between these sections and adjusted topic sentences to reinforce their separate roles.

2. Conceptual Foundations

• When discussing "fear" in biological and artificial systems, it is recommended that the references be updated by including recent studies in computational neuroscience and affective robotics.

Response: We have updated the literature discussion on fear in both biological and artificial contexts to include recent relevant studies from computational neuroscience and affective robotics. In particular, we added citations of recent research (from 2022–2024) that explore fear or emotion-like mechanisms in AI and advanced neural modeling of fear responses. This inclusion ensures our references are up-to-date and interdisciplinary. We have added approximately 12 new references.

• Although the reference to Asimov is interesting, it should be immediately contrasted with current scientific frameworks to maintain academic rigor.

Response: We have followed this advice. In the manuscript, right after mentioning Asimov’s fictional Laws of Robotics, we now immediately contrast those with a modern, scientific AI safety framework. Specifically, we added a sentence noting that Asimov’s laws, while influential in science fiction, do not adequately address real-world AI complexities, and we cite the OECD AI Principles as a current globally-recognized framework for AI ethics and safety. This direct contrast maintains academic rigor and shows that our approach is grounded in contemporary thinking.

• It is important to improve the graphical quality of all figures, as they present issues with contrast and resolution that hinder proper interpretation.

Response: We have improved all figures for clarity. The figures have been replaced or edited to ensure higher resolution and better contrast. Colors and text in the figures were adjusted so that all details (labels, arrows, etc.) are clearly visible. These changes will make the figures easier to read and interpret.

 

• Regarding Table 1, it is recommended to introduce a brief explanation beforehand, clarifying its relevance for the reader.

Response: We have added a short introductory sentence before Table 1 to explain what the table represents and why it is relevant. This sentence guides the reader on how to read Table 1 and ties it into the discussion. It emphasizes that Table 1 is comparing superficial versus deeply embedded “fear” in AI, illustrating the differences.

3. Rationale for Embedding Fear

• Although it is stated that there is no direct evidence, it is advisable to link the proposal to related research on risk-aware AI and human-in-the-loop AI, where safety improvements have been demonstrated.

Response: We have revised Section 3 (Rationale for Embedding Fear) to connect our idea to existing work on risk-aware and human-in-the-loop AI systems. We acknowledge that while our specific “fear module” has not been tested, there are studies in which adding risk-sensitive or human oversight elements improved AI safety. For example, we now cite a recent case study of an AI for sepsis management that underscores the importance of safety assurances in AI-driven decisions. We also mention research on AI in military contexts (Rowe 2022 on ethical AI in military applications) to show parallels. By including these, we position our work in the context of known strategies that enhance safety, implying that an embedded fear mechanism is conceptually aligned with those successes. The phrase “no direct evidence” is now tempered by adding supporting citations and context as above.

 

 

• It is suggested that absolute claims such as "there is no direct evidence" be avoided and that the statement be qualified by supporting it with relevant indirect literature.

Response: As stated in previous remark, we concur and have adjusted the wording in the Rationale section to avoid an absolute tone. *As described above, the claim about “no direct evidence” is now qualified: we explicitly state that, while no prior study has specifically implemented a fear module in AI, there is relevant indirect evidence in the form of related risk-sensitive AI research. We cite that literature to support our rationale rather than leaving it as an unsupported claim. This change makes our argument more nuanced and evidence-based.

4. Proposed Mechanism

• It is recommended to explicitly detail how penalties and risk thresholds would be integrated into real medical pipelines (e.g., imaging diagnosis workflows and clinical decision support systems).

Response: We have expanded the description of our proposed mechanism to clarify how it could be implemented in actual medical workflows. We now provide concrete examples of integration: for instance, we describe how in a medical imaging diagnosis pipeline, the fear module would function by flagging uncertain diagnostic outputs (like an AI detecting a tumor with low confidence) for human radiologist review before finalizing a report. Similarly, for a clinical decision support system, we explain that the module would monitor treatment recommendations and if a recommendation carries a risk above a certain threshold, it would require a human clinician’s approval. These examples demonstrate how the Bayesian risk thresholds and penalty-driven learning components plug into typical healthcare AI processes.

• Although Figures 2 and 3 are pertinent, their understanding could be enhanced by adding brief real or fictional clinical examples alongside the illustrations.

Response: We have added short illustrative examples in the text accompanying Figures 2 and 3 to help readers understand the scenarios depicted. For Figure 2 (which outlines the AI agent’s fear mechanism architecture), we now walk through a fictional neurosurgical case in the text, describing how each component (risk assessment, uncertainty modeling, etc.) would behave during an intracranial aneurysm evaluation. For Figure 3, we include another concrete example (for instance, a scenario in internal medicine or an ICU decision) to demonstrate the concept shown in that figure. These examples are clearly marked in the text and correspond to the figures, thereby clarifying the figures’ content through narrative.

• Expanding the discussion to include technical limitations, such as latency issues in critical medical operations or computational power constraints, is advisable.

Response: We acknowledge the importance of technical limitations. We have added a dedicated brief discussion about the practical limits of implementing a fear module. Specifically, we mention that latency could be introduced when the AI defers to human oversight (which could be problematic in split-second decisions like emergency interventions). We also note potential computational overhead, since continuously running risk estimations and uncertainty modeling might strain resources or slow down response times, especially in real-time systems. By highlighting these, we inform the reader that our framework, while beneficial, must be engineered carefully to mitigate such issues (for example, by optimizing algorithms or pre-defining certain emergency override rules).

 

5. Implementation Approaches

• Suggesting specific algorithms that could implement the "fear module," such as Proximal Policy Optimization (PPO) in reinforcement learning or Bayesian Deep Learning for uncertainty modeling, would be beneficial.

Response: We have incorporated specific algorithmic suggestions into the Implementation section to make our framework more concrete. In particular, we now mention that the reinforcement learning component of the fear module could be implemented using algorithms like Proximal Policy Optimization (PPO) or other safe-RL techniques, which are well-suited for balancing reward with penalty constraints. For the uncertainty estimation part, we note that Bayesian deep learning methods (e.g., Bayesian neural networks or Monte Carlo dropout) could serve to quantify uncertainty in the model’s predictions. By naming these, we provide a clearer blueprint of how one might technically realize the fear mechanism within an AI agent.

 

• It is recommended to explicitly discuss the trade-offs between a more conservative AI system (more excellent safety, reduced agility) and a more proactive one (higher efficiency, increased risk).

Response: We have added a discussion of the inherent trade-offs in tuning the “fear” level of an AI. The revision now explicitly contrasts a highly cautious (conservative) AI versus a highly proactive AI. We explain that a very conservative AI (with a low risk threshold for fear activation) will catch more potential issues and be safer, but might also over-alert and slow down decision-making or decline interventions that could help. Conversely, a proactive, risk-tolerant AI might act quickly and efficiently, but with a higher chance of errors or adverse events. We stress that finding the right balance is crucial and likely context-dependent (some settings may prioritize safety over speed, or vice versa). This new text helps frame our fear module as adjustable on a spectrum, not one-size-fits-all.

 

6. Counterarguments and Ethical Considerations

• Currently, counterarguments appear somewhat mixed. It is recommended to structure them into clearly separated subsections, such as:
  • Risk of Overcautiousness
  • Risk of Adversarial Manipulation
  • Responsibility Dilemmas

Response: We have considered the complete reorganization of the “Counterarguments and Ethical Considerations” section. We have created three distinct subsections with the suggested headings: (1) Risk of Over-Cautiousness, (2) Risk of Adversarial Manipulation, and (3) Responsibility Dilemmas, albeit the texts were not proportional and we did not consider it better after the edits, so we have rolled back the text reorganizations and just kept your proposed subchapter titles for the second and third subsections.  Currently the readers can easily see our acknowledgments of each challenge and our responses or mitigations for them. We have reorganized the texts in these sections.

• Adding brief, concrete examples to better illustrate the ethical risks discussed would be valuable.

Response: In each of the newly separated subsections (mentioned above), we have included a short example scenario to illustrate the risk. For Risk of Overcautiousness, we added an example of an AI that, due to an overly sensitive fear setting, delays an urgent treatment (and explain the potential harm of that delay). For Risk of Adversarial Manipulation, we gave an example of how a malicious input could trigger the AI’s fear response inappropriately (for instance, an attacker feeding false data to make the AI constantly alarm and shut down critical functions). For Responsibility Dilemmas, we described a hypothetical situation where an AI’s caution prevented an action that might have saved a patient, raising the question of whether the blame lies with the AI or the clinicians who deferred to it. These concrete vignettes help the reader grasp the real-world implications of each ethical concern.

7. Broader Implications and Future Directions

• Although applications in autonomous vehicles and disaster response are discussed, it is suggested that the scope be expanded by considering AI systems in judicial settings, where a "fear of injustice" could play a crucial regulatory role.

Response: We have expanded our “Broader Implications and Future Directions” section to include a discussion of how similar principles might apply in the judicial domain. We introduce the idea of an AI judge or legal decision support system endowed with a “fear of injustice” – effectively, a mechanism to avoid unjust or biased outcomes. We explain that such an AI might, for example, hesitate or flag decisions that could disproportionately harm a defendant’s rights. By adding this, we show that our framework of embedding a risk-averse module has relevance beyond medicine, in any high-stakes decision system (like law) where caution is equally paramount. This complements our earlier mentions of autonomous vehicles and disaster response, broadening the interdisciplinary reach of the concept.

• It would be ideal to conclude this section by proposing open research questions to guide future interdisciplinary investigations.

Response: We have concluded the “Future Directions” section with a set of explicit open research questions. These questions highlight areas that require further study, such as: How can we quantitatively determine the optimal level of “fear” in different AI contexts? What human factors need to be considered when integrating such a module? How can regulatory frameworks adapt to certify AI with internal safety mechanisms? By listing these, we not only address your point but also engage the broader research community, pointing to next steps beyond our current work.

8. Conclusions and Vision

• In conclusion, it is recommended to highlight this proposal's distinctive contribution compared to traditional "safe AI" approaches, such as classical AI alignment.

Response: We have revised the Conclusions section to explicitly state how our framework differs from and adds to traditional AI safety paradigms (like standard AI alignment strategies). We emphasize that, unlike generic alignment or fail-safe methods, our proposal introduces a specific, biologically-inspired mechanism (a fear module) that proactively biases the AI toward caution in real time. We clarify that this is a novel contribution: it’s not just aligning AI with goals in the abstract, but embedding a dynamic constraint that mirrors a well-understood human survival instinct. By doing so, we underscore the unique value of our approach and how it complements or goes beyond existing safe AI approaches.

 

• It would be advisable to include an explicit call for interdisciplinary collaboration, emphasizing the need for joint efforts among technologists, healthcare professionals, ethicists, and policymakers.

Response: This is present in the manuscript as chapter 8.2 “. Call to Action” we have edited it to a more a clear call-to-action. The revised conclusion now explicitly invites collaboration: we mention computer scientists, clinicians, neuroscientists, ethicists, and regulators/policymakers as stakeholders who must work together. We also highlight that pilot projects and policy frameworks should be developed in tandem. This addition underscores that our proposal is not just a theoretical exercise but a direction that demands broad, interdisciplinary engagement to implement safely and effectively.

• To avoid potential misunderstandings, it is necessary to consistently remind the reader that the term "fear-based AI" is a computational metaphor.
• Response: It is already present in the text repetitiously. We have taken care throughout the revised manuscript to reinforce that “fear” in our context is metaphorical and functional, not literal emotion. In several key places, we inserted reminders or parenthetical clarifications that the AI does not feel fear but rather simulates the effect of fear (heightened caution). For example, in the Introduction and in the Conclusion, we added phrasing like “(used here as an analogy for a risk-awareness module, not a true emotion).” We also ensured that whenever we use anthropomorphic language for style, we immediately clarify it. These consistent reminders will prevent readers from misinterpreting our intent.

General Comment

The article is innovative, well-argued, and deeply interdisciplinary. However, its contribution could be further strengthened by:

• Consolidating ideas to avoid repetitions.
• Incorporating empirical examples or recent references to reinforce key arguments.
• Making the technical architecture more tangible by including real-world applications and specific implementation details.

Submission Date 11 April 2025

Date of this review 20 Apr 2025 00:15:17

Response: We appreciate the reviewer’s positive assessment of our work’s innovation and interdisciplinarity. In line with the suggestions to further strengthen the paper, we have implemented several overarching improvements: (1) Consolidation of ideas – we went through the manuscript and removed or merged repetitive passages (especially in the Introduction and background sections) to improve flow and avoid diluting key points. (2) Empirical examples and recent references – we added concrete examples (hypothetical case studies in medicine, as well as referencing real incidents and up-to-date research literature from 2019–2024) to support and illustrate our arguments, as detailed in responses above. These inclusions make our points less abstract and show real precedents or parallels. (3) Tangible technical details – we enriched the description of our proposed architecture with specifics such as algorithm names (PPO, Bayesian networks) and formulae (the utility threshold equation), and described how the system would operate in actual medical scenarios. Collectively, these changes make the manuscript more concise, evidence-backed, and practically informative. We believe these revisions have enhanced the overall quality and impact of the paper.

Thank you for your time and effort to improve this manuscript.

authors

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Please see the attached file.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

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

Author Response

Author's Reply to the Review Report (Reviewer 2)   

Quality of English Language

(x) The English could be improved to more clearly express the research.
	Yes	Can be improved	Must be improved	Not applicable
Does the introduction provide sufficient background and include all relevant references?	( )	(x)	( )	( )
Is the research design appropriate?	( )	(x)	( )	( )
Are the methods adequately described?	( )	( )	(x)	( )
Are the results clearly presented?	( )	( )	(x)	( )
Are the conclusions supported by the results?	( )	(x)	( )	( )

Comments and Suggestions for Authors

Please see the attached file. peer-review-46177059.v2.pdf

AI

Embedding Fear in Medical AI: A Risk-Averse Framework for  Safety and Ethics  Andrej Thurzo and Vladimír Thurzo 

The article examines a poorly researched approach in medical informatics – the use of “fear” as a functional module to enhance safety in autonomous decisions in medical AI systems. The authors build a compelling analogy between the human amygdala mechanism and a proposal for an “amygdala-like” subsystem in AI that would act as a constant observer and harm avoidance module. The article is distinguished by an interdisciplinary approach (medicine, neuroscience, engineering and ethics).

The proposal for a fear-inspired module as part of medical AI agents is innovative. The use of reinforcement learning, Bayesian thresholds and uncertainty modeling in combination with ethical principles makes it scientifically sound. The idea that “fear” is not an emotion but an engineering function for avoiding harm is original and logically justified.

We thank Reviewer 2 for the positive evaluation of our work’s innovation and the detailed recommendations provided. We have addressed each point raised, resulting in a more readable, substantiated, and rigorous manuscript. Our responses and the corresponding revisions are outlined below.

 

The reviewer has the following recommendations, comments and questions:

1. Some parts of the article are overloaded with terms and references (especially in section 2.6), which may make it difficult to understand for non-specialist readers.

Response: We have revised Section 2.6 thoroughly to improve its readability. Technical terms that appeared in rapid succession have been either explained in simpler words or trimmed if they were not essential. We also reduced the clustering of references by spreading them out and citing only the most relevant ones to support each point, instead of listing many at once. Additionally, we introduced a bit more explanatory context for concepts in Section 2.6 so that a reader without a deep background in AI can follow along. As a result, Section 2.6 is now more approachable and less jargon-heavy, addressing the reviewer’s concern.

2. More empirical or simulation data could be included that illustrate how this “fear module” would behave in real clinical scenarios.

Response: We acknowledge that our original submission was conceptual without empirical validation. While conducting a full experiment was beyond the scope of our current work, we have taken steps to address this concern: we added a detailed hypothetical scenario and a pseudo-simulation walkthrough to illustrate how the fear module might function. Essentially, we present a step-by-step narrative of the module’s behavior in a simulated clinical decision (this is akin to a thought experiment or in-text simulation). For example, we walk through how the module would react as an AI monitors a patient and encounters rising risk levels, showing triggers and outputs at each stage (this is supported by the formula we provided for the utility U). Additionally, we have explicitly noted in the manuscript that this framework is a proposal that requires future simulation and validation. We have included a statement in the Discussion/Future Work emphasizing the need for and our plan for empirical testing (such as running a prototype on retrospective medical data or creating a simplified simulation environment to gather preliminary results). These additions do not provide new data per se, but they do give the reader a much clearer idea of how the system would work in practice, and they demonstrate our awareness of the importance of moving from theory to practice.

3. It would be good to consider a variant of the algorithm with the possibility of contextual adaptation of fear, e.g. by case severity or by diagnosis specificity.

Response: This is an excellent suggestion, and we have incorporated it. We now discuss the idea that the “fear threshold” or sensitivity of the module could be dynamic and context-dependent. Specifically, the manuscript explains that in more severe cases or high-risk contexts, the AI might adjust its parameters to be either more or less cautious. For example, for a critical, time-sensitive scenario (like a trauma patient in ER), the fear module might use a higher threshold (so it doesn’t trigger too readily and slow down necessary action), whereas in an elective or lower-stakes scenario, it might use a lower threshold to err on the side of caution. We also mention adjusting by diagnosis specificity – for instance, an AI might be more cautious (fearful) when dealing with pediatric patients or rare conditions where uncertainty is inherently high. By adding these points, we show that our approach is flexible and can be tuned to the context, which addresses the reviewer’s idea of a variant algorithm. Text was added in lines 722-730.

4. Empirical data are lacking. Response: Explained in point 2.
5. The concept of the “fear module” is entirely theoretical. No experimental models, simulations or scenarios in which the system has been tested are presented.

Response: Explained in point 2.

6. It is good to demonstrate the proposed method through a simulation experiment or proof-ofconcept.

Response: Yes, Explained in point 2.

7. Too broad definition of “fear”. The term is used metaphorically, but there is no clear distinction between “fear” as an adaptive threshold and “fear” as a cognitive process. Will this not lead to ambiguous interpretation in the implementation of such a model?

Response: We appreciate this point and have worked to clarify our terminology. We have explicitly defined what we mean by “fear” in the AI context to avoid ambiguity: in the revision, we state that “fear” refers to an adaptive threshold mechanism for risk aversion (an engineering feature), not a cognitive or emotional experience. We make it clear that the AI’s “fear” is effectively a computation that, when certain risk metrics are exceeded, triggers a particular response (like halting an action or seeking oversight). We distinguish this from any anthropomorphic notion of fear as felt by humans or animals. By tightening this definition and repeating it where necessary (as also done per Reviewer 1’s *** Comment  on metaphor), we eliminate potential confusion about how to interpret “fear” in implementation. Essentially, we instruct the reader (and implicitly any implementer) to treat “fear = precaution module output above threshold,” nothing mystical.

8. There is a risk of the system being overly cautious. The risk of too high “fear thresholds” that can lead to excessive inertia or inadequate reaction in critical situations is not considered in depth.

Response: This is a crucial point, and we have addressed it in two ways. First, we expanded our Counterarguments section (as also noted for Reviewer 1) to include a dedicated discussion on the Risk of Overcautiousness, where we delve into the possibility that if the fear module is too sensitive, the AI might hesitate or stop when it actually should act. We discuss the potential negative outcomes of that (such as missing a window for treatment). Second, we propose mitigation strategies: for example, we now explicitly mention implementing an upper bound on the fear module’s influence – meaning the AI can warn and request human input, but it shouldn’t be allowed to completely block necessary actions without human override. We gave an example (neurosurgical scenario) demonstrating that even if the AI is very “scared” of a high-risk surgery, it would issue strong warnings rather than outright forbidding the procedure; the final call rests with the human surgeon to proceed if they judge it necessary. Additionally, as discussed above, we consider adjusting thresholds for critical scenarios to avoid paralyzing the system when quick action is required. These additions show that we have thought about and provided answers to the issue of excessive caution.

9. A strategy for balancing safety and efficiency can be considered.

Response: This Comment  closely relates to the previous two (over-cautiousness and contextual adaptation), and we have addressed it through those revisions. To be explicit: the strategy to balance safety and efficiency in our system is through careful calibration of the fear module’s thresholds and influence. We have made sure the manuscript states that one must tune the system – possibly scenario by scenario – to achieve an acceptable balance. For clarity, we also highlight that this balance might be different in different medical contexts (emergency vs non-emergency, etc.). This point is now covered in our added discussion on trade-offs (see Reviewer 1’s trade-off response) and in the counterargument about overcaution. We present the “upper bound on fear influence” and “context-dependent threshold” as concrete strategies to ensure safety measures do not excessively compromise efficiency.

10. The algorithmic implementation is not clearly presented. Although reinforcement learning and Bayesian models are mentioned, no description or pseudocode is given to illustrate the operation of the “fear module”.

Response: We have bolstered the manuscript with a clearer algorithmic description of the fear module. While we did not provide full pseudocode, we introduced a mathematical representation that serves a similar explanatory purpose. Specifically, we added an equation defining a utility or risk metric (U = w1 * R – w2 * B – w3 * UQ) that combines Risk (R), Benefit (B), and Uncertainty (UQ) with weighting factors, and we explain that the fear module triggers when this composite measure falls below a certain threshold. This effectively shows how the module would compute and decide to escalate a decision. We walk the reader through each term of the equation and what it represents in practice (e.g., R = risk probability, B = expected benefit, etc.), which is essentially a pseudocode logic: “if (risk high AND benefit not overwhelmingly high AND uncertainty high) then trigger fear”. Additionally, we enumerated the steps the system takes in the integrated scenario (risk calc -> penalty learning -> check threshold -> if threshold crossed, call human). These steps illustrate the algorithm in words. We believe these additions satisfy the reviewer’s request by making the inner workings of the module much more transparent.

11. Insufficient discussion of interaction with human oversight. It is unclear how human operators will react or interact with systems that stop or change their behavior based on “fear.”

Response: We have expanded our discussion on the role of human oversight and how the system interfaces with human decision-makers. Specifically, we clarify that when the AI’s fear module triggers, it communicates a clear warning or explanation to the human operator (doctor, nurse, etc.). We describe what form this might take (e.g., an alert on the interface indicating “High risk detected – action paused for review”). We also discuss expected human response: the human can either override the AI’s caution or follow its recommendation for caution. In the revision, we stress that the workflow should be designed so that the human is prepared for these interventions – for example, by having protocols in place for when the AI defers a decision. Additionally, we mention that user training or calibration could be needed so that the clinicians know how to interpret and trust the AI’s fear signals (this ties in with interpretability and trust as well). By adding these points, we make it clear how the AI and human collaborate in real time, especially in scenarios where the AI “feels fear” and changes its behavior (stopping or asking for help).

12. A section on interpretability and user trust could be added.

Response: We have added content focusing on interpretability of the fear module and how that affects user (clinician) trust. While we did not create an entirely new standalone section, we integrated this discussion into our Ethical Considerations and Future Directions. We note that for users to trust the system, it’s important they understand why the AI is issuing a warning or behaving cautiously. Thus, we propose that the AI should provide explanations (e.g., “Action paused due to 7% predicted risk of severe bleeding with high uncertainty in data”). We also cite or mention concepts of explainable AI and the need for user training. This addition underscores that beyond technical functionality, the fear mechanism must be transparent enough to be accepted by medical staff and patients. In short, we explicitly address interpretability as a requirement for the success of our framework and mention ongoing work in XAI (explainable AI) in healthcare that aligns with this need.

13. More up-to-date clinical sources would enrich realism.

Response: We have added several recent clinical references to ground our discussion in current reality. For example, we cite a 2022 BMJ Health Informatics paper on assuring safety of an AI clinician (for sepsis), a 2024 BMC Medical Ethics study on integrating ethics in AI development, and a 2024 JMIR paper on human-AI teaming in critical care. These references are all from 2022–2024, thus very up-to-date. We use them to illustrate points such as the importance of safety in clinical AI (the sepsis AI case study), current thinking on AI ethics integration, and perspectives on how clinicians view AI. Additionally, we mention recent technological developments (like the use of CNN+LSTM for EEG classification from Abooelzahab et al. 2023) to show practical examples of AI learning from past mistakes. By incorporating these contemporary sources, we increase the realism and relevance of our arguments, showing that we are building on the latest knowledge and concerns in medical AI.

14. Line 164 - fix the citation of references.

Response: We have corrected the formatting of all reference citations in the manuscript. The specific issue at original line 164 likely referred to a formatting error (such as a reference number not appearing correctly or being merged with text). We reviewed that line and others for any citation problems. All citations now follow the journal guidelines: they are in square brackets, numbered, and placed after punctuation as required. Any stray characters or mis-numbered references have been fixed. For instance, where previously a citation may have been shown incorrectly (like “[12,13]” being out of place), it is now properly placed and formatted. We also updated the reference order to ensure consistency with the numbering.

15. Fix the punctuation (e.g. line 333).

Response: We have combed through the manuscript and corrected punctuation issues. We have fixed errors such as missing spaces after periods, double periods, or commas outside quotation marks, etc.

16. In places there is unnecessary underlining of the text. Correct the size of the text, which is unnecessarily large in places.

Response: We have fixed all formatting anomalies. Any unintended underlined text (perhaps leftover from hyperlinks or editing markup in the Word document) has been removed or properly formatted (e.g., as regular or italic text if needed). We also normalized the font size throughout; any sections that had larger text (possibly headings or an inconsistency) have been adjusted to match the journal template or standard font size. Now the manuscript should have a consistent appearance with no odd underlines or font jumps.

17. Correct the citation of references according to the requirements of the journal.

Response: We have reviewed the reference and citation style and ensured full compliance with the journal’s guidelines. This included checking that references in the text are numbered in order of appearance, that bracket formatting and punctuation are correct (as addressed above), and adjusting the reference list format if needed (order, punctuation, etc., per the journal’s style). For example, if the journal requires abbreviated journal names and proper doi formatting, we have applied those. All references have been updated to match the required style (e.g., ensuring all author names, titles, etc., are in the correct format, removing any residual hyperlink underlining, adding missing information like publisher or page numbers where needed). The citations in the text now correspond exactly to entries in the reference list, sequentially and without stylistic errors.

Comments on the Quality of English Language

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

Response: – we have improved the English throughout. We reiterate here that the manuscript’s language is now clearer and more polished, as per the reviewer’s suggestion.)

Submission Date 11 April 2025

Date of this review 23 Apr 2025 13:39:12

 

Thank you for your time and effort to improve this manuscript.

authors

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

• How can we quantify and calibrate “caution” in computational systems without compromising their efficiency or responsiveness in critical scenarios like emergency surgery or battlefield decision-making?

• In what ways might the proposed “internal caution system” balance automated decision-making with real-time human oversight, especially when time-sensitive judgments are required?

• What are the ethical implications of embedding a pseudo-instinctive module in AI systems—especially when these systems might override or delay actions based on perceived risks?

• How can we ensure that the Bayesian and reinforcement learning components of the framework remain adaptable to new data, environments, and threats without overfitting to rare or outdated caution signals?

• Add a paragraph about the possibility of mounting security attacks to your work include publications: "Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare", "Energy-Efficient Long-term Continuous Personal Health Monitoring", "Artificial intelligence security: Threats and countermeasures", "AI-empowered IoT security for smart cities" 
  

• What roles should regulators and interdisciplinary panels play in evaluating and certifying these “cautious” AI systems before they are deployed in high-risk sectors like healthcare or defense?

Author Response

Author's Reply to the Review Report (Reviewer 3)   

Quality of English Language

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

	Yes	Can be improved	Must be improved			Not applicable
Does the introduction provide sufficient background and include all relevant references?	( )	(x)		( )	( )
Is the research design appropriate?	( )	(x)		( )	( )
Are the methods adequately described?	( )	(x)		( )	( )
Are the results clearly presented?	( )	(x)		( )	( )
Are the conclusions supported by the results?	( )	(x)		( )	( )

 

We thank Reviewer 3 for thought-provoking questions and suggestions. The questions have prompted us to clarify several important aspects of our work. We have addressed each point by adding explanations and new content to the manuscript, ensuring that the concerns about quantification, oversight, ethics, adaptability, security, and regulation are thoroughly covered. Our point-by-point responses are below.

 

Comments and Suggestions for Authors

• How can we quantify and calibrate “caution” in computational systems without compromising their efficiency or responsiveness in critical scenarios like emergency surgery or battlefield decision-making?

Response: This question raises the key issue of tuning the “caution” level. In response, we have elaborated in the manuscript how we envision quantifying and calibrating the fear module’s trigger conditions. We explain that the caution can be quantified in terms of a risk threshold (e.g., a percentage probability of harm) and an uncertainty level. Calibrating it involves choosing these thresholds (and the weights in our utility function) based on the context. We added that one can empirically adjust these parameters by analyzing false-positive vs. false-negative rates of the module in simulations or trials. Importantly, we note that in critical scenarios (like emergency surgery), the system should use a higher threshold for intervention to avoid unnecessary slowdowns – essentially, making the AI less sensitive (only the most extreme risks would trigger a pause). Conversely, in routine scenarios, thresholds can be lower (more sensitive to risk). We believe this dynamic calibration ensures we do not compromise efficiency when time is of the essence. This explanation has been added to the text, directly addressing how caution is measured and tuned.

• In what ways might the proposed “internal caution system” balance automated decision-making with real-time human oversight, especially when time-sensitive judgments are required?

Response: We have expanded on how the AI and human oversight work together, particularly under time pressure. The manuscript now clarifies that the system is designed so that human oversight is invoked only when needed – i.e., the AI will handle routine decisions autonomously, but in edge cases where it “feels” significant risk, it will call in the human. We emphasize that in time-sensitive judgments, the system should not simply wait idly for human input if that would cause harm due to delay. Instead, we propose a couple of approaches: (a) The AI could continue to monitor or take safe interim actions while alerting the human (for example, it might stabilize a patient’s vitals in an emergency while waiting a few minutes for a surgeon’s decision on a high-risk procedure, rather than doing nothing). (b) We suggest that for true split-second situations (where even a short delay is unacceptable), the threshold for invoking human help would be set so high that the AI is effectively trusted to act (because any scenario that crosses that threshold likely cannot be saved anyway without human presence – this ties back to calibration). We included these considerations to show how oversight and autonomy are balanced. Additionally, we stress the importance of communication: the AI provides clear information to the human so the transition is seamless. Overall, the revision assures that real-time collaboration is accounted for, with minimal disruption to urgent decision-making.

• What are the ethical implications of embedding a pseudo-instinctive module in AI systems—especially when these systems might override or delay actions based on perceived risks?

Response: We have addressed these ethical implications in our expanded Ethical Considerations section (specifically under “Responsibility Dilemmas” and “Overcautiousness”). We discuss that if an AI can delay or override actions (even if only by prompting a human), this raises questions of accountability and patient autonomy. For example, we note the scenario where an AI’s caution might conflict with a patient’s or doctor’s willingness to take a risk – ethically, who decides? We answer that ultimately the human should decide, and the AI’s role is advisory. We also mention that if an AI did override an action (say in a future scenario of more autonomous systems), it would blur responsibility lines, which we argue should be avoided by design (hence our insistence on human final authority). Furthermore, we point out potential legal implications: an AI delaying care could be seen as malpractice if it were wrong – so we emphasize governance and clear protocols, as well as the importance of the AI’s accuracy to justify any delays it causes. These ethical discussions are now clearly laid out, acknowledging both the benefit (preventing harm) and the risk (impeding needed treatment or complicating consent and responsibility).

• How can we ensure that the Bayesian and reinforcement learning components of the framework remain adaptable to new data, environments, and threats without overfitting to rare or outdated caution signals?

Response: We have added a discussion about the adaptability and continuous learning aspects of our framework. In the revised manuscript, we explain that to avoid overfitting to rare events (e.g., one freak accident causing the AI to be forever too fearful), the system should include mechanisms for periodic retraining and updating its parameters with new data. We suggest using techniques like online learning where the model gradually incorporates new examples so it doesn’t overweight old experiences. For the Bayesian component, ensuring it uses priors that can be updated and perhaps decays old evidence over time is mentioned. For the RL component, we note that one can include exploration or at least not lock the policy – meaning the AI can adjust its penalty values if it notices that certain feared outcomes haven’t occurred in a long time (indicating maybe it was too cautious). We also mention concept drift: if the medical environment changes (new treatments, different patient demographics), the fear module should be re-calibrated accordingly. These points ensure the reviewer’s concern is met: the system is not static or brittle; it’s designed to evolve with new information.

• Add a paragraph about the possibility of mounting security attacks to your work include publications: "Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare", "Energy-Efficient Long-term Continuous Personal Health Monitoring", "Artificial intelligence security: Threats and countermeasures", "AI-empowered IoT security for smart cities" 

Response: We have added a new paragraph (in the Ethical/Counterargument section, under Adversarial Manipulation) explicitly discussing security vulnerabilities. We acknowledge that an adversary could attempt to exploit the fear module – for instance, by feeding it false inputs (data poisoning or adversarial examples) to either trigger constant fear (causing the AI to shut down useful functions) or suppress fear (tricking it into not noticing a real risk). We reference known work on attacks in healthcare AI and AI security (including the suggested publications, appropriately cited in the revised reference list) to underline that these are real concerns. We also mention potential defenses: for example, robust training methods, anomaly detection for input data, and redundancy/cross-checks (the AI could cross-verify critical signals with multiple sources to avoid being fooled by one tampered sensor). By adding this, we cover the security aspect thoroughly. The references the reviewer gave have been cited to show we’ve considered prior research on such attacks and mitigations.

• What roles should regulators and interdisciplinary panels play in evaluating and certifying these “cautious” AI systems before they are deployed in high-risk sectors like healthcare or defense?

Response: We have added a discussion in our Broader Implications/Future Directions about the role of regulatory bodies and interdisciplinary oversight in the deployment of such AI. We note that because this is a safety-critical system, it would likely need regulatory approval (for example, in healthcare, an FDA-like approval process for the AI as a medical device). We mention that regulators should develop specific evaluation criteria for these systems, such as tests to ensure the fear module triggers appropriately and does not have unintended side-effects. We also suggest that interdisciplinary panels (including ethicists, clinicians, AI experts, and even patient representatives) should be involved in certifying and monitoring the AI. These panels can help create guidelines for acceptable behavior of the AI (when should it defer to humans, how to document incidents, etc.). The revised text emphasizes that deployment isn’t just a technical decision but a policy and ethics one, and calls for standards and possibly new regulatory frameworks tailored to AI with autonomous safety mechanisms. We have thus addressed the reviewer’s query by outlining how such oversight might look and why it’s essential.

 

Quality of English: “The English could be improved to more clearly express the research.”

Response: As with the other reviewers, we have undertaken a thorough language revision to improve clarity. We have addressed any ambiguous or confusing phrasing that might have hindered understanding, ensuring that the answers to these complex questions (quantification, oversight, etc.) are presented in a straightforward manner. The overall readability and professionalism of the manuscript have been enhanced as a result.

Submission Date           11 April 2025
Date of this review        22 Apr 2025 04:02:51

 

 

Thank you for your time and effort to improve this manuscript.

authors

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

We appreciate that the paper brings to the attention of the academic community and practitioners an essential topic related to the use of AI in the medical field, highlighting its ethical challenges and concerns. Through an innovative approach, the authors propose a conceptual framework for integrating a “fear instinct” into AI systems used in healthcare.
We recommend that the authors address the following issues:
- outline the structure of the paper at the end of the Introduction section (mention the main parts of the paper).
- clearly state the research questions;
- mention only the title below each figure, but provide further details in the text (see Figure 2, Figure 5);
- indicate the source below each figure and table, even in the case of the authors’ elaboration.
- cite the references in the text according to the journal’s citation guidelines (check the recommended style);
- correct typing errors (e.g., add a space before/after square brackets, as in “[73]a” on line 594, “[75]d” on line 605, “adaptive(72)” on line 622, and “. [96]further” on line 767);
- highlight the research limitations.

Author Response

Author's Reply to the Review Report (Reviewer 4)   

Quality of English Language

(x) The English is fine and does not require any improvement.
	Yes	Can be improved	Must be improved	Not applicable
Does the introduction provide sufficient background and include all relevant references?	( )	(x)	( )	( )
Is the research design appropriate?	( )	(x)	( )	( )
Are the methods adequately described?	(x)	( )	( )	( )
Are the results clearly presented?	(x)	( )	( )	( )
Are the conclusions supported by the results?	(x)	( )	( )	( )

Comments and Suggestions for Authors

We appreciate that the paper brings to the attention of the academic community and practitioners an essential topic related to the use of AI in the medical field, highlighting its ethical challenges and concerns. Through an innovative approach, the authors propose a conceptual framework for integrating a “fear instinct” into AI systems used in healthcare.

We thank Reviewer 4 for the supportive Comments and helpful suggestions aimed at improving the structure and formatting of our manuscript. We have made all the requested changes, as detailed below, to ensure the paper’s organization and presentation meet the journal’s standards and clearly communicate our work.

We recommend that the authors address the following issues:
- outline the structure of the paper at the end of the Introduction section (mention the main parts of the paper).

Response: We have added a brief roadmap at the end of the Introduction to summarize the structure of the paper. This new passage informs the reader of the content and purpose of each of the upcoming sections. By including this outline, we make it easier for readers to follow the flow of our argument throughout the paper.

- clearly state the research questions;

Response: We have explicitly stated the research questions that our work addresses. In the Introduction (following the motivation), we added a concise formulation of the primary research questions driving this study. These questions make it clear to the reader what issues we are investigating (for example: “Can an AI be endowed with a fear-like mechanism to improve safety?” and “How would such a mechanism be implemented and evaluated in practice?”). Stating these questions upfront strengthens the focus of the paper and aligns with the your’s recommendation.

- mention only the title below each figure, but provide further details in the text (see Figure 2, Figure 5);

Response: We have modified the figure captions to comply with this request. Now, each figure’s caption is limited to a brief descriptive title or one simple sentence describing the figure at a high level. Any detailed explanation that was previously in the caption has been moved into the main text of the paper. For example, for Figure 2 and Figure 5, we shortened the captions to a single line (e.g., “Figure 2. Architecture of the proposed ‘fear’ module in a medical AI system.”) and we made sure that the text of the Results/Implementation section walks the reader through the figure’s details (as we described in responses above for adding examples and explanations). This way, the figures have clean, uncluttered captions, and the necessary elaboration is available in the body text.

- indicate the source below each figure and table, even in the case of the authors’ elaboration.

Response: We have added source attributions for all figures and tables. For figures and tables that are original (created by us), we have noted “Source: Authors’ own elaboration.” below the caption. If any figure was adapted from another source or data, we have provided the appropriate reference citation as the source. This addition meets the journal’s requirement and ensures proper credit and clarity on figure/table provenance.

- cite the references in the text according to the journal’s citation guidelines (check the recommended style);

Response: We are well aware of the citation style guidelines of the journal AI as we are many-year collaborators and reviewers. We have reviewed them again and ensured our in-text citations follow them exactly. Our references are now cited by number in square brackets in the proper order, and we’ve removed any instances of citations not matching the required format. We also checked that multiple citations are separated by commas and listed in increasing order within one set of brackets, as typically required. Essentially, the manuscript’s citation style has been standardized. For example, if the guideline is to use [1–3] format for consecutive references, we have done so; if the guideline is one reference per bracket repeated, we would do that (but most likely it’s the numeric in one bracket style which we have implemented). This ensures compliance with the journal’s requirements.

- correct typing errors (e.g., add a space before/after square brackets, as in “[73]a” on line 594, “[75]d” on line 605, “adaptive(72)” on line 622, and “. [96] further” on line 767);

Response: We have fixed all the typographical errors noted. Specifically: we added missing spaces around reference citations where needed (for example, what was “[73]a” is now “[73] a”, separating the reference [73] from the following letter). The instance of “[75]d” similarly has been corrected to “[75] d” or rephrased so that the citation stands apart from the word. The “adaptive(72)” case was corrected by inserting a space: it now reads “adaptive [72]” (assuming [72] was a reference, we placed it in brackets with a space before it instead of within the parentheses). The case “. [96] further” likely needed a capital letter after the citation or adjustment; we have corrected it to “. [96] Further” by moving the period before the reference or rewording the sentence so that it doesn’t start oddly. Additionally, we searched for and fixed any similar minor errors throughout the document (like double periods, missing spaces after commas, etc.). These corrections ensure that the text is clean and professionally formatted.

- highlight the research limitations.

Response: We have added a clear statement of the limitations of our research. In the Discussion (or a new short section titled “Limitations”), we explicitly mention the main limitations: for example, the conceptual nature of the work (lack of experimental validation thus far), potential challenges in generalizing the approach, and any assumptions we made (like assuming accurate risk estimation is possible). We also tie this into future work, noting that these limitations provide opportunities for further investigation. By highlighting limitations, we acknowledge the boundaries of our contribution and show humility about what remains to be addressed, which strengthens the manuscript’s integrity.

 

Submission Date           11 April 2025

Date of this review        01 May 2025 06:35:52

 

Thank you for your time and effort to improve this manuscript.

authors

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I am grateful for the review and changes made to the manuscript. The modifications have significantly improved the clarity and quality of the article. The manuscript now meets the required standards and can be considered for publication.