Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education
Open accessPublished version of a preprint
Abstract
The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on “informed consent” in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics—autonomy, beneficence, nonmaleficence, and justice—and extending them by integrating 3 public health ethics principles—efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.
AI snapshot
Written from the paper’s full text.
TL;DR
This viewpoint proposes a principle-based approach for teaching AI ethics in medical education, extending the 4 principles of medical ethics with 3 public health ethics principles. It argues this framework can address current and emerging AI-related ethical challenges better than teaching based solely on anticipated challenges.
Key findings
- The paper identifies 12 potential teaching subjects for AI ethics in medical education: informed consent, bias, safety, transparency, privacy, allocation, fairness, responsibility, empathy, explainability, liability, and accountability.
- It proposes building medical AI ethics on the 4 principles of medical ethics—autonomy, beneficence, nonmaleficence, and justice—and extending them with 3 public health ethics principles: efficiency, common good orientation, and proportionality.
- Using informed consent as an example, it illustrates intersections with 5 frequently proposed teaching subjects: bias, safety, transparency, privacy, and liability.
- It notes that current literature lacks specific guidance on content and methods for integrating AI ethics into medical curricula and calls for future development of problem-based and competency-oriented learning objectives.
Methods
- Theoretical analysis and viewpoint paper, not an empirical study; it builds on a previous scoping review of literature on teaching AI ethics in medical education and relevant AI ethics guidelines and frameworks.
- Publications from the scoping review, including proposed challenges associated with AI in medicine, were re-evaluated to analyze informed consent and develop the proposed principles.
- The methodology focuses on conceptual development and theoretical insights rather than empirical testing or data collection.
Limitations
- Stated in the paper: rapid AI evolution challenges static ethical guidelines; public health ethics principles are still evolving and not universally agreed upon; applicability may vary across cultural and health care settings; curriculum integration is constrained by accreditation processes, extensive content, and limited access to instructors knowledgeable in ethics, medicine, and AI.
- Evident from the text: the proposed framework is not empirically validated, and no learner outcomes or implementation evaluation are reported; the paper states future research is needed to develop learning objectives and educational content.
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Integrity signals
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Cites
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Claims the paper makes
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- Premisecitation
Other publications acknowledge the importance of ethics in AI education but do not provide specific content or guidance.
“Although other publications acknowledge the importance of ethics in AI education, they do not provide specific content or guidance [36-39].”
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Medical students have limited knowledge and understanding of AI.
“Furthermore, research suggests limited knowledge and understanding of AI among medical students [14].”
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- Premisecitation
There is a lack of guidance on specific content and methods for integrating AI ethics into medical curricula.
“Despite the need for early teaching of AI ethics, there is a lack of guidance on specific content and methods for integrating AI ethics into medical curricula [10].”
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Bias on the part of developers can result in inequitable treatment.
“Bias on the part of developers can also result in inequitable treatment [4].”
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- Premisecitation
Existing guidelines and frameworks on AI ethics echo the emphasis on the principles of medical ethics.
“This emphasis is also echoed by existing guidelines and frameworks regarding AI ethics [30].”
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- Premisecitation
A recurrent theme in teaching AI ethics in medical education focuses on the principles of medical ethics according to Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice).
“A recurrent theme related to the teaching of AI ethics as part of medical education focuses on the principles of medical ethics according to Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) [10].”
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In a recent scoping review, only a limited number of publications specifically focusing on teaching AI ethics as part of medical education were identified.
“In a recent scoping review, only a limited number of publications specifically focusing on the teaching of AI ethics as part of medical education were identified [10].”
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- Premisecitation
Medical students anticipate significant ethical challenges from the use of AI in medicine.
“Recent studies have indicated that medical students anticipate significant ethical challenges from the use of AI in medicine [12,13].”
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The potential for injustice can be further exacerbated if an increasing prevalence of AI in medical practice is anticipated.
“This potential for injustice can be further exacerbated if an increasing prevalence of AI in medical practice is anticipated.”
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Medical decisions are not made in isolation but are profoundly influenced by factors such as resource availability, health care infrastructure, and broader societal implications.
“It demonstrated how medical decisions are not made in isolation but are profoundly influenced by factors such as resource availability, health care infrastructure, and broader societal implications.”
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The benefit of AI in enhancing diagnostic speed and treatment choices is contingent on data quality.
“However, this benefit is contingent on the data quality.”
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Treatment recommendations from AI in medicine could lead to disadvantages for different groups or individuals due to risk of bias from unrepresentative training data, directly conflicting with the principle of justice.
“Owing to the risk of bias due to unrepresentative training data, for example, treatment recommendations from the use of AI in medicine could lead to disadvantages for different groups or individuals, directly conflicting with the principle of justice [50].”
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Biases in training data could lead to unequal or unfair treatment outcomes.
“It involves actively working to mitigate risks, such as biases in training data, that could lead to unequal or unfair treatment outcomes [50].”
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- Interpretationcitation
Hesitance to adopt AI technologies could directly conflict with the principle of common good orientation, as it hinders the widespread and equitable implementation of AI that could benefit the entire community.
“This hesitance could directly conflict with the principle of common good orientation, as it hinders the widespread and equitable implementation of AI that could benefit the entire community [25].”
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AI systems used in diagnostics and treatment recommendations often involve algorithms that might be nontransparent to both patients and health care professionals.
“AI systems, particularly those used in diagnostics and treatment recommendations such as ML, often involve algorithms that might be nontransparent to both patients and health care professionals.”
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- Interpretationreasoning
The challenge-based approach to AI ethics teaching does not fully capture the multidisciplinary and interdisciplinary nature of AI ethics in medicine.
“However, this approach does not fully capture the multidisciplinary and interdisciplinary nature of this field.”
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The incorporation of AI into health care decision-making can affect the presentation and comprehension of options by patients.
“The incorporation of AI into health care decision-making can affect the presentation and comprehension of options by patients.”
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Diagnostic AI-based applications might exhibit varying levels of accuracy across different demographic groups, potentially owing to data representation issues.
“For instance, a diagnostic AI-based application might exhibit varying levels of accuracy across different demographic groups, potentially owing to data representation issues [21].”
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- Interpretationcitation
The proposed AI ethics teaching subjects in existing literature primarily focus on challenges associated with the use of AI in medicine and fail to acknowledge the broader implications of foundational ethical principles.
“As the proposed AI ethics teaching subjects for medical education in the existing scientific literature primarily focus on the challenges associated with the use of AI in medicine, they fail to acknowledge the broader implications of foundational ethical principles.”
- Also stated in: § Theoretical Analysis of Recommended AI Ethics Teaching Subjects in Medical Education · plain
“While these 4 principles have been an integral part of current scientific publications on AI ethics in medical education, the recommended teaching subjects are mainly derived from the anticipated challenges associated with the use of AI in medicine [10].”
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- Also stated in: § Theoretical Analysis of Recommended AI Ethics Teaching Subjects in Medical Education · plain
- Premisecitation
Lack of transparency in AI systems presents a substantial challenge to conventional informed consent, complicating understanding and communicating how AI-based applications formulate recommendations.
“This lack of transparency presents a substantial challenge to the conventional process of informed consent, complicating the task of understanding and communicating how an AI-based application formulates recommendations [44].”
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Poor-quality data can result in AI models that incorrectly predict outcomes based on artifacts in the data rather than actual clinical results.
“Poor-quality data can result in AI models that incorrectly predict outcomes based on artifacts in the data rather than actual clinical results [21].”
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- Interpretationreasoning
If not carefully managed, AI could unintentionally increase health disparities by favoring populations with better access to the technology.
“Conversely, if not carefully managed, AI could unintentionally increase these disparities by favoring populations with better access to the technology.”
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ChatGPT demonstrated extensive medical knowledge, as exemplified by its ability to pass the written part of the United States Medical Licensing Exam.
“As ChatGPT demonstrated extensive medical knowledge, as exemplified by its ability to pass the written part of the United States Medical Licensing Exam, AI-based chat applications offer new opportunities for medical education and medical students, such as in simulated patient interactions and case study analysis [69,70].”
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Public health ethics principles such as efficiency, common good orientation, and proportionality are not universally agreed upon or applied consistently across different contexts.
“Unlike the well-established principles of medical ethics proposed by Beauchamp and Childress [34], public health ethics principles such as efficiency, common good orientation, and proportionality are not universally agreed upon or applied consistently across different contexts.”
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AI products for teaching, often sourced from third parties and guided by cost considerations, may pose unique challenges such as the risk of bias or rapid obsolescence.
“In addition, AI products for teaching, often sourced from third parties and guided by cost considerations, may pose unique challenges such as the risk of bias or rapid obsolescence [66,67].”
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AI-based chat applications offer new opportunities for medical education and medical students, such as in simulated patient interactions and case study analysis.
“As ChatGPT demonstrated extensive medical knowledge, as exemplified by its ability to pass the written part of the United States Medical Licensing Exam, AI-based chat applications offer new opportunities for medical education and medical students, such as in simulated patient interactions and case study analysis [69,70].”
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The lack of standardization of public health ethics principles presents a challenge for formulating a universally applicable ethical framework for AI in medicine.
“This lack of standardization presents a challenge for formulating a universally applicable ethical framework for AI in medicine.”
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The field of public health ethics is still evolving.
“Although the integration of public health ethics principles as part of medical AI ethics offers a comprehensive approach for teaching AI ethics in the medical setting, it is important to recognize that the field of public health ethics is still evolving [72].”
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- Premisecitation
Traditional ethics education primarily focuses on the 4 principles of medical ethics as formulated by Beauchamp and Childress.
“Traditional ethics education primarily focuses on the 4 principles of medical ethics as formulated by Beauchamp and Childress [41].”
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Findingown dataBased on analysis of existing publications on teaching AI ethics in medical education, 12 potential subjects were considered for teaching AI ethics.
“On the basis of the analysis of existing publications on teaching AI ethics in medical education, 12 potential subjects were considered for teaching AI ethics.”
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PremisecitationAdditional recommendations on AI ethics teaching content include explainability, liability, and accountability.
“Additional recommendations on AI ethics teaching content include “explainability,” “liability,” and “accountability,” which are also considered important by available guidelines [30,40].”
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PremisecitationAI algorithms are often trained on unrepresentative data, leading to potential discrimination and disadvantages for certain patient groups.
“AI algorithms are often trained on unrepresentative data, leading to potential discrimination and disadvantages for certain patient groups.”
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PremisecitationThe use of AI in medicine raises significant ethical challenges, including an increased risk of bias and potential discrimination against patients, as well as misdiagnoses that can lead to over- or underdiagnosis with substantial consequences for patients.
“However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients.”
- Also stated in: § Background · plain
“The use of AI is expected to pose significant ethical challenges.”
- Also stated in: § Background · plain
“The use of AI in medicine can also lead to erroneous diagnoses such as unnecessary treatment, which violates the basic principles of medical ethics [5].”
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- Also stated in: § Background · plain
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PremisecitationAnother publication emphasizes teaching fairness and responsibility in AI ethics education.
“Besides these 6 potential teaching subjects, the importance of teaching fairness and responsibility is emphasized by another publication that focuses on AI ethics education [11].”
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PremisecitationThe importance of empathy has been emphasized in relation to the use of AI in medicine and the associated need to teach AI ethics.
“Furthermore, the importance of empathy has been emphasized in relation to the use of AI in medicine and the associated need to teach AI ethics [6].”
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PremisecitationRecent publications emphasize the importance of teaching AI ethics in medical education.
“In addition to the technical and legal aspects of the use of AI in medicine, recent publications emphasize the importance of teaching AI ethics in medical education [9-11].”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationThe use of AI in medicine can lead to erroneous diagnoses such as unnecessary treatment, which violates the basic principles of medical ethics.
“The use of AI in medicine can also lead to erroneous diagnoses such as unnecessary treatment, which violates the basic principles of medical ethics [5].”
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PremisecitationResearch recommends teaching AI ethics early in medical education to prepare for its potential impacts and challenges.
“Research recommends teaching AI ethics early in medical education to prepare for its potential impacts and challenges [6-8].”
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PremisecitationOne identified publication defined 6 potential topics for AI ethics teaching: informed consent, bias, safety, transparency, patient privacy, and allocation.
“In one of the 2 identified publications specifically addressing AI ethics teaching content for medical education, 6 potential topics were defined: informed consent, bias, safety, transparency, patient privacy, and allocation [9].”
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InterpretationreasoningThe proposed principle-based approach provides an important foundation to ensure that future medical professionals are aware of the ethical dimensions of AI in medicine and equipped to make informed ethical decisions in their practice.
“The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice.”
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InterpretationreasoningInformed consent serves more than just a legal requirement to minimize liabilities; it is a crucial aspect of patient-centered care and a fundamental element of ethical medical practice.
“As a result, informed consent serves more than just a legal requirement to minimize liabilities; it is a crucial aspect of patient-centered care and a fundamental element of ethical medical practice.”
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InterpretationreasoningThe proposed teaching subject of informed consent intersects with each of the other proposed teaching subjects, resulting in substantial overlap that can challenge clear distinctions between different areas of AI ethics.
“For example, the proposed teaching subject of “informed consent” warrants a detailed analysis to exemplify the high level of interdisciplinarity present in AI ethics, intersecting with each of the other proposed teaching subjects. This interconnection results in a substantial overlap, which can challenge the establishment of clear distinctions between the different areas of AI ethics.”
Quote in the paperSubstantial claim- Rests on an assumption: Informed consent is a representative example for illustrating the interdisciplinarity and interconnectedness of AI ethics teaching subjects.
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PremisecitationAI in medicine has the potential to enhance the efficiency of medical services through faster and more accurate diagnostics.
“Owing to the capabilities of AI in medicine with the potential to enhance the efficiency of medical services through faster and more accurate diagnostics, it is crucial to consider the ethical implications of these developments [19].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] Topol, E, 2019, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
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PremisecitationInformed consent represents an important development in medical ethics and patient rights, representing a departure from the historically paternalistic nature of medical practice.
“Informed consent represents an important development in medical ethics and patient rights, representing a departure from the historically paternalistic nature of medical practice [42].”
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PremisecitationThe use of AI holds substantial promise in enhancing the efficiency and effectiveness of distributing limited medical resources.
“In the critical area of resource allocation within health care, the use of AI holds substantial promise in enhancing the efficiency and effectiveness of distributing limited medical resources [63].”
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InterpretationreasoningThe proposed principle-based approach offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, as recommended in current academic discourse.
“The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningEmbracing an integrated perspective is important for a comprehensive understanding of AI ethics in medical practice and education, underscoring the need to re-evaluate and potentially refine current teaching recommendations.
“Embracing this integrated perspective is important for a comprehensive understanding of AI ethics in medical practice and education, underscoring the need to re-evaluate and potentially refine current teaching recommendations.”
Quote in the paperNot a substantial claim- Rests on an assumption: Informed consent is a representative example for illustrating the interdisciplinarity and interconnectedness of AI ethics teaching subjects.
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PremisecitationAI systems may inadvertently perpetuate existing biases or fail to address the diverse needs of different patient groups.
“However, it is essential to guard against the risk of AI systems inadvertently perpetuating existing biases or failing to address the diverse needs of different patient groups.”
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InterpretationreasoningThe ability of AI to rapidly analyze large data sets can greatly enhance the speed of diagnostic procedures, which could result in more timely patient care and improved treatment choices that are more precise.
“The ability of AI to rapidly analyze large data sets can greatly enhance the speed of diagnostic procedures, which could result in more timely patient care and improved treatment choices that are more precise.”
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InterpretationreasoningThe development and integration of informed consent into medical practice represents a substantial cultural and ethical transition toward acknowledging and upholding patient autonomy.
“The development and integration of informed consent into medical practice represents a substantial cultural and ethical transition toward acknowledging and upholding patient autonomy.”
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InterpretationreasoningA focus on core ethical principles rather than solely on specific challenges lays the groundwork for a robust and comprehensive educational strategy for AI in medicine.
“Similarly, when considering AI in medicine, a focus on core ethical principles rather than solely on specific challenges lays the groundwork for a robust and comprehensive educational strategy.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
PremisecitationThe 4 foundational principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-are highly relevant in the context of teaching ethics in medical education.
“The 4 foundational principles of medical ethics—autonomy, beneficence, nonmaleficence, and justice—are highly relevant in the context of teaching ethics in medical education [41].”
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InterpretationreasoningFocusing on intersections among recommended teaching subjects highlights the importance of an integrated educational approach in medical AI ethics.
“Focusing on these intersections, this section highlights the importance of an integrated educational approach in the context of medical AI ethics.”
Quote in the paperNot a substantial claim- Rests on an assumption: Informed consent is a representative example for illustrating the interdisciplinarity and interconnectedness of AI ethics teaching subjects.
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InterpretationreasoningIncorporating the 3 public health ethics principles ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine.
“By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine.”
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Interpretationnone givenThe ability of AI to process and analyze data can be harnessed to identify and address gaps in health care delivery, offering insights into underserved populations and tailoring interventions to meet their specific needs.
“The ability of AI to process and analyze data can be harnessed to identify and address gaps in health care delivery, offering insights into underserved populations and tailoring interventions to meet their specific needs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningInformed consent is pivotal in facilitating shared decision-making, ensuring patients are informed of their medical choices and engaged in selecting options that resonate with their personal health goals and values.
“In this context, informed consent is pivotal in facilitating SDM, as it ensures that patients are not only informed of their medical choices but also engaged in selecting options that resonate with their personal health goals and values.”
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InterpretationreasoningThe complexity of AI ethics in medicine extends beyond anticipated challenges, encompassing a wide range of disciplines such as law, medicine, ethics, and computer science.
“The complexity of AI ethics in medicine extends beyond these anticipated challenges, encompassing a wide range of disciplines such as law, medicine, ethics, and computer science.”
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InterpretationreasoningThe COVID-19 pandemic illustrates the necessity of integrating insights from various disciplines, including epidemiology, health economics, and ethics, into medical decision-making.
“The pandemic also illustrates the necessity of integrating insights from various disciplines, including epidemiology, health economics, and ethics into medical decision-making.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenTopics such as bias, privacy, and transparency are not merely isolated subjects but require a comprehensive, holistic evaluation.
“Such an approach acknowledges that topics such as bias, privacy, and transparency, among others, are not merely isolated subjects but instead require a comprehensive, holistic evaluation.”
Quote in the paperNot a substantial claim- Rests on an assumption: Informed consent is a representative example for illustrating the interdisciplinarity and interconnectedness of AI ethics teaching subjects.
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PremisecitationThe COVID-19 pandemic has highlighted the importance of balancing individual patient care with broader public health measures.
“The COVID-19 pandemic has highlighted the importance of balancing individual patient care with broader public health measures [54].”
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Interpretationnone givenUsing AI in medical practice could potentially lead to events where patients are harmed, for example, through biased decision-making or errors made by users.
“Furthermore, using AI in medical practice could potentially lead to events where patients are harmed, for example, through biased decision-making or errors made by users.”
Quote in the paperNot a substantial claim- Rests on an assumption: AI in medicine carries potential risks such as misdiagnosis, algorithmic biases, and data security breaches.
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InterpretationreasoningA negative perception of AI within the broader population could result in a general unwillingness or resistance to adopting AI technologies.
“This could potentially lead to a negative perception of AI within the broader population, which in turn may result in a general unwillingness or resistance to adopting AI technologies.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAI in medicine can address or potentially exacerbate health disparities.
“It is essential to recognize how AI in medicine can address or potentially exacerbate health disparities [64].”
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PremisecitationThe development of AI-based applications involves extensive data sets, raising concerns regarding data privacy and the potential for expropriation of personal health data.
“Moreover, the development of AI-based applications involves extensive data sets, raising concerns regarding data privacy and the potential for expropriation of personal health data [9].”
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InterpretationreasoningThe high degree of interdisciplinarity and intersectionality in AI ethics highlights potential conflicts in teaching AI ethics based solely on the anticipated challenges associated with the implementation and use of AI in medicine.
“The high degree of interdisciplinarity and intersectionality in AI ethics, as detailed in the previous section, highlights potential conflicts in teaching AI ethics based solely on the anticipated challenges associated with the implementation and use of AI in medicine.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAccess to AI technology could be limited, for example, by economic means, thereby potentially perpetuating existing inequalities in access to advanced medical technologies.
“Furthermore, access to the technology itself could be limited, for example, by economic means, thereby potentially perpetuating existing inequalities in access to advanced medical technologies [35].”
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PremisecitationAI-based chat applications can hallucinate and might not provide correct medical information due to improper "prompting".
“This becomes particularly evident, as AI-based chat applications can hallucinate and might not provide correct medical information due to improper “prompting” [70].”
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InterpretationreasoningThe interdisciplinary nature of public health ethics adds to the complexity of integrating these principles into medical AI ethics education.
“Furthermore, the interdisciplinary nature of public health ethics, encompassing the aspects of sociology, economics, and political science, adds to the complexity of integrating these principles into medical AI ethics education.”
Quote in the paperNot a substantial claim- Rests on an assumption: Public health ethics is interdisciplinary, encompassing sociology, economics, and political science.
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InterpretationreasoningThe variability in applicability and relevance of ethical principles across cultural and health care settings could impact the universality of the proposed ethical framework and limit the applicability of teaching medical AI ethics as part of medical education.
“This variability could impact the universality of the proposed ethical framework and limit the applicability of teaching medical AI ethics as a part of medical education.”
Quote in the paperNot a substantial claim- Rests on an assumption: The applicability and relevance of ethical principles as part of medical AI ethics education may vary across cultural and health care settings.
- Rests on an assumption: Different regions may have varying access to AI technologies, and cultural values may influence the perceptions of integrating and using AI in the medical setting.
The paper relies on this without support.
The advancement of AI technologies in medicine will increase.
“As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly.”
The paper relies on this without support.
Informed consent is a representative example for illustrating the interdisciplinarity and interconnectedness of AI ethics teaching subjects.
“To effectively illustrate the interdisciplinarity and interconnectedness of frequently recommended teaching subjects for AI ethics in medical education, “informed consent” should be discussed in the context of 5 frequently proposed teaching subjects: bias, safety, transparency, privacy, and liability.”
The paper relies on this without support.
AI in medicine carries potential risks such as misdiagnosis, algorithmic biases, and data security breaches.
“The potential risks of using AI in medicine, such as misdiagnosis, algorithmic biases, and data security breaches, reinforce the relevance of the principle of nonmaleficence.”
The paper relies on this without support.
AI will increasingly be integrated into medical practice.
“Given the anticipated impact of AI on the field of medicine, which extends beyond the traditional concept of medical practice owing to its inherent interdisciplinarity and complexity, ethical considerations must be adapted accordingly.”
The paper relies on this without support.
Patients can understand and use information about AI biases, safety, transparency, privacy, and liability to make informed decisions about AI in their treatment.
“Patients must be informed of such disparities in accuracy as this information is vital for them to consent to the use of AI in their treatment.”
The paper relies on this without support.
Physicians can effectively communicate AI-driven medical information to patients if they understand how AI-based applications work.
“A clear understanding of how AI-based applications work, particularly how decision-making processes are performed, is required for physicians to effectively communicate with their patients [48].”
The paper relies on this without support.
Transparency and explainability in AI systems are achievable.
“Furthermore, transparency and explainability in AI systems are fundamental to ensure that patients autonomously understand and evaluate AI-driven health care choices.”
The paper relies on this without support.
Data quality can be rigorously validated.
“Therefore, the ethical use of AI in health care must include rigorous validation of the data quality to ensure accurate and reliable outcomes.”
The paper relies on this without support.
The need for timely adaptation challenges traditional ethics education in medicine, which may not account for the current use of AI in medicine.
“The need for timely adaptation challenges traditional ethics education in medicine, which may not account for the current use of AI in medicine.”
The paper relies on this without support.
The integration of AI into medical education may not always keep pace with rapid advancements in medical practice.
“This importance is underscored by the fact that the integration of AI into medical education may not always keep pace with rapid advancements in medical practice.”
The paper relies on this without support.
ChatGPT was not explicitly developed for use in the medical context and does not adhere to stringent medical device regulations.
“However, as ChatGPT was not explicitly developed for use in the medical context and, for example, does not adhere to stringent medical device regulations, it raises new ethical challenges.”
The paper relies on this without support.
The implementation and use of AI in medicine has a high level of intersectionality and interdisciplinarity.
“The high level of intersectionality and interdisciplinarity inherited by the implementation and use of AI in medicine highlights the importance of a principle-based approach rather than solely focusing on anticipated challenges.”
The paper relies on this without support.
Continuous evolution in the field of AI presents substantial challenges for the development of static ethical guidelines and frameworks for medical education.
“Continuous evolution in the field of AI presents substantial challenges for the development of static ethical guidelines and frameworks for medical education.”
The paper relies on this without support.
The applicability and relevance of ethical principles as part of medical AI ethics education may vary across cultural and health care settings.
“Furthermore, the applicability and relevance of ethical principles as a part of medical AI ethics education may vary across cultural and health care settings.”
The paper relies on this without support.
Different regions may have varying access to AI technologies, and cultural values may influence the perceptions of integrating and using AI in the medical setting.
“Different regions may have varying access to AI technologies, and cultural values may influence the perceptions of integrating and using AI in the medical setting.”
The paper relies on this without support.
Integrating new teaching content into medical curricula is challenging due to the need for time-intensive accreditation processes and extensive teaching content.
“Moreover, integrating new teaching content into medical curricula is challenging due to the need for time-intensive accreditation processes and extensive teaching content.”
The paper relies on this without support.
Limited access to instructors knowledgeable in ethics, medicine, and AI may pose a challenge to implementing the proposed teaching of medical AI ethics, as these experts may not be available in most institutions.
“In addition, limited access to instructors knowledgeable in ethics, medicine, and AI may pose a challenge to implementing the proposed teaching of medical AI ethics, as these experts may not be available in most institutions.”
The paper relies on this without support.
New advancements in AI cannot be foreseen and the capabilities of AI and AI-based applications in medicine are anticipated to expand.
“Given that new advancements, for example, as exemplified by AI-based chat applications such as ChatGPT, cannot be foreseen and that the capabilities of AI and AI-based applications in medicine are anticipated to expand, continuous updates of existing educational frameworks and content are required.”
The authors state this as an assumption.
The paramount importance of AI-based chat applications such as ChatGPT must be assumed.
“In the applicability of the principle-based approach to AI ethics, the paramount importance of AI-based chat applications such as ChatGPT must be assumed [68].”
The paper relies on this without support.
The advancement of AI technologies in medicine is expected to increase.
“As the advancement of AI technologies in medicine is expected to increase, it is essential for medical ethics education to adapt and evolve accordingly to keep pace with these developments.”
The paper relies on this without support.
Public health ethics is interdisciplinary, encompassing sociology, economics, and political science.
“Furthermore, the interdisciplinary nature of public health ethics, encompassing the aspects of sociology, economics, and political science, adds to the complexity of integrating these principles into medical AI ethics education.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fifteen publications focused explicitly on ethical considerations concerning the use of AI in medical education.
“15 publications focused explicitly on this topic [7, 38, 84, 97, 186, 190–199].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [193]
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Artificial Intelligence and Competency‐Based Education: A Rapid Scoping Review
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AI at the Helm: Evaluating Claude 3.5 Sonet and ChatGPT-4.0 in Tympanoplasty Management
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AI and Lifelong Learning in Developing Countries
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A multi-model longitudinal assessment of ChatGPT performance on medical residency examinations
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Artificial intelligence innovations challenges and emerging trends in engineering education
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Ethics Education in Neonatology: Integrating Theory, Multimodal Methods, and AI Innovation
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Ota, Y., Asada, Y., Kubo, S., Kanno, T., Yagi, M., & Matsuyama, Y. (2025) · JMIR Medical Education · doi:10.2196/84535 · Cited by 3 · see citing works
Miranda, J. P. P., Bansil, J. A., Fernando, E. Q., Gamboa, A. B., Hernandez, H. E., Cruz, M., Dianelo, R. F. B., Gonzales, D., & Penecilla, E. (2024) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia61827.2024.10761175 · Cited by 5 · see citing works
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“When adopting new technologies in nursing education and practice, factors such as ethics, privacy, and professional development must be carefully weighed (61).”
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Enhancing dental clinical education through AI: insights into post-clinical summaries
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Chen, S., Peng, Z., & Liu, N. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1771351 · Cited by 2 · see citing works
Uncertain hands: ethical implications of AI dependency in medical education
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From doing to being: Generative AI, workplace affordances, and professional identity
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Challenges for Ethics Review Committees in Regulating Medical Artificial Intelligence Research
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Intelligent Immersion: AI and VR Tools for Next-Generation Higher Education
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Ndacyayisenga, J. C. Y., Kidega, C., & Aciro Can, L. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09303-7 · Cited by 1 · see citing works
“The introduction of generative artificial intelligence (AI) built on large language models (LLMs) is shaping a paradigm shift in human intelligence and craftsmanship and how higher education is transformed, particularly in medical education [1, 2].”
Innovative Approaches to Training in Evidence‐Based Neurology
Raman, A., Ahmed, A. T., Sanghvi, G. V., Ballal, S., Aminov, Z., Al‐Hetty, H. R. A. K., Kalia, R., Arya, R., Kariem, M., & Mustafa, Y. F. (2025) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70224 · Cited by 1 · see citing works
Applying AI Ethics Tools in Mining Industry Organizations
Masárová, T. (2025) · Acta Montanistica Slovaca · doi:10.46544/ams.v30i3.04 · Cited by 1 · see citing works
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Pereira, S. F., & Vernekar, S. D. P. (2026) · Ethics Medicine and Public Health · doi:10.1016/j.jemep.2026.101300 · Cited by 1 · see citing works
Mapping AI Literacy in Medical Education: A Review of Concepts and Teaching Practices
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Reframing AI Literacy in Higher Education: Designing Curricula for Ethically Competent Managers
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Baka, E., Krischer, N., Silva, U. D., Tan, Y.-R., Yap, P., & Wong, B. L. H. (2025) · doi:10.2196/preprints.78649
Principles and Ethics for the Implementation of Digital Transformation on the Energy Transition
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Espinel, J. C. D., Casquete, A. L. G., & Garcet, Y. B. (2025) · Revista Española de Educación Médica · doi:10.6018/edumed.685191
Teaching Ethics and Shaping Futures: Building Ethical Competence in Tomorrow’s Healers
Rajasegaran, R. (2025) · International Journal of Advanced Medical and Health Research · doi:10.4103/ijamr.ijamr_144_25
The Role of Artificial Intelligence in Medical Education
Golahmadi, A. K., Rad, A. A., & Athanasiou, T. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_6
AI Ethics Education in the Arab Region
Baytiyeh, H., Elbassuoni, S., Muller, H. D., Mohamad, R., & Farah, E. N. (2026) · Springer eBooks · doi:10.1007/978-3-031-99068-7_9
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Agrawal, A., Akshara, & Agrawal, V. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-2680-2_49
De la alfabetización digital a la alfabetización ética en educación médica: apuntes para una premisa
García-Rivero, A. A., & Domínguez‐López, A. (2026) · Educación Médica · doi:10.1016/j.edumed.2026.101152
Optimizing AI for surgical aftercare: Claude 3.5 Sonnet outperforms ChatGPT-5.0 in otoplasty
Sarı, E., Ahmadov, N., & Muradova, A. (2026) · The Egyptian Journal of Otolaryngology · doi:10.1186/s43163-026-01038-y
Xunming, J. I., & Gong, M. (2026) · doi:10.2196/preprints.94349
Yao, G. (2026) · Ethics & Behavior · doi:10.1080/10508422.2026.2634324
Evolving and novel applications of artificial intelligence in interventional oncology
Almashni, S. Y., Fayek, F. B., Javens, D. C., Boulis, M. T., & Makary, M. S. (2026) · World Journal of Clinical Oncology · doi:10.5306/wjco.v17.i3.113226
Baka, E., Krischer, N., Silva, U. D., Tan, Y.-R., Yap, P., & Wong, B. L. H. (2026) · JMIR AI · doi:10.2196/78649
Tabatabaei, K., Rajaei, S. B., & Boland, D. G. (2026) · doi:10.2196/preprints.96660
Artificial Intelligence in Medical Education: An Umbrella Review
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Promise to Practice: Reimagining Artificial Intelligence for Equitable Global Health Impact
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Let’s Talk Systems - What Do We Really Mean by a Systems Approach in AI-Enhanced Medical Education?
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How cultural humility and ethical sensitivity shape ethical decision-making competence
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- Authors
- Weidener, L., & Fischer, M.
- Affiliations
- UMIT TIROL - Private University for Health Sciences and Health Technology (AT)
- Corresponding author
- Weidener, Lukas
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2024-02-09
- Volume
- 10
- Pages
- e55368
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC10891487
- OpenAlex
- W4391304280
- Cited by
- 90
- References
- 70
- Citations by year
- 2026: 47 · 2025: 35 · 2024: 8
- FWCI
- 8.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- AI ethics education, artificial intelligence in healthcare, medical ethics principles, informed consent, algorithmic bias, diagnostic bias, medical autonomy, beneficence, non-maleficence, justice, efficiency, common good, proportionality, medical curriculum
- MeSH terms
- Artificial Intelligence, Education, Medical, Ethics, Medical, Humans, Informed Consent, Beneficence
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