The DOI matches its Crossref record; 62 of the 63 references it cites were found in a registry or an index; 3 of the 28 sources its claims cite were checked for support.
Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education
Open accessAbstract
Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This article presents twelve practical tips for medical educators to address ethical concerns when implementing artificial intelligence (AI) in medical education, covering transparency, bias, validation, privacy, consent, collaboration, training, monitoring, accountability, regulation, and ethics oversight. The authors argue that adhering to these tips can support a responsible, sustainable, and ethically grounded integration of AI.
Key findings
As a tips/commentary paper, it offers recommendations rather than empirical results:
- Transparency requires choosing explainable algorithms and documenting data, training processes, and decision logic, though the authors acknowledge "complete transparency in AI systems is not achievable in all contexts."
- Bias should be addressed through diverse, representative training data and regular bias audits, while conceding that "eliminating all bias in AI systems may not be fully achievable."
- AI-generated educational content should be validated by domain experts and end-user feedback as an ongoing, not one-time, process.
- Privacy protection should use encryption, anonymization where feasible, and compliance with HIPAA; the authors note "complete privacy in AI models cannot be guaranteed" and that personalization may require users to "sacrifice a certain level of privacy."
Methods
This is a narrative, expert-opinion "twelve tips" article; no study design, dataset, or sample is reported. The authors synthesize prior literature (61 references) and illustrate each tip with hypothetical scenarios (e.g., an adaptive learning platform, an AI-driven recommendation system, a virtual escape room, a CDSS training case, an AI radiology curriculum, an AI tutoring system, and an AI surgical simulation). No formal analysis method is described.
Limitations
Not stated in the paper; the article does not include a limitations section. Evident from the text: the recommendations are not empirically tested, and the authors repeatedly acknowledge that full transparency, complete bias elimination, and guaranteed privacy are unattainable goals, framing the tips as aspirational guidance rather than validated interventions.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
24 Not checked yet · 6 Own interpretation · 3 Partly supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Even though AI has numerous advantages, medical schools worldwide are yet to explore AI to its full potential with robust implementation.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction
What the sources say
- Partly supportedcites [5] Grunhut J, Marques O, Wyatt ATM. Needs, challenges, and applications of artificial intelligence in medical education curriculum. JMIR Med Educ. 2022; 8(2): e35587. doi: 10.2196/35587 35671077 PMC9214616 sourceExperimental — accuracy not yet measured
The Abstract says medical schools do not currently incorporate AI into their curricula, and the Introduction’s “Suggested Steps” section describes AI in medical education as being in its infancy. These passages support the point that implementation remains limited, but the paper does not establish the claim specifically for medical schools worldwide. (LLM, conf=0.82)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The main reasons for this include the lack of AI expertise in faculty and the lack of standard guidelines for the responsible use of AI [5].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction
What the sources say
- Partly supportedcites [5] Grunhut J, Marques O, Wyatt ATM. Needs, challenges, and applications of artificial intelligence in medical education curriculum. JMIR Med Educ. 2022; 8(2): e35587. doi: 10.2196/35587 35671077 PMC9214616 sourceExperimental — accuracy not yet measured
The Abstract (page unavailable) identifies lack of faculty expertise as a factor preventing medical schools from incorporating AI into their curricula. It also mentions a lack of guidance from the Liaison Committee on Medical Education, but not a lack of standard guidelines for the responsible use of AI; it lists other barriers as well, without ranking these two as the main reasons. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
In the paper
“Additionally, monitoring the algorithm’s performance helps identify any potential biases or unintended consequences that may arise during its use [43].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 9: ensuring ongoing maintenance of AI algorithms for performance, accuracy, and reliability
What the sources say
- Not checked yetcites [43] Larson DB, Harvey H, Rubin DL, et al. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J Am Coll Radiol. 2021; 18(3 Pt A): 413– 424. doi: 10.1016/j.jacr.2020.09.060 33096088 PMC7574690 source
plainconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“if these systems are based on algorithms constructed several years ago, they may become outdated and less effective.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 9: ensuring ongoing maintenance of AI algorithms for performance, accuracy, and reliability
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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In the paper
“the lack of transparency in AI decision-making processes making it difficult for users to understand how conclusions are drawn”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction
What the sources say
- Not checked yetcites [4] Nagi F, Salih R, Alzubaidi M, et al. Applications of artificial intelligence (AI) in medical education: a scoping review. Stud Health Technol Inform. 2023; 305: 648– 651. doi: 10.3233/SHTI230581 37387115 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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In the paper
“Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Abstract
What the sources say
- Not checked yetcites [4] Nagi F, Salih R, Alzubaidi M, et al. Applications of artificial intelligence (AI) in medical education: a scoping review. Stud Health Technol Inform. 2023; 305: 648– 651. doi: 10.3233/SHTI230581 37387115 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“AI’s reliance on knowledge derived from available literature and experimental results underscores the importance of a strong foundation in the data and information used to train or develop AI systems [11].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 2: addressing biases in AI algorithms to ensure fair representation of educational content
What the sources say
- Not checked yetcites [11] Gurupur V, Wan TTH. Inherent bias in artificial intelligence-based decision support systems for healthcare. Medicina (Kaunas). 2020; 56(3): 141. doi: 10.3390/medicina56030141 32244930 PMC7142873 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“the potential devaluation of human judgment as AI becomes more integrated into educational frameworks”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction
What the sources say
- Not checked yetcites [4] Nagi F, Salih R, Alzubaidi M, et al. Applications of artificial intelligence (AI) in medical education: a scoping review. Stud Health Technol Inform. 2023; 305: 648– 651. doi: 10.3233/SHTI230581 37387115 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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In the paper
“Hence, the quality and diversity of the input data play a significant role in shaping the system’s knowledge base as well as the output derived [12].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 2: addressing biases in AI algorithms to ensure fair representation of educational content
What the sources say
- Not checked yetcites [12] Nelson GS. Bias in Artificial Intelligence. N C Med J. 2019; 80(4): 220– 222. doi: 10.18043/ncm.80.4.220 31278182 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“AI also raises significant ethical concerns including privacy issues related to the handling and security of sensitive student and patient data”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction
What the sources say
- Not checked yetcites [4] Nagi F, Salih R, Alzubaidi M, et al. Applications of artificial intelligence (AI) in medical education: a scoping review. Stud Health Technol Inform. 2023; 305: 648– 651. doi: 10.3233/SHTI230581 37387115 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 20
- It may state two separate claims as one. 5
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This approach not only safeguards against potential ethical pitfalls but also contributes to the establishment of ethical best practices, promoting responsible and accountable use of AI in medical education [61].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 12: forming an ethics committee or engage existing institutional review boards for the ethical oversight of AI in medical education
What the sources say
- Partly supportedcites [61] Kenny LM, Nevin M, Fitzpatrick K. Ethics and standards in the use of artificial intelligence in medicine on behalf of the royal Australian and New Zealand college of radiologists. J Med Imaging Radiat Oncol. 2021; 65(5): 486– 494. doi: 10.1111/1754-9485.13289 34342139 PMC9291743 sourceExperimental — accuracy not yet measured
The Overarching governance section describes RANZCR’s Artificial Intelligence Committee developing ethical guidance, and Principle 9 calls for accountable governance of AI used in patient care. The paper does not recommend engaging institutional review boards or establish that this approach promotes ethical AI use specifically in medical education; its governance recommendations concern clinical practice. Page numbers are not available in our copy. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Partly supported
In the paper
“This is designed to protect the privacy of individuals’ health information while allowing the flow of health information needed to ensure high-quality health care and to protect the public’s health and well-being [24].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 4: prioritize privacy with robust measures to protect student and patient data used in AI-driven educational tools
What the sources say
- Not checked yetcites [24] Office for Civil Rights, H. H. S. Standards for privacy of individually identifiable health information. Final Rule Federal Register. 2002: 67(157): 53181– 53273. 12180470
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Anonymizing data whenever feasible adds an extra layer of privacy, allowing educational insights without compromising individuals’ identities [22].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 4: prioritize privacy with robust measures to protect student and patient data used in AI-driven educational tools
What the sources say
- Not checked yetcites [22] Car J, Sheikh A, Wicks P, et al. Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom. BMC Med. 2019; 17(1): 143. doi: 10.1186/s12916-019-1382-x 31311603 PMC6636050 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it’s essential to acknowledge that complete transparency in AI systems is not achievable in all contexts.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 1: ensuring transparency in the development and deployment of AI systems in medical education
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“This bias in AI algorithms can manifest in various ways such as selection or information bias, potentially leading to unfair and inaccurate outcomes [10].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 2: addressing biases in AI algorithms to ensure fair representation of educational content
What the sources say
- Not checked yetcites [10] Daneshjou R, Smith MP, Sun MD, et al. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review. JAMA Dermatol. 2021; 157(11): 1362– 1369. doi: 10.1001/jamadermatol.2021.3129 34550305 PMC9379852 source
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If educators are not aware, it not only leads to improper and irresponsible use of CDSS for teaching purposes but also paves the way for students to misuse CDSS in future without having proper guidelines on its ethical use.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 7: conducting faculty development programs on AI ethics to equip them with the knowledge and skills necessary to guide students
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, anonymization is not the only way to protect user data always.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 4: prioritize privacy with robust measures to protect student and patient data used in AI-driven educational tools
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even though complete privacy in AI models cannot be guaranteed because of changing risks and complex data issues, the aim should be set to keep information as safe as possible.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 4: prioritize privacy with robust measures to protect student and patient data used in AI-driven educational tools
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Establishing such a transparent framework for accountability not only mitigates risks but also fosters a culture of responsibility and collaboration among all involved parties [48].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 10: establishing clear lines of accountability for the development, deployment, and outcomes of AI systems in medical education
What the sources say
- Not checked yetcites [48] Choudhury A. Toward an ecologically valid conceptual framework for the use of artificial intelligence in clinical settings: need for systems thinking, accountability, decision-making, trust, and patient safety considerations in safeguarding the technology and clinicians. JMIR Hum Factors. 2022; 9(2): e35421. doi: 10.2196/35421 35727615 PMC9257623 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“This forward-looking approach ensures that the integration of AI into medical education aligns with evolving legal frameworks, thereby safeguarding patient data, ensuring privacy, and upholding the ethical standards essential to responsible AI deployment in healthcare settings [53].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 11: enhancing the regulatory awareness about the standards governing the use of AI in healthcare and medical education
What the sources say
- Not checked yetcites [53] Harvey HB, Gowda V. Regulatory Issues and Challenges to Artificial Intelligence Adoption. Radiol Clin North Am. 2021; 59(6): 1075– 1083. doi: 10.1016/j.rcl.2021.07.007 34689875 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Establishing such a transparent framework for accountability not only mitigates risks but also fosters a culture of responsibility and collaboration among all involved parties [48].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 10: establishing clear lines of accountability for the development, deployment, and outcomes of AI systems in medical education
What the sources say
- Not checked yetcites [48] Choudhury A. Toward an ecologically valid conceptual framework for the use of artificial intelligence in clinical settings: need for systems thinking, accountability, decision-making, trust, and patient safety considerations in safeguarding the technology and clinicians. JMIR Hum Factors. 2022; 9(2): e35421. doi: 10.2196/35421 35727615 PMC9257623 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“This forward-looking approach ensures that the integration of AI into medical education aligns with evolving legal frameworks, thereby safeguarding patient data, ensuring privacy, and upholding the ethical standards essential to responsible AI deployment in healthcare settings [53].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 11: enhancing the regulatory awareness about the standards governing the use of AI in healthcare and medical education
What the sources say
- Not checked yetcites [53] Harvey HB, Gowda V. Regulatory Issues and Challenges to Artificial Intelligence Adoption. Radiol Clin North Am. 2021; 59(6): 1075– 1083. doi: 10.1016/j.rcl.2021.07.007 34689875 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, just like humans, machines also have implicit bias and eliminating all bias in AI systems may not be fully achievable, but it is imperative to strive towards minimizing biases to ensure fairness and equity in AI applications.”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 2: addressing biases in AI algorithms to ensure fair representation of educational content
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Involving students in these discussions provides valuable input into the development process, helping to tailor AI applications to meet their needs and preferences [31].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 6: fostering collaboration between AI experts, educators, and students to bring diverse perspectives and expertise
What the sources say
- Not checked yetcites [31] Abhee SS, Phillips R. How artificial intelligence (AI) could have helped our medical education during the COVID-19 pandemic - a student’s perspective. Med Teach. 2020; 42(11): 1315– 1316. doi: 10.1080/0142159X.2020.1798371 32755431 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Implementation of advanced data encryption techniques plays a crucial role in protecting individual identities and maintaining the integrity of personal and health-related data [20,21].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 4: prioritize privacy with robust measures to protect student and patient data used in AI-driven educational tools
What the sources say
- Not checked yetcites [20] Goldsteen A, Farkash A, Moffie M, et al. Applying artificial intelligence privacy technology in the healthcare domain. Stud Health Technol Inform. 2022; 294: 121– 122. doi: 10.3233/SHTI220410 35612030 source
- Not checked yetcites [21] Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021; 22(1): 122. doi: 10.1186/s12910-021-00687-3 34525993 PMC8442400 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Assigning specific roles to AI developers, educators, administrators, and other stakeholders including students ensures that everyone understands their responsibilities and is accountable for the ethical and effective use of AI in education [46].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 10: establishing clear lines of accountability for the development, deployment, and outcomes of AI systems in medical education
What the sources say
- Not checked yetcites [46] Naik N, Hameed BMZ, Shetty DK, et al. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg. 2022; 9: 862322. doi: 10.3389/fsurg.2022.862322 35360424 PMC8963864 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Obtaining written informed consent is a foundational step in the ethical integration of AI technologies into educational as well as clinical processes [26].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 5: obtaining informed consent from students and stakeholders regarding the use of AI technologies in educational processes
What the sources say
- Not checked yetcites [26] Amann J, Blasimme A, Vayena E, et al. & Precise4Q consortium (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020; 20(1): 310. doi: 10.1186/s12911-020-01332-6 33256715 PMC7706019 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“Though not martinet, the ethics committees play a crucial role in stressing the importance of adherence to regulation and scrutinizing potential ethical challenges, including privacy concerns, fairness, and transparency [57].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 12: forming an ethics committee or engage existing institutional review boards for the ethical oversight of AI in medical education
What the sources say
- Not checked yetcites [57] Abràmoff MD, Roehrenbeck C, Trujillo S, et al. A reimbursement framework for artificial intelligence in healthcare. npj Digital Med. 2022; 5(1): 72. doi: 10.1038/s41746-022-00621-w PMC9184542 35681002 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“Regular collaboration with these ethical oversight bodies ensures that any emerging ethical concerns are identified and addressed promptly [60].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 12: forming an ethics committee or engage existing institutional review boards for the ethical oversight of AI in medical education
What the sources say
- Not checked yetcites [60] Jia H. Yi Zeng: promoting good governance of artificial intelligence. Natl Sci Rev. 2020; 7(12): 1954– 1956. doi: 10.1093/nsr/nwaa255 34676086 PMC7665600 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“This approach not only safeguards against potential ethical pitfalls but also contributes to the establishment of ethical best practices, promoting responsible and accountable use of AI in medical education [61].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 12: forming an ethics committee or engage existing institutional review boards for the ethical oversight of AI in medical education
What the sources say
- Not checked yetcites [61] Kenny LM, Nevin M, Fitzpatrick K. Ethics and standards in the use of artificial intelligence in medicine on behalf of the royal Australian and New Zealand college of radiologists. J Med Imaging Radiat Oncol. 2021; 65(5): 486– 494. doi: 10.1111/1754-9485.13289 34342139 PMC9291743 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Engaging institutional review boards leverages their expertise in ethical review processes, particularly when handling sensitive data or novel applications of AI [59].”
§ Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education > Introduction > Tip 12: forming an ethics committee or engage existing institutional review boards for the ethical oversight of AI in medical education
What the sources say
- Not checked yetcites [59] McKay F, Williams BJ, Prestwich G, et al. Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model. Res Involv Engagem. 2022; 8(1): 21. doi: 10.1186/s40900-022-00357-7 35598004 PMC9123617 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
Biases in AI algorithms can lead to unequal representation and impact the quality of medical education.
“Biases in AI algorithms can lead to unequal representation and impact the quality of medical 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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To effectively harness the technology, it is essential to develop guidelines that promote innovative teaching strategies and assessments [44].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [44]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A lack of empirical research and foundational definitions of AI ethics was noted as a barrier to guiding curriculum development [190, 195, 198–200].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [195]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Numerous articles (5%) provide guidelines and “tips” on AI in medical education [57, 83, 94, 135, 136, 162, 179, 186, 195, 196, 202, 205–210].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [195]
Citations
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Metadata
- Authors
- Franco D’Souza, R., Mathew, M., Mishra, V., & Surapaneni, K. M.
- Affiliations
- Department of Education, UNESCO Chair in Bioethics, Melbourne, Australia
- Manipal Academy of Higher Education (IN)
- Datta Meghe Institute of Higher Education and Research (IN)
- Panimalar Medical College Hospital & Research Institute (IN)
- Corresponding author
- Surapaneni, Krishna Mohan
- Venue
- Medical Education Online
- Publisher
- Informa UK Limited
- Published
- 2024-04-03
- Volume
- 29
- Issue
- 1
- Pages
- 2330250-2330250
- ISSN
- 1087-2981 (ISSN-L)
- PMC
- PMC10993743
- OpenAlex
- W4393862318
- Cited by
- 147
- References
- 63
- Citations by year
- 2026: 57 · 2025: 69 · 2024: 20
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Medical Education Online (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- no funding
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- artificial intelligence in medical education, AI ethics, transparency, algorithmic bias, data protection, informed consent, teacher professional development, ethics committees, accountability, AI monitoring, responsible AI adoption
- MeSH terms
- Artificial Intelligence, Education, Medical, Humans, Social Responsibility, Students
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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