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Artificial Intelligence in Health AI in higher medical education

On the other hand, barriers to implementation and use in augmented reality based technology to visualize the internal
developing countries, such as limited internet connectivity, structure of the dental root – a proof of concept. Head Face
have resulted in lower levels of discussion around global Med. 2022;18(1):12.
fairness as an ethical issue. In addition, input data latency and doi: 10.1186/s13005-022-00307-4
potential dataset and algorithmic bias raise genuine concerns 2. Baker RS, Hawn A. Algorithmic bias in education. Int J Artif
about the output validity, especially regarding GenText and Intell Educ. 2022;32:1052-1092.
statistical analytical output. A particular concern in discursive
production is the ability of GenText to hallucinate (create non- doi: 10.1007/s40593-021-00285-9
existent references) and create output text of a biased nature 3. Albahri AS, Duhaim AM, Fadhel MA, et al. A systematic
(opinions and accounts ultimately derived from bias found in review of trustworthy and explainable artificial intelligence
input datasets and algorithmic structures) that could distort in healthcare: Assessment of quality, bias risk, and data
the nature of medical education, leading to bad ethical and fusion. Inform Fusion. 2023;96:156-191.
practical outcomes in the future. Furthermore, the intensive doi: 10.1016/J.INFFUS.2023.03.008
development of computer hardware, including quantum 4. Rudnicka Z, Szczepanski J, Pregowska A. Artificial
computers, and the algorithms themselves, and in particular intelligence-based algorithms in medical image scan
their learning methods, which is the heart of AI, is likely segmentation and intelligent visual content generation-a
to significantly shorten the time needed for more precise concise overview. Electronics (Basel). 2024;13(4):746.
analysis, which is crucial in the context of medical data. doi: 10.3390/electronics13040746

Acknowledgments 5. Pregowska A, Perkins M. Artificial Intelligence in Medical
Education: Technology and Ethical Risk. Available from:
None. https://ssrn.com/abstract=4643763 [Last accessed on 2024

Funding Oct 18].
doi: 10.2139/ssrn.4643763
This study was partially supported by the National Center
for Research and Development (research grant Infostrateg 6. Naik N, Hameed BMZ, Shetty DK, et al. Legal and ethical
consideration in artificial intelligence in healthcare: Who
I/0042/2021-00).
takes responsibility? Front Surg. 2022;9:862322.
Conflict of interest doi: 10.3389/fsurg.2022.862322
The authors declare they have no competing interests. 7. Bae CY, Im Y, Lee J, et al. Comparison of biological age
prediction models using clinical biomarkers commonly
Author contributions measured in clinical practice settings: AI techniques Vs.
traditional statistical methods. Front Anal Sci. 2021;1:709589.
Conceptualization: All authors
Writing – original draft: All authors doi: 10.3389/frans.2021.709589
Writing – review & editing: All authors 8. Hassija V, Chamola V, Mahapatra A, et al. Interpretability
of black-box models: A review on explainable artificial
Ethics approval and consent to participate intelligence (XAI). Cognit Comput. 2024;16:45-74.
Not applicable. doi: 10.1007/s12559-023-10179-8
9. Kunze KN, Williams RJ 3 , Ranawat AS, et al. Artificial
rd
Consent for publication intelligence (AI) and large data registries: Understanding
Not applicable. the advantages and limitations of contemporary data sets
for use in AI research. Knee Surg Sports Traumatol Arthrosc.
Availability of data 2024;32(1):13-18.
Not applicable. doi: 10.1002/ksa.12018
10. Baniecki H, Biecek P. Adversarial attacks and defenses in
Further disclosure explainable artificial intelligence: A survey. Information
None. Fusion. 2023;107:102303.
doi: 10.1016/j.inffus.2024.102303
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Volume 2 Issue 1 (2025) 9 doi: 10.36922/aih.3276