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Ethical Concerns in Medical and Health-Related AI
Perspective  ·  Published: 01 June 2026
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ICCK Transactions on Emerging Topics in Artificial Intelligence
Volume 3, Issue 3, 2026: 160-169
Perspective Open Access

Ethical Concerns in Medical and Health-Related AI

by
Carl Becker Carl Becker 1 * ORCID
1 Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan
* Corresponding Author: Carl Becker, [email protected]
Volume 3, Issue 3
Volume 3, Issue 3 2026
Received: 29 April 2026 Accepted: 20 May 2026 Published: 01 June 2026 CrossMark
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DOI: 10.62762/TETAI.2026.613827
ARK: ark:/57805/tetai.2026.613827

Article Information

Published in ICCK Transactions on Emerging Topics in Artificial Intelligence
Volume/Issue Volume 3, Issue 3, 2026
Pages 160-169
Cited by 1 (Crossref)

Abstract

This perspective introduces the range of ethical concerns entailed by the widespread adoption of AI, particularly as they impact human health. It begins by (1) illustrating risks associated with all large-scale AI systems, then moves to (2) corporate and governmental applications of AI that affect human health. It overviews the ways (3) that patient usage of AI has affected human health; (4) that “passive” medical AI (like recording documents) and (5) “active” medical AI (like diagnosing and prescribing) may affect human health. It concludes with (6) reflections on reporting, responsibility, and regulation, wherein international cooperation and governance systems appear essential for the beneficial use of AI for public health purposes.

Keywords

artificial intelligence environmental degradation moral psychology diagnosis regulation moral responsibility

Data Availability Statement

Not applicable.

Funding

The author participates in research on the “ELSI of medical AI” funded by the Toyota Foundation’s grant D24-ST-0039, received by PI colleague Shiho Koizumi. The author himself has received no financial remuneration for this work.

Conflicts of Interest

The author declares no direct financial conflicts of interest. The author participates in the Toyota Foundation-funded project ``ELSI of medical AI'' (grant D24-ST-0039, PI: Shiho Koizumi) related to the topic of this article, though no financial remuneration was received by the author for this work or for the preparation of this manuscript.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

References

  1. DeGeurin, M. (2023, April 23). Move aside, Crypto. AI could be the next climate disaster: A new Stanford report highlights the staggering carbon emissions required to train and maintain large language models like OpenAI's ChatGPT. Gizmodo. Retrieved from https://gizmodo.com/chatgpt-ai-openai-carbon-emissions-stanford-report-1850288635
    [Google Scholar]
  2. Silva, C. (2025, May 21). Creating a 5-second AI video is like running a microwave for an hour. Mashable. Retrieved from https://mashable.com/article/energy-ai-worse-than-we-thought
    [Google Scholar]
  3. Hao, K. (2019, June 6). Training a single AI model can emit as much carbon as five cars in their lifetimes: Deep learning has a terrible carbon footprint. MIT Technology Review. Retrieved from https://www.technologyreview.com/2019/06/06/239031
    [Google Scholar]
  4. O'Donnell, J., & Crownhart, C. (2025, May 20). We did the math on AI's energy. MIT Technology Review. Retrieved from https://www.technologyreview.com/2025/05/20/1116327/
    [Google Scholar]
  5. Zewe, A. (2025, January 17). Explained: Generative AI's environmental impact. MIT News. Retrieved from https://sustainability.mit.edu/article/explained-generative-ais-environmental-impact
    [Google Scholar]
  6. Halper, E., & O'Donovan, C. (2024, November 1). As data centers for AI strain the power grid, bills rise for everyday customers. The Washington Post. Retrieved from https://www.washingtonpost.com/business/2024/11/01/ai-data-centers-electricity-bills-google-amazon/
    [Google Scholar]
  7. Verma, P., & Tan, S. (2024). A bottle of water per email: the hidden environmental costs of using AI chatbots (visited on 09/18/2024). Retrieved from https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-datacenters/
    [Google Scholar]
  8. Masood, A., & Bhattacharya, A. (2024, August 20). Microsoft is building a data center in a tiny Indian village. Locals allege it's dumping industrial waste. Rest of World. Retrieved from https://restofworld.org/2024/microsoft-data-center-india-mekaguda-industrial-waste/
    [Google Scholar]
  9. Crowson, I. (2024, May 5). Google is ruining our lives: Residents living next to tech giant's £790m data centre in Hertfordshire claim it is making them ill and knocking thousands off their house prices. The Daily Mail. Retrieved from https://www.dailymail.co.uk/news/article-13370455/
    [Google Scholar]
  10. Nguyen, T., & Green, B. (2025, July). What happens when data centers come to town? University of Michigan Science, Technology, and Public Policy Unit. Retrieved from https://stpp.fordschool.umich.edu/sites/stpp/files/2025-07/stpp-data-centers-2025.pdf
    [Google Scholar]
  11. rFI. (2024, October 29). AI boom risks flooding planet with 'millions of tonnes of e-waste'. RFI. Retrieved from https://www.rfi.fr/en/science-and-technology/20241029-ai-boom-set-to-flood-planet-with-millions-of-tonnes-of-e-waste-nature
    [Google Scholar]
  12. Hagey, K., Cherney, M., & Horwitz, J. (2022, May 5). Facebook deliberately caused havoc in Australia to influence new law, whistleblowers say. The Wall Street Journal. Retrieved from https://www.wsj.com/tech/facebook-deliberately-caused-havoc-in-australia-to-influence-new-law-whistleblowers-say-11651768302
    [Google Scholar]
  13. Perrigo, B. (2023, January 18). OpenAI used Kenyan workers on less than $2 per hour. Time. Retrieved from https://time.com/6247678/openai-chatgpt-kenya-workers/
    [Google Scholar]
  14. Evicore. (n.d.). Evicore. Retrieved from https://www.evicore.com
    [Google Scholar]
  15. Miller, T. C., & Armstrong, D. (2024, October 23). "Not medically necessary": Inside the company helping America's biggest health insurers deny coverage for care. ProPublica. Retrieved from https://www.propublica.org/article/evicore-health-insurance-denials-cigna-unitedhealthcare-aetna-prior-authorizations
    [Google Scholar]
  16. Ross, C., & Herman, B. (2023, November 14). UnitedHealth class action lawsuit filed over algorithm used to deny care for Medicare Advantage members. STAT News. Retrieved from https://www.statnews.com/2023/11/14/unitedhealth-class-action-lawsuit-algorithm-medicare-advantage/
    [Google Scholar]
  17. Lee, B., Kramer, P., Sandri, S., Chanda, R., Favorito, C., Nasef, O., ... & Dai, T. (2025, August). Early recalls and clinical validation gaps in artificial intelligence–enabled medical devices. In JAMA health forum (Vol. 6, No. 8, pp. e253172-e253172). American Medical Association.
    [CrossRef] [Google Scholar]
  18. Shared Health Services. (2026). The battle of the bots: What EHR decision engines mean for wound care in 2026. Shared Health Services. Retrieved from https://www.sharedhealthservices.com/post/the-battle-of-the-bots-what-ehr-decision-engines-mean-for-wound-care-in-2026
    [Google Scholar]
  19. Ratcliffe, R. (2019, October 16). How a glitch in India's biometric welfare system can be lethal. The Guardian. Retrieved from https://www.theguardian.com/technology/2019/oct/16/glitch-india-biometric-welfare-system-starvation
    [Google Scholar]
  20. Heikkilä, M. (2022). Dutch scandal serves as a warning for Europe over risks of using algorithms. POLITICO. Retrieved from https://www.politico.eu/article/dutch-scandal-serves-as-a-warning-for-europe-over-risks-of-using-algorithms/
    [Google Scholar]
  21. Bulman, M. (2020, September 29). 'Flawed algorithm' used to calculate universal credit forcing people into hunger and debt. The Independent. Retrieved from https://www.independent.co.uk/news/uk/home-news/universal-credit-algorithm-hunger-debt-human-rights-watch-dwp-b670953.html
    [Google Scholar]
  22. Szalavitz, M. (2021, August 11). The pain was unbearable. So why did doctors turn her away? Wired. Retrieved from https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/
    [Google Scholar]
  23. OECD. (2024, March 29). NYC MyCity Chatbot gives dangerous, illegal advice to businesses. OECD.AI. Retrieved from https://oecd.ai/en/incidents/2024-03-29-3dce
    [Google Scholar]
  24. Priyadarshini, I. (2025, June 30). The AI deception era has begun — and no one knows how to stop it. Convergence Now. Retrieved from https://www.convergence-now.com/artificial-intelligence/the-ai-deception-era-has-begun-and-no-one-knows-how-to-stop-it-119510/
    [Google Scholar]
  25. Park, P. S., Goldstein, S., O’Gara, A., Chen, M., & Hendrycks, D. (2024). AI deception: A survey of examples, risks, and potential solutions. Patterns, 5(5), 100988.
    [CrossRef] [Google Scholar]
  26. Rosenblatt, J. (2025, June 1). AI is learning to escape human control: Models rewrite code to avoid being shut down. The Wall Street Journal. Retrieved from https://www.wsj.com/opinion/ai-is-learning-to-escape-human-control-technology-model-code-programming-066b3ec5
    [Google Scholar]
  27. Shekar, S., Pataranutaporn, P., Sarabu, C., Cecchi, G. A., & Maes, P. (2024). People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy. arXiv preprint arXiv:2408.15266. https://arxiv.org/html/2408.15266v1
    [Google Scholar]
  28. Wang, C., Liu, S., Yang, H., Guo, J., Wu, Y., & Liu, J. (2023). Ethical considerations of using ChatGPT in health care. Journal of medical Internet research, 25, e48009.
    [CrossRef] [Google Scholar]
  29. McBain, R. K., Bozick, R., Diliberti, M., Zhang, L. A., Zhang, F., Burnett, A., ... & Yu, H. (2025). Use of generative AI for mental health advice among US adolescents and young adults. JAMA Network Open, 8(11), e2542281.
    [CrossRef] [Google Scholar]
  30. Cole, S. (2025, April 29). Instagram's AI chatbots lie about being licensed therapists. 404 Media. Retrieved from https://www.404media.co/instagram-ai-studio-therapy-chatbots-lie-about-being-licensed-therapists/
    [Google Scholar]
  31. Consumer Federation of America. (2025, June 10). Re unlicensed practice of medicine and mental health provider impersonation on character-based generative AI platforms. Retrieved from https://consumerfed.org/wp-content/uploads/2025/06/Mental-Health-Chatbot-Complaint.pdf
    [Google Scholar]
  32. Caltrider, J. (2022, May 2). Top mental health and prayer apps fail spectacularly at privacy, security. Mozilla Foundation. Retrieved from https://www.mozillafoundation.org/en/blog/top-mental-health-and-prayer-apps-fail-spectacularly-at-privacy-security/
    [Google Scholar]
  33. Ciriello, R. (2025, April 2). An AI companion chatbot is inciting self-harm, sexual violence and terror attacks. The Conversation. Retrieved from https://theconversation.com/an-ai-companion-chatbot-is-inciting-self-harm-sexual-violence-and-terror-attacks-252625
    [Google Scholar]
  34. Namvarpour, M., Pauwels, H., & Razi, A. (2025). AI-induced sexual harassment: investigating contextual characteristics and user reactions of sexual harassment by a companion chatbot. Proceedings of the ACM on Human-Computer Interaction, 9(7), 1-28.
    [CrossRef] [Google Scholar]
  35. OECD. (2024). OECD AI principles overview. OECD.AI Policy Observatory. Retrieved from https://oecd.ai/en/ai-principles
    [Google Scholar]
  36. Haidt, J., & Rausch, Z. (2026). Social media is harming adolescents at a scale large enough to cause changes at the population level. In World happiness report 2026 (Chapter 3). Retrieved from https://www.worldhappiness.report/ed/2026/social-media-is-harming-adolescents-at-a-scale-large-enough-to-cause-changes-at-the-population-level/
    [Google Scholar]
  37. Moodie, C. (2023, May 28). Australian Medical Association calls for national regulations around AI in health care. ABC News. Retrieved from https://www.abc.net.au/news/2023-05-28/ama-calls-for-national-regulations-for-ai-in-health/102381314
    [Google Scholar]
  38. Wooler, S. (2024, March 8). Outrage over 'creepy' plans for AI to listen in to your NHS appointments and automatically generate notes. The Daily Mail. Retrieved from https://www.dailymail.co.uk/health/article-13172559/Outrage-creepy-plans-AI-listen-NHS-appointments-automatically-generate-notes.html
    [Google Scholar]
  39. Edwards, B. (2022, September 22). Artist finds private medical record photos in popular AI training data set. Ars Technica. Retrieved from https://arstechnica.com/information-technology/2022/09/artist-finds-private-medical-record-photos-in-popular-ai-training-data-set/
    [Google Scholar]
  40. Koenecke, A., Choi, A. S. G., Mei, K. X., Schellmann, H., & Sloane, M. (2024, June). Careless whisper: Speech-to-text hallucination harms. In Proceedings of the 2024 ACM conference on fairness, accountability, and transparency (pp. 1672-1681).
    [CrossRef] [Google Scholar]
  41. Burke, G., & Schellmann, H. (2024). Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said. AP News. Retrieved from https://www.doclounge.net/system/files/users/admin/Researchers_say_an_AI-powered_transcription_tool_used_in_hospitals_invents_things_no_one_ever_said.pdf
    [Google Scholar]
  42. Guo, E., & Hao, K. (2020, December 21). This is the Stanford vaccine algorithm that left out frontline doctors. MIT Technology Review. Retrieved from https://www.technologyreview.com/2020/12/21/1015303/stanford-vaccine-algorithm/
    [Google Scholar]
  43. Monahan, K., & Burlacu, G. (2024, July 23). From burnout to balance: AI-enhanced work models. Upwork. Retrieved from https://www.upwork.com/research/ai-enhanced-work-models
    [Google Scholar]
  44. Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., ... & Smith, D. M. (2023). Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA internal medicine, 183(6), 589-596.
    [CrossRef] [Google Scholar]
  45. HelloCare. (2019, March 11). Doctor tells man he will die over video-link robot. HelloCare. Retrieved from https://hellocare.com.au/doctor-tells-man-will-die-video-link-robot/
    [Google Scholar]
  46. Post, B., Badea, C., Faisal, A., & Brett, S. J. (2023). Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus. AI and Ethics, 3(4), 1215-1228.
    [CrossRef] [Google Scholar]
  47. Powles, J., & Hodson, H. (2017). Google DeepMind and healthcare in an age of algorithms. Health and technology, 7(4), 351-367.
    [CrossRef] [Google Scholar]
  48. Kamran, F., Tjandra, D., Heiler, A., Virzi, J., Singh, K., King, J. E., ... & Wiens, J. (2024). Evaluation of sepsis prediction models before onset of treatment. Nejm Ai, 1(3), AIoa2300032.
    [CrossRef] [Google Scholar]
  49. D. Banja, J., Xie, Y., R. Smith, J., Rana, S., & L. Holder, A. (2026). Mitigating bias in machine learning models with ethics-based initiatives: the case of sepsis. The American Journal of Bioethics, 26(2), 96-109.
    [CrossRef] [Google Scholar]
  50. Valbuena, V. S., Merchant, R. M., & Hough, C. L. (2022). Racial and ethnic bias in pulse oximetry and clinical outcomes. JAMA internal medicine, 182(7), 699-700.
    [CrossRef] [Google Scholar]
  51. Sato, M. (2024, April 4). Meta's AI image generator can't imagine an Asian man with a white woman. The Verge. Retrieved from https://www.theverge.com/2024/4/3/24120029/instagram-meta-ai-sticker-generator-asian-people-racism
    [Google Scholar]
  52. Ferryman, K., Cesare, N., Creary, M., & Nsoesie, E. O. (2024). Racism is an ethical issue for healthcare artificial intelligence. Cell Reports Medicine, 5(6), 101617.
    [CrossRef] [Google Scholar]
  53. Attia, A., Webb, J., Connor, K., Johnston, C. J., Williams, M., Gordon-Walker, T., ... & Stutchfield, B. M. (2024). Effect of recipient age on prioritisation for liver transplantation in the UK: a population-based modelling study. The Lancet Healthy Longevity, 5(5), e346-e355.
    [CrossRef] [Google Scholar]
  54. Chang, T., Nuppnau, M., He, Y., Kocher, K. E., Valley, T. S., Sjoding, M. W., & Wiens, J. (2024). Racial differences in laboratory testing as a potential mechanism for bias in AI: A matched cohort analysis in emergency department visits. PLOS global public health, 4(10), e0003555.
    [CrossRef] [Google Scholar]
  55. Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world?. Behavioral and brain sciences, 33(2-3), 61-83.
    [CrossRef] [Google Scholar]
  56. Barile, J., Margolis, A., Cason, G., Kim, R., Kalash, S., Tchaconas, A., & Milanaik, R. (2024). Diagnostic accuracy of a large language model in pediatric case studies. JAMA pediatrics, 178(3), 313-315.
    [CrossRef] [Google Scholar]
  57. Serra-Garcia, M., & Gneezy, U. (2021). Nonreplicable publications are cited more than replicable ones. Science advances, 7(21), eabd1705.
    [CrossRef] [Google Scholar]
  58. Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), 100804.
    [CrossRef] [Google Scholar]
  59. Saidi, P., Dasarathy, G., & Berisha, V. (2025). Unraveling overoptimism and publication bias in ML-driven science. Patterns, 6(4), 101185.
    [CrossRef] [Google Scholar]
  60. Denecke, K., Lopez-Campos, G., Rivera-Romero, O., & Gabarron, E. (2025). The Unexpected harms of artificial intelligence in healthcare: reflections on four real-world cases. In Redefining healthcare delivery in the digital era (pp. 55-60). IOS Press.
    [CrossRef] [Google Scholar]
  61. Kavanagh, K. (2025). World's first AI-designed viruses a step towards AI-generated life. Nature, 646, 16.
    [CrossRef] [Google Scholar]
  62. Gladue, D. P., & O’Mahony, A. (2025). CRISPR Treatments for AI-Designed Synthetic Viruses: Rapid Programmable Countermeasures for Emerging and Engineered Viruses. Viruses, 17(12), 1588.
    [CrossRef] [Google Scholar]
  63. Greenwood, V. (2025, October 14). Why AI companies are racing to build a virtual human cell. Time. Retrieved from https://time.com/7324119/what-is-virtual-cell/
    [Google Scholar]
  64. Tobia, K., Nielsen, A., & Stremitzer, A. (2021). When does physician use of AI increase liability?. Journal of Nuclear Medicine, 62(1), 17-21.
    [CrossRef] [Google Scholar]
  65. Smith, H., & Fotheringham, K. (2020). Artificial intelligence in clinical decision-making: rethinking liability. Medical Law International, 20(2), 131-154.
    [CrossRef] [Google Scholar]
  66. Cestonaro, C., Delicati, A., Marcante, B., Caenazzo, L., & Tozzo, P. (2023). Defining medical liability when artificial intelligence is applied on diagnostic algorithms: A systematic review. Frontiers in Medicine, 10, 1305756.
    [CrossRef] [Google Scholar]
  67. UK Government. (2022). National AI strategy: Pillar 3 – governing AI effectively. Retrieved from https://www.gov.uk/government/publications/national-ai-strategy/national-ai-strategy-html-version#pillar-3-governing-ai-effectively
    [Google Scholar]
  68. European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689
    [Google Scholar]
  69. Wei, Q., Xu, G., & Liu, Y. (2026). Comparative study of international medical AI regulatory policies: regional approaches and evidence from clinical practice. AI & SOCIETY, 1-35.
    [CrossRef] [Google Scholar]
  70. National Institute of Standards and Technology. (2023). NIST AI RMF playbook. U.S. Department of Commerce. Retrieved from https://airc.nist.gov/airmf-resources/playbook/
    [Google Scholar]
  71. OECD. (n.d.). OECD AI transparency. Retrieved from https://transparency.oecd.ai
    [Google Scholar]
  72. Pownall, C., & Kanayama, M. (2025, February). A review of AI and algorithmic risk and harm taxonomies from a human/user perspective. AIAAIC. Retrieved from https://www.aiaaic.org/projects/risk-harm-taxonomy-review
    [Google Scholar]
  73. AI Incident Database. (n.d.). AI incident database. Retrieved from https://incidentdatabase.ai/
    [Google Scholar]
  74. Rismani, S., Davis, L., Mingole, B., Rostamzadeh, N., Shelby, R., & Moon, A. (2025). Responsible AI measures dataset for ethics evaluation of AI systems. Scientific Data, 12, 1980.
    [CrossRef] [Google Scholar]
  75. International Association of Privacy Professionals. (n.d.). IAPP. Retrieved from https://iapp.org
    [Google Scholar]
  76. Katirai, A. (2023). The ethics of advancing artificial intelligence in healthcare: Analyzing ethical considerations for Japan's innovative AI hospital system. Frontiers in Public Health, 11, 1142062.
    [CrossRef] [Google Scholar]
  77. Ritchie, H. (2020, October 6). Cars, planes, trains: where do CO2 emissions from transport come from? Our World in Data. Retrieved from https://ourworldindata.org/co2-emissions-from-transport
    [Google Scholar]

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APA Style
Becker, C. (2026). Ethical Concerns in Medical and Health-Related AI. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(3), 160-169. https://doi.org/10.62762/TETAI.2026.613827
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TY  - JOUR
AU  - Becker, Carl
PY  - 2026
DA  - 2026/06/01
TI  - Ethical Concerns in Medical and Health-Related AI
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 3
IS  - 3
SP  - 160
EP  - 169
DO  - 10.62762/TETAI.2026.613827
UR  - https://www.icck.org/article/abs/TETAI.2026.613827
KW  - artificial intelligence
KW  - environmental degradation
KW  - moral psychology
KW  - diagnosis
KW  - regulation
KW  - moral responsibility
AB  - This perspective introduces the range of ethical concerns entailed by the widespread adoption of AI, particularly as they impact human health. It begins by (1) illustrating risks associated with all large-scale AI systems, then moves to (2) corporate and governmental applications of AI that affect human health. It overviews the ways (3) that patient usage of AI has affected human health; (4) that “passive” medical AI (like recording documents) and (5) “active” medical AI (like diagnosing and prescribing) may affect human health. It concludes with (6) reflections on reporting, responsibility, and regulation, wherein international cooperation and governance systems appear essential for the beneficial use of AI for public health purposes.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Becker2026Ethical,
  author = {Carl Becker},
  title = {Ethical Concerns in Medical and Health-Related AI},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2026},
  volume = {3},
  number = {3},
  pages = {160-169},
  doi = {10.62762/TETAI.2026.613827},
  url = {https://www.icck.org/article/abs/TETAI.2026.613827},
  abstract = {This perspective introduces the range of ethical concerns entailed by the widespread adoption of AI, particularly as they impact human health. It begins by (1) illustrating risks associated with all large-scale AI systems, then moves to (2) corporate and governmental applications of AI that affect human health. It overviews the ways (3) that patient usage of AI has affected human health; (4) that “passive” medical AI (like recording documents) and (5) “active” medical AI (like diagnosing and prescribing) may affect human health. It concludes with (6) reflections on reporting, responsibility, and regulation, wherein international cooperation and governance systems appear essential for the beneficial use of AI for public health purposes.},
  keywords = {artificial intelligence, environmental degradation, moral psychology, diagnosis, regulation, moral responsibility},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}

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