Ethical Issues of Artificial Intelligence in Diabetes Mellitus

Main Article Content

Gumpeny Ramachandra Sridhar, MD DM FACE FRCP Gumpeny Lakshmi, MD

Abstract

Artificial intelligence (AI) has permeated various branches of clinical medicine, with promising applications in predictive, diagnostic and therapeutic areas. Digital innovations are increasingly useful in the management of non communicable diseases in the form of tracking applications,  data collection systems (EMRs) and wearable sensors. Globally, diabetes mellitus is the most common and serious non communicable disease. There is a mismatch between the people with diabetes and the number of healthcare professionals needed to manage them. Therefore, artificial intelligence has the potential to play a significant role in addressing the unmet need. Majority of AI applications have been developed for the diabetes population. Ethical issues arising from the application can be carried over to its application in other areas of clinical medicine. Among diabetic population, artificial intelligence has been prominently employed in screening for diabetic retinopathy. Continuous glucose monitoring and insulin pumps are other areas of application. Data collection and sharing through AI media can ease the burden of poor doctor-patient ratio, and improve efficacy of treatment. Despite its advantages, and the fact that citizen juries have been found to be favourable towards the use of AI in research and treatment, certain drawbacks continue to exist. With the threat of data theft and breach of privacy, due diligence must be given to ethical and legal aspects to protect the patient. It is acknowledged that AI can facilitate the decision making process but not entirely replace a physician's role. With able governing laws, systems to protect safety, minimize bias and improve transparency, AI and precision medicine could help control the burden of disease.

Keywords: Privacy, security, reliability, safety, fairness, transparency, accountability, retinopathy

Article Details

How to Cite
SRIDHAR, Gumpeny Ramachandra; LAKSHMI, Gumpeny. Ethical Issues of Artificial Intelligence in Diabetes Mellitus. Medical Research Archives, [S.l.], v. 11, n. 8, aug. 2023. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/4287>. Date accessed: 27 may 2024. doi: https://doi.org/10.18103/mra.v11i8.4287.
Section
Research Articles

References

1. Hasanzad M, Aghaei Meybodi HR, Sarhangi N, Larijani B. Artificial intelligence perspective in the future of endocrine diseases. J Diabetes Metab Disord. 2022;21(1):971-978. J Diabetes Metab Disord. 2022;21(1):971-978.
2. Gubbi S, Hamet P, Tremblay J, Koch CA, Hannah-Shmouni F. Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era. Front Endocrinol (Lausanne). 2019;10:185. doi: 10.3389/fendo.2019.00185. PMID: 30984108; PMCID: PMC6448412.
3. London AJ. Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care? Cell Rep Med. 2022;3(5):100622. doi: 10.1016/j.xcrm.2022.100622. Epub 2022 Apr 27. PMID: 35584620; PMCID: PMC9133460.
4. Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023;388(13):1201-1208. doi: 10.1056/NEJMra2302038. PMID: 36988595.
5. Sridhar GR, Lakshmi G. Artificial Intelligence in Medicine: Diabetes as a Model. In: Srinivasa K.G., G. M. S., Sekhar S.R.M. (eds) Artificial Intelligence for Information Management: A Healthcare Perspective. Studies in Big Data, vol 88. Singapore, Springer 2021: 88. https://doi.org/10.1007/978-981-16-0415-7_14
6. Vellido A. Societal Issues Concerning the Application of Artificial Intelligence in Medicine. Kidney Dis (Basel). 2019;5:11-17. doi: 10.1159/000492428. Epub 2018 Sep 3. PMID: 30815459; PMCID: PMC6388581.
7. Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, Shetty S, Rai BP, Chlosta P, Somani BK. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front Surg. 2022;9:862322. doi: 10.3389/fsurg.2022.862322. PMID: 35360424; PMCID: PMC8963864.
8. Sabatello M, Martschenko DO, Cho MK, Brothers KB. Data sharing and community-engaged research. Science. 2022;378(6616):141-143. doi: 10.1126/science.abq6851. Epub 2022 Oct 13. PMID: 36227983.
9. Tully MP, Bozentko K, Clement S, Hunn A, Hassan L, Norris R, Oswald M, Peek N. Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens' Juries. J Med Internet Res. 2018;20:e112. doi: 10.2196/jmir.7763. PMID: 29592847; PMCID: PMC5895919.
10. Sara Gerke, Timo Minssen, Glenn Cohen. Ethical and legal challenges of artificial intelligence-driven healthcare, In (Ed) Adam Bohr, Kaveh Memarzadeh. Artificial Intelligence in Healthcare, Academic Press,2020,295-336,https://doi.org/10.1016/B978-0-12-818438-7.00012-5.
11. Stahl, B.C. (2021). Ethical Issues of AI. In: Artificial Intelligence for a Better Future. SpringerBriefs in Research and Innovation Governance. 2021; Springer, Cham. https://doi.org/10.1007/978-3-030-69978-9_4
12. Price II WN. Artificial intelligence in the medical system: four roles for potential transformation. Yale J Law Technol 2019;212:122-32. https://balkin.blogspot.com/2018/10/four-roles-for-artificial-intelligence.html
13. Koonrungsesomboon N, Hirayama K. Editorial: Ethical and regulatory challenges in genetic and genomic research involving stored biological specimens. Front Genet. 2022;13:1062188. doi: 10.3389/fgene.2022.1062188. PMID: 36338956; PMCID: PMC9634577.
14. Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial Intelligence Applications in Health Care Practice: Scoping Review. J Med Internet Res. 2022;24:e40238. doi: 10.2196/40238. PMID: 36197712; PMCID: PMC9582911.
15. Chikhaoui E, Alajmi A, Larabi-Marie-Sainte S. Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges. Emerging Science J 2022;6:717-37 Doi: 10.28991/ESJ-2022-06-04-05
16. Ursin F, Timmermann C, Orzechowski M, Steger F. Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent? Front Med (Lausanne). 2021;8:695217. doi: 10.3389/fmed.2021.695217. PMID: 34368192; PMCID: PMC8333706.
17. Saheb T. "Ethically contentious aspects of artificial intelligence surveillance: a social science perspective". AI Ethics. 2022:1-11. doi: 10.1007/s43681-022-00196-y. Epub ahead of print. PMID: 35874304; PMCID: PMC9294797.
18. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021;22:122. doi: 10.1186/s12910-021-00687-3. PMID: 34525993; PMCID: PMC8442400.
19. Knight HE, Deeny SR, Dreyer K, Engmann J, Mackintosh M, Raza S, Stafford M, Tesfaye R, Steventon A. Challenging racism in the use of health data. Lancet Digit Health. 2021;3:e144-e146. doi: 10.1016/S2589-7500(21)00019-4. Epub 2021 Feb 3. PMID: 33549513.
20. Pham Q, Gamble A, Hearn J, Cafazzo JA. The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. J Med Internet Res. 2021;23:e22320. doi: 10.2196/22320. PMID: 33565982; PMCID: PMC7904401.
21. Amann J, Blasimme A, Vayena E, Frey D, Madai VI; Precise4Q consortium. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20:310. doi: 10.1186/s12911-020-01332-6. PMID: 33256715; PMCID: PMC7706019.
22. Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang GZ. XAI-Explainable artificial intelligence. Sci Robot. 2019 ;4):eaay7120. doi: 10.1126/scirobotics.aay7120. PMID: 33137719.
23. van der Veer SN, Riste L, Cheraghi-Sohi S, Phipps DL, Tully MP, Bozentko K, Atwood S, Hubbard A, Wiper C, Oswald M, Peek N. Trading off accuracy and explainability in AI decision-making: findings from 2 citizens' juries. J Am Med Inform Assoc. 2021;28:2128-2138. doi: 10.1093/jamia/ocab127. PMID: 34333646; PMCID: PMC8522832.
24. Brown J. IBM Watson reportedly recommended cancer treatments that were ‘unsafe and incorrect’. Gizmodo, https://gizmodo.com/ibm-watson-reportedly-recommended-cancer-treatments-tha-1827868882; 2018
25. Kawamleh, Suzanne. (2022). Against explainability requirements for ethical artificial intelligence in health care. AI and Ethics. 2022; 1-16. 10.1007/s43681-022-00212-1.
26. Refolo P, Sacchini D, Raimondi C, Spagnolo AG. Ethics of digital therapeutics (DTx). Eur Rev Med Pharmacol Sci. 2022;26:6418-6423. doi: 10.26355/eurrev_202209_29741. PMID: 36196692.
27. Solanki, P., Grundy, J. & Hussain, W. Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers. AI Ethics; 2022. https://doi.org/10.1007/s43681-022-00195-z
28. Meszaros Janos, Minari Jusaku, Huys Isabelle. The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union. Frontiers in Genetics.13:2022. URL=https://www.frontiersin.org/articles/10.3389/fgene.2022.927721.DOI=10.3389/fgene.2022.927721
29. Shandhi MMH, Dunn JP. AI in medicine: Where are we now and where are we going? Cell Rep Med. 2022;3:100861. doi: 10.1016/j.xcrm.2022.100861. PMID: 36543109.