J Korean Diabetes.  2020 Sep;21(3):126-129. 10.4093/jkd.2020.21.3.126.

Medical Ethics in the Era of Artificial Intelligence Based on Medical Big Data

• Na HR
• Kim HS

• KMID: 2507043
• DOI: http://doi.org/10.4093/jkd.2020.21.3.126

Abstract

When incorporating artificial intelligence (AI) based on medical big data into the clinical and research settings, it is important to consider the associated ethical philosophy in addition to medical behavior. Simply improving the processing speed and increasing the amount of data will not suffice. Instead, it is necessary to continuously provide a direction for AI progress in medical algorithms that are required in order to make use of medical big data. To integrate AI with healthcare research and medical practice, it is essential that AI algorithms are reviewed by experienced medical staff. Additionally, the question regarding which levels of data can or cannot be trusted by medical staff needs to be answered. AI algorithms are best suited to provide assistance (decision-supporting) during the decision-making process. Hence, if more AI algorithms are implemented through such a series of processes, skilled medical personnel can play large roles, and their roles can be subcategorized. Furthermore, based on the medical value of AI, health care providers should have a role in determining the reasonableness and suitability of AI algorithms.

Keyword

Artificial intelligence; Big data; Medical ethics

Reference

1. Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health. 2018; 15:2796.

2. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018; 2:719–31.

3. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018; 71:2668–79.

4. Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence- and machine learning-based software devices in medicine. JAMA. 2019; 322:2285–6.

5. Kim HS. Decision-making in artificial intelligence: is it always correct? J Korean Med Sci. 2020; 35:e1.

6. Coiera E. The price of artificial intelligence. Yearb Med Inform. 2019; 28:14–5.

7. Abràmoff MD, Tobey D, Char DS. Lessons learned about autonomous AI: finding a safe, efficacious, and ethical path through the development process. Am J Ophthalmol. 2020; 214:134–42.

8. Grzybowski A, Brona P. A pilot study of autonomous artificial intelligence-based diabetic retinopathy screening in Poland. Acta Ophthalmol. 2019; 97:e1149–50.

9. Kim H, Lee H, Kim TM, Yang SJ, Baik SY, Lee SH, et al. Change in ALT levels after administration of HMG-CoA reductase inhibitors to subjects with pretreatment levels three times the upper normal limit in clinical practice. Cardiovasc Ther. 2018; 36:e12324.

10. London AJ. Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep. 2019; 49:15–21.

11. Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018; 15:e1002711.

12. Tsakiridis NL, Diamantopoulos T, Symeonidis AL, Theocharis JB, Iossifides A, Chatzimisios P, et al. Versatile internet of things for agriculture: an eXplainable AI approach. Maglogiannis I, Iliadis L, Pimenidis E, editors. Artificial intelligence applications and innovations. Cham: Springer;2020. p. 180–91.

13. Kim SG, Theera-Ampornpunt N, Fang CH, Harwani M, Grama A, Chaterji S. Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions. BMC Syst Biol. 2016; 10 Suppl 2(Suppl 2):54.

14. The Lancet Respiratory Medicine. Opening the black box of machine learning. Lancet Respir Med. 2018; 6:801.

15. Kim HS, Kim JH. Proceed with caution when using real world data and real world evidence. J Korean Med Sci. 2019; 34:e28.

16. Kim HS, Kim DJ, Yoon KH. Medical big data is not yet available: why we need realism rather than exaggeration. Endocrinol Metab (Seoul). 2019; 34:349–54.

17. Keskinbora KH. Medical ethics considerations on artificial intelligence. J Clin Neurosci. 2019; 64:277–82.