Ethical Considerations in the Development of AI-Powered Healthcare Assistants

Authors

  • Jett Lee Willson IJIIS Incorporation
  • Asher Nuche IJIIS Incorporation
  • Riya Widayanti Esa Unggul University

DOI:

https://doi.org/10.33050/itee.v2i2.566

Keywords:

artificial intelligence, Health assistants, Ethical, Considerations, Healthcare

Abstract

Advances in the field of artificial intelligence (AI) have led to the development of increasingly sophisticated health assistants that can provide support in diagnosis, treatment and general health management. However, as with the use of new technologies in the healthcare context, ethical considerations play an important role in the design, development, and implementation of AI-based health assistants. In this paper, we investigate various ethical considerations associated with the development of AI-based healthcare assistants. We explore issues such as the privacy and security of patient data, transparency and accountability in decision making, and the social and psychological impact of reliance on technology in the healthcare context. We also discuss efforts that can be taken to address these ethical challenges, including the development of appropriate regulatory guidelines, ongoing monitoring of system performance, and education and training for health professionals and end users. By seriously considering ethical aspects in the development of AI-based healthcare assistants, we hope to ensure that this technology can provide maximum benefit to patients while maintaining the ethical and moral values that underlie good healthcare practices.

References

S. F. S. Alhashmi, M. Alshurideh, B. Al Kurdi, and S. A. Salloum, “A systematic review of the factors affecting the artificial intelligence implementation in the health care sector,” in Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), Springer, 2020, pp. 37–49.

P. Manickam et al., “Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare,” Biosensors (Basel), vol. 12, no. 8, p. 562, 2022.

J. Yang, B. Luo, C. Zhao, and H. Zhang, “Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework,” Digit Health, vol. 8, p. 20552076221126030, 2022.

S. Secinaro, D. Calandra, A. Secinaro, V. Muthurangu, and P. Biancone, “The role of artificial intelligence in healthcare: a structured literature review,” BMC Med Inform Decis Mak, vol. 21, pp. 1–23, 2021.

D. S. S. Wuisan, R. A. Sunardjo, Q. Aini, N. A. Yusuf, and U. Rahardja, “Integrating Artificial Intelligence in Human Resource Management: A SmartPLS Approach for Entrepreneurial Success,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 3, pp. 334–345, 2023.

P. Kumar, Y. K. Dwivedi, and A. Anand, “Responsible artificial intelligence (AI) for value formation and market performance in healthcare: The mediating role of patient’s cognitive engagement,” Information Systems Frontiers, vol. 25, no. 6, pp. 2197–2220, 2023.

A. Abera et al., “Air quality in Africa: Public health implications,” Annu Rev Public Health, vol. 42, pp. 193–210, 2021.

A. Rachmawati, “Analysis of Machine Learning Systems for Cyber Physical Systems,” International Transactions on Education Technology, vol. 1, no. 1, pp. 1–9, 2022.

D. Jonas, E. Maria, I. R. Widiasari, U. Rahardja, T. Wellem, and others, “Design of a TAM Framework with Emotional Variables in the Acceptance of Health-based IoT in Indonesia,” ADI Journal on Recent Innovation, vol. 5, no. 2, pp. 146–154, 2024.

P. Kumar, S. K. Sharma, and V. Dutot, “Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation,” Int J Inf Manage, vol. 69, p. 102598, 2023.

U. Rahardja, Q. Aini, A. S. Bist, S. Maulana, and S. Millah, “Examining the interplay of technology readiness and behavioural intentions in health detection safe entry station,” JDM (Jurnal Dinamika Manajemen), vol. 15, no. 1, pp. 125–143, 2024.

G. Syuhada et al., “Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia,” Int J Environ Res Public Health, vol. 20, no. 4, p. 2916, 2023.

J. Amann, A. Blasimme, E. Vayena, D. Frey, V. I. Madai, and P. Consortium, “Explainability for artificial intelligence in healthcare: a multidisciplinary perspective,” BMC Med Inform Decis Mak, vol. 20, pp. 1–9, 2020.

B. E. Sibarani, “Smart Farmer Sebagai Optimalisasi Digital Platform Dalam Pemasaran Produk Pertanian Pada Masa Pandemi Covid-19,” Technomedia Journal, vol. 6, no. 1, pp. 43–55, 2021.

U. Rahardja, S. Sudaryono, N. P. L. Santoso, A. Faturahman, and Q. Aini, “Covid-19: Digital Signature Impact on Higher Education Motivation Performance,” International Journal of Artificial Intelligence Research, vol. 4, no. 1, May 2020, doi: 10.29099/ijair.v4i1.171.

P. A. Sunarya, F. Andriyani, Henderi, and U. Rahardja, “Algorithm automatic full time equivalent, case study of health service,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 1.5 Special Issue, pp. 387–391, 2019, doi: 10.30534/ijatcse/2019/6281.52019.

Q. Aini, I. Sembiring, A. Setiawan, I. Setiawan, and U. Rahardja, “Perceived Accuracy and User Behavior: Exploring the Impact of AI-Based Air Quality Detection Application (AIKU),” Indonesian Journal of Applied Research (IJAR), vol. 4, no. 3, pp. 209–218, 2023.

W. Fan, J. Liu, S. Zhu, and P. M. Pardalos, “Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS),” Ann Oper Res, vol. 294, no. 1, pp. 567–592, 2020.

B. A. Stauffer et al., “Considerations in harmful algal bloom research and monitoring: perspectives from a consensus-building workshop and technology testing,” Front Mar Sci, vol. 6, p. 399, 2019.

M. Upreti, C. Pandey, A. S. Bist, B. Rawat, and M. Hardini, “Convolutional Neural Networks in Medical Image Understanding,” Aptisi Transactions on Technopreneurship (ATT), vol. 3, no. 2, pp. 6–12, 2021.

T. Palit, A. B. M. M. Bari, and C. L. Karmaker, “An integrated Principal Component Analysis and Interpretive Structural Modeling approach for electric vehicle adoption decisions in sustainable transportation systems,” Decision Analytics Journal, vol. 4, p. 100119, 2022.

M. Chen, J. Yang, L. Hu, M. Shamim Hossain, and G. Muhammad, “Urban Healthcare Big Data System Based on Crowdsourced and Cloud-Based Air Quality Indicators,” IEEE Communications Magazine, vol. 56, no. 11, pp. 14–20, 2018, doi: 10.1109/MCOM.2018.1700571.

P. Friedlingstein et al., “Global carbon budget 2020,” Earth System Science Data Discussions, vol. 2020, pp. 1–3, 2020.

U. Rahardja, “Risk Assessment, Risk Identification, and Control in The Process Of Steel Smelting Using the Hiradc Method,” APTISI Transactions on Management, vol. 7, no. 3, pp. 261–272, 2023.

J. F. Hair Jr, M. C. Howard, and C. Nitzl, “Assessing measurement model quality in PLS-SEM using confirmatory composite analysis,” J Bus Res, vol. 109, pp. 101–110, 2020.

K. Kwong-Kay Wong, “Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS,” Marketing Bulletin, vol. 24, no. 1, pp. 1–32, 2013.

T. Hariguna, U. Rahardja, and Q. Aini, “The antecedent e-government quality for public behaviour intention, and extended expectation-confirmation theory,” Computer Science and Information Technologies, vol. 4, no. 1, pp. 33–42, 2023.

J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” European Business Review, vol. 31, no. 1, pp. 2–24, 2019, doi: 10.1108/EBR-11-2018-0203.

M. Sarstedt, J. F. Hair Jr, C. Nitzl, C. M. Ringle, and M. C. Howard, “Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!,” International Journal of Market Research, vol. 62, no. 3, pp. 288–299, 2020.

R. R. Ahmed, D. Štreimikienė, and J. Štreimikis, “The extended UTAUT model and learning management system during COVID-19: evidence from PLS-SEM and conditional process modeling,” Journal of Business Economics and Management, vol. 23, no. 1, pp. 82–104, 2022.

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Published

2024-05-07

How to Cite

Willson, J. L., Nuche, A., & Widayanti, R. (2024). Ethical Considerations in the Development of AI-Powered Healthcare Assistants. International Transactions on Education Technology (ITEE), 2(2), 109–119. https://doi.org/10.33050/itee.v2i2.566