Navigating the Ethical Landscape: Implementing Machine Learning in Smart Healthcare Informatics

Authors

DOI:

https://doi.org/10.47203/IJCH.2024.v36i01.024

Keywords:

Machine Learning, Smart Healthcare, Ethical Considerations, Ethical Challenges, ML

Abstract

The integration of Machine Learning (ML) into healthcare informatics holds immense promise, revolutionizing patient care and treatment strategies. However, as this technology advances, it brings forth ethical challenges crucial for careful navigation. ML offers unprecedented abilities to analyze vast healthcare data, leading to personalized medicine and improved outcomes. Yet, ethical concerns emerge, notably in privacy protection, algorithm bias, transparency, informed consent, and data quality. Transparency, explainability, and patient autonomy in decision-making processes are crucial to foster trust and accountability. Striking a balance between innovation and compliance, ensuring data quality, and promoting human-AI collaboration are essential. Addressing these challenges demands adherence to ethical frameworks, continuous monitoring, multidisciplinary governance, education, and regulatory compliance. To fully harness ML's potential in healthcare while upholding ethical standards, collaboration among stakeholders is imperative, ensuring patient welfare remains central amid technological advancements. Ethical considerations must be embedded at every stage of ML implementation to maintain an ethical, equitable, and patient-centered healthcare system.

Downloads

Download data is not yet available.

References

Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks,2022; 3:58-73.

Masood, I., Wang, Y., Daud, A., Aljohani, N. R., & Dawood, H. (2018). Towards smart healthcare: patient data privacy and security in sensor-cloud infrastructure. Wireless Communications and Mobile Computing, 2018:1-23.

Gaonkar, B., Cook, K., & Macyszyn, L. Ethical issues arising due to bias in training AI algorithms in healthcare and data sharing as a potential solution. The AI Ethics Journal, 2020;1(1):2

Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. Explainable, trustworthy, and ethical machine learning for healthcare: A survey. Computers in Biology and Medicine. 2022;149 (Oct) 106043.

Bharati, S., Mondal, M. R. H., Podder, P., & Kose, U. Explainable Artificial Intelligence (XAI) with IoHT for Smart Healthcare: A Review. Interpretable Cognitive Internet of Things for Healthcare, 2023;1-24.

Sqalli, M. T., Al-Thani, D., Qaraqe, M., & Fernandez-Luque, L. Perspectives on human-AI interaction applied to health and wellness management: Between milestones and hurdles. In Multiple Perspectives on Artificial Intelligence in Healthcare: Opportunities and Challenges 2021:41-51. Cham: Springer International Publishing.

Thapa, C., & Camtepe, S. Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in biology and medicine, 2021;129, 104130.

Shaikh, T. A., Dar, T. R., & Sofi, S. data-centric artificial intelligent and extended reality technology in smart healthcare systems. Social Network Analysis and Mining, 2022;12(1), 122.

Motwani, A., Shukla, P. K., & Pawar, M. Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review. Artificial Intelligence in Medicine, 2022;102431

Al Amin, M., Altarawneh, A., & Ray, I. Informed consent as patient driven policy for clinical diagnosis and treatment: A smart contract based approach. In Proceedings of the 20th International Conference on Security and Cryptography-SECRYPT 2023;159-170.

Downloads

Published

2024-02-29

How to Cite

1.
Sharma AK, Sharma R. Navigating the Ethical Landscape: Implementing Machine Learning in Smart Healthcare Informatics. Indian J Community Health [Internet]. 2024 Feb. 29 [cited 2024 Jul. 24];36(1):149-52. Available from: https://www.iapsmupuk.org/journal/index.php/IJCH/article/view/2799

Issue

Section

Perspective