Ethical Implications and Educational Integration of AI-Driven Predictive Analytics in Healthcare: A Comprehensive Review
Journal Title: Healthcraft Frontiers - Year 2024, Vol 2, Issue 2
Abstract
This comprehensive review investigates the ethical implications of artificial intelligence (AI)-driven predictive analytics in healthcare, with a focus on patient privacy, algorithmic bias, equitable access, and transparency. The study further explores the integration of these ethical considerations into educational frameworks to enhance the training and preparedness of healthcare professionals in the responsible use of AI technologies. A systematic literature review was conducted using databases such as PubMed, Scopus, and Google Scholar, employing keywords related to AI, predictive analytics, healthcare, education, and ethics. Articles published from 2017 onwards, discussing the ethical challenges and applications of AI in healthcare and educational settings, were included. Thematic analysis of selected articles revealed significant ethical concerns, including patient privacy, algorithmic bias, and equitable access to AI technologies. Findings underscored the necessity for robust data protection mechanisms, transparent algorithm development, and equitable access policies. The study also highlighted the importance of incorporating AI literacy and ethical training in medical education. An ethical framework was proposed, outlining strategies to address these challenges in both healthcare practice and educational curricula. This framework aims to ensure the responsible use of AI technologies, promote transparency, and mitigate biases in healthcare settings. By addressing a critical gap in understanding the ethical implications of AI-driven predictive analytics in healthcare and its integration into education, the study contributes to the development of guidelines and policies for the equitable and transparent deployment of AI. The proposed ethical framework provides actionable recommendations for stakeholders, aiming to enhance medical education and improve patient outcomes while upholding essential ethical principles.
Authors and Affiliations
Askar Garad, Budiman AL Iman, Halim Purnomo
Keywords
Related Articles
- EP ID EP744654
- DOI https://doi.org/10.56578/hf020204
- Views 23
- Downloads 0
Ethical Implications and Educational Integration of AI-Driven Predictive Analytics in Healthcare: A Comprehensive Review
This comprehensive review investigates the ethical implications of artificial intelligence (AI)-driven predictive analytics in healthcare, with a focus on patient privacy, algorithmic bias, equitable access, and tran...
Evaluation of Factors Contributing to Potential Drug-Drug Interactions in Cardiovascular Disease Management: A Retrospective Study
A retrospective analysis was conducted to assess potential drug-drug interactions (pDDIs) in the management of cardiovascular diseases, evaluating 500 prescriptions from hospitalized patients between January 1 and Apri...
Enhancing Fall Risk Assessment in the Elderly: A Study Utilizing Transfer Learning in an Improved EfficientNet Network with the Gramian Angular Field Technique
Recent years have seen a significant increase in the incidence of falls among the elderly, leading to accidental injuries and fatalities. This trend underscores the critical need for accurate fall risk assessment, a majo...
Pneumonia Detection Technique Empowered with Transfer Learning Approach
Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep l...
A CNN Approach for Enhanced Epileptic Seizure Detection Through EEG Analysis
Epilepsy, the most prevalent neurological disorder, is marked by spontaneous, recurrent seizures due to widespread neuronal discharges in the brain. This condition afflicts approximately 1% of the global population, with...