The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review

Abstract

Artificial intelligence (AI) is being increasingly applied in healthcare. The expansion of AI in healthcare necessitates AI-related ethical issues to be studied and addressed. This systematic scoping review was conducted to identify the ethical issues of AI application in healthcare, to highlight gaps, and to propose steps to move towards an evidence-informed approach for addressing them. A systematic search was conducted to retrieve all articles examining the ethical aspects of AI application in healthcare from Medline (PubMed) and Embase (OVID), published between 2010 and July 21, 2020. The search terms were “artificial intelligence” or “machine learning” or “deep learning” in combination with “ethics” or “bioethics”. The studies were selected utilizing a PRISMA flowchart and predefined inclusion criteria. Ethical principles of respect for human autonomy, prevention of harm, fairness, explicability, and privacy were charted. The search yielded 2166 articles, of which 18 articles were selected for data charting on the basis of the predefined inclusion criteria. The focus of many articles was a general discussion about ethics and AI. Nevertheless, there was limited examination of ethical principles in terms of consideration for design or deployment of AI in most retrieved studies. In the few instances where ethical principles were considered, fairness, preservation of human autonomy, explicability and privacy were equally discussed. The principle of prevention of harm was the least explored topic. Practical tools for testing and upholding ethical requirements across the lifecycle of AI-based technologies are largely absent from the body of reported evidence. In addition, the perspective of different stakeholders is largely missing.

Ethical Implications of Artificial Intelligence in the Healthcare Sector

“…Researchers argue that patients should have greater control over their data and how it is used, while others explore current regulations and the need for obtaining patient consent before sharing information. In the USA, Health Insurance Portability and Accountability Act (HIPAA) allows sharing of protected health information for certain purposes without patient consent, while in the UK, patient consent is required for sharing information with any third party not involved in direct patient care (Karimian et al, 2022). Data privacy is important to protect against discrimination, mental health consequences, erosion of trust, and other harms.…”

“…The literature highlights concerns regarding privacy, trust, accountability and responsibility, and bias, but largely ignores the ethics of AI in public and population health, and in low-and middle-income countries (LMICs). The review concludes that while AI holds promise for improving health systems, its introduction should be approached with caution and further research is needed to ensure its development and implementation is ethical for everyone, everywhere.European Parliament: Directorate General for Parliamentary Research Services (2022) provides an overview of the potential benefits of artificial intelligence (AI) in healthcare, including improving diagnosis and treatment, increasing efficiency, and optimising resource allocation Karimian et al (2022). examined the ethical issues surrounding the increasing use of artificial intelligence (AI) in healthcare.…”

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

“…Some biases are very difficult to identify and address, and it can lead to serious consequences such as discrimination, unfairness, and lack of diversity and inclusivity. 139 Standardized metrics of fairness is another emerging issue of debate since the complexity of the disparities and inequalities in the datasets and algorithms is not an easy process to dissolve. 140 Finally, interpretability and explainability are of paramount importance to gain trustworthiness from the physicians.…”

“…These biases can arise from a variety of factors, such as data collection methods, algorithmic design, and user behavior, among others. Some biases are very difficult to identify and address, and it can lead to serious consequences such as discrimination, unfairness, and lack of diversity and inclusivity 141 . Standardized metrics of fairness is another emerging issue of debate since the complexity of the disparities and inequalities in the datasets and algorithms is not an easy process to dissolve 142 .…”

The Language of Nature and Artificial Intelligence in Patient Care

“…In order to deal with and discuss these ethical issues, there are approaches that address privacy, explainability, and transparency, bias from the technical point of view [63]. Moreover, in recent scientific literature, there are also abundant practical guidelines to guide the development of AI for the greater good of patients and caregivers.…”