Exploring the Ethical Dimensions of AI in Healthcare
In the face of mounting challenges such as increased patient loads, staffing shortages, and rising healthcare costs, hospitals are turning to artificial intelligence (AI) as a potential solution. However, as AI becomes more integral to healthcare operations, the ethical implications of its use cannot be ignored. A recent systematic review assesses how current literature on hospital-focused AI aligns with the World Health Organization’s (WHO) six ethical AI principles: autonomy, well-being and safety, transparency and explainability, responsibility and accountability, inclusivity and justice, and responsiveness and sustainability.
Methods and Analysis
This review, registered under PROSPERO (CRD42022347871), analyzed studies from databases including Embase, MEDLINE ALL, Web of Science, and the Cochrane Central Register of Controlled Trials, from their inception until December 2023, with additional insights from Google Scholar. The focus was on English-language studies that discuss AI technologies—such as machine learning, deep learning, and predictive analytics—within inpatient settings, specifically those relating to at least one WHO ethical principle. Two reviewers conducted independent screenings of titles, abstracts, and full texts. They also extracted data regarding publication year, country, study design, AI type, technology readiness level, and ethical considerations, resolving any disagreements through consensus.
Key Findings
Out of 4770 unique records, 673 studies were included in the review. An overwhelming majority (83%) originated from high-income countries, with a noticeable surge in publications post-2021. Among these, 558 studies (83%) addressed at least one WHO principle in depth. The most frequently discussed principles were inclusiveness and equity (49%), transparency and explainability (45%), and autonomy (42%). However, areas like well-being and safety (26%) and responsibility and accountability (29%) were less frequently covered. Responsiveness and sustainability were the least examined, featuring in only 6% of the studies. Of note, within the 44 studies concentrating on AI applications at technology readiness levels 1-6, ethical principles were acknowledged but seldom implemented.
Conclusion
The review highlights a growing awareness of ethical considerations in AI research within hospitals. Nonetheless, the application of these principles, particularly regarding sustainability, remains limited. The predominance of high-income countries in this discourse points to a critical need for more inclusive global participation. To achieve equitable, safe, and sustainable AI in clinical practice, there is a pressing demand for more defined operational guidelines and collaborative efforts across diverse regions. For further details, the complete study can be accessed Here.
“`
