Artificial Intelligence in Healthcare: Unveiling Ethical Challenges Through Meta-synthesis of Evidence

Background: With the rapid advancement of artificial intelligence (AI) technologies in healthcare systems, alongside their numerous benefits, various ethical concerns have emerged regarding their application in different areas.This study aimed to identify the ethical challenges associated with the implementation of AI in healthcare. Methods:This research employed a qualitative meta-synthesis method using a thematic analysis approach.In accordance with the preferred reporting items for systematic reviews and metaanalyses (PRISMA) guidelines, qualitative and review studies published between 2010 and 2025 that addressed ethical issues related to AI applications in healthcare were analyzed.The quality of the included studies was assessed using the critical appraisal skills programme (CASP) checklist, and confidence in the findings was evaluated based on the grading of recommendations assessment, development and evaluation-confidence in the evidence from reviews of qualitative research (GRADE-CERQual) approach.Results: Out of 38 selected studies, seven main themes and 33 subthemes were identified.The key challenges included risks to privacy and data security, limited transparency and explainability, algorithmic bias, undermining the autonomy of patients and healthcare professionals, interference with professional responsibilities, reduced quality of clinical judgment, and regulatory and legal gaps.Furthermore, the commercial use of health data and the absence of integrated ethical frameworks have exacerbated concerns related to justice, public trust, and human-centered care. Conclusion:The findings of this study indicate that the ethical and evidence-based integration of AI into healthcare requires the development of transparent regulatory frameworks, the enhancement of ethical digital literacy within the medical profession, and the formulation of comprehensive policies to protect patient rights and promote health equity.These results can serve as a strategic foundation for decision-making by policymakers, technology developers, and clinical professionals.