Bias, Transparency, and Patient Harm in Clinical AI: Ethical Failures and Governance Solutions

Abstract

The growing use of artificial intelligence (AI) in the clinical decision-making process has also created a critical ethical dilemma concerning bias, transparency, and patient safety. Although clinical AI systems have been found to offer better diagnostic precision and efficiency, the large-scale nature of the data they use and intricate algorithms have revealed systemic risks such as algorithmic bias, lack of explainability, and lack of accountability. Any bias within training data and model design might be used to support existing health disparities and disproportionately impact marginalized groups of patients. Concurrently, the lack of transparency in most AI systems compromises clinical trust, informed consent and the capacity of health care professionals to make any meaningful evaluation of AI-driven recommendations. These weaknesses significantly increase the possibility of harm to the patients associated with misdiagnosis, improper treatment choices, and disproportionate quality of care. This paper will explore major ethical shortcomings related to the use of clinical AI and focus on such concerns as bias and the lack of transparency and discuss the implication of these issues on patient safety. It also examines the problem of governance and regulatory proposals that can reduce these risks, such as ethical-by-design solutions, algorithmic audits, requirements on explainability, and human-in-the-loop controls. It is also necessary to strengthen the structures of governance to make sure that clinical AI systems can be safe, equitable, and in line with the fundamental medical ethics.