Social and Ethical Implications

As AI does offer many benefits, it also can raise some very crucial social and ethical questions. With AI, also comes the possibility of algorithmic bias, which is a critical liability. This stems from the fact that if the extensive datasets used to train these models are incomplete or reflect historical prejudices, the resulting AI can have a negative impact on marginalized populations and eventually lead to unfair treatment outcomes (Chusteki, n.d.). For example, if there are diagnostic algorithms that are predominately trained for white populations, then this algorithm may underperform when applied to people of color, which can lead to false diagnoses and delayed treatment. This is an ethical failure that continues to reinforce the already existing disparities that are seen in health care facilities today. This technical flaw translates directly into an ethical failure by reinforcing existing disparities in care.

Furthermore, there has also arisen the concern of the detachment of patient relationships with their providers, due to a distinct lack of human interaction (Akingbola, 2024). This shift risks eroding the essential, empathetic role of healthcare built on the foundation of empathy, trust, and shared understanding. For AI to be widely accepted by more people, building trust is crucial to effective patient-centered care. Surveys have clearly shown that while many patients see potential in the use of AI, there are also many who remain skeptical of how certain decisions are made and, critically, who is accountable for those errors (Nong, 2025). This ambiguity—whether the error falls to the clinician, the manufacturer, or the institution—creates a profound legal liability, an ethical gray area. Until there are clear frameworks for assigning responsibility, this accountability gap will continue to undermine confidence in the technology. Healthcare systems must prioritize inclusivity, transparency, and ethical oversight. This also includes diversifying datasets, incorporating clearer AI models, and establishing more defined structures that promote responsibility. When these challenges are handled, then we are able to ensure that AI actually enhances the humanity of healthcare with integrity, equity, and humanity.