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Abstract: AI presents a transformative potential in healthcare, enhancing patient care, diagnosis, and treatment via heightened accuracy, speed, and personalization. Yet, to have responsible implementations, ethics issues including privacy, bias, accountability, and transparency should be taken into consideration as well. AI models, by nature, require vast amounts of data, and thus, there are concerns about how sensitive medical information can be secured. If this bias is embedded in training data, the result is disparity, with understudied groups facing the consequences and the widening of the inequality gap within the health sector. If an AI-based decision goes wrong, we need to ensure that we have systems in place where people are held accountable for mistakes and negative consequences. So, the AI systems are “black boxes”, which means that their use is filled with transparency threats, and thus, explainable AI (XAI) is required to build trust. Furthermore, unequal accessibility to AI technologies can worsen the global health gap, and that is another very important issue to be addressed. If we want to make sure that AI does serve to enhance the delivery of healthcare in this manner, technologists, the healthcare workforce, and policymakers, among others with a stake in healthcare, will need to work to build a governance framework around it that maintains the ethical integrity of the health system.