Algorithmic bias is a significant concern. AI systems learn from the data they are trained on. If the training datasets are not representative of the diverse patient population, or if they contain historical biases related to race, gender, socioeconomic status, or geographic location, the AI can perpetuate or even amplify these disparities. This can lead to differential quality of care, misdiagnosis, or inappropriate treatment for marginalized groups. Ensuring fairness requires careful data curation, bias detection, and mitigation strategies throughout the AI lifecycle.

The development and operation of healthcare AI systems necessitate the collection, storage, and processing of large volumes of sensitive personal health information (PHI). Protecting this data from cyber threats, ensuring compliance with privacy regulations (such as HIPAA in the US or GDPR in Europe), and obtaining informed consent for data usage are critical. The potential for re-identification of anonymized data also poses a privacy risk that must be addressed.

Establishing clear lines of accountability when an AI system contributes to an adverse patient outcome is a significant ethical and legal hurdle. Is the AI developer responsible for faulty algorithms? Is the clinician accountable for relying on AI recommendations? Or is the healthcare institution liable for deploying the system? This ambiguity can hinder the adoption of AI and create challenges in addressing patient harm.

Many advanced AI models, particularly deep learning systems, operate as 'black boxes,' meaning their internal workings and decision-making logic are not easily interpretable by humans. This lack of explainability can erode trust among clinicians and patients, making it difficult to validate AI outputs, identify errors, or understand the rationale behind a diagnosis or treatment suggestion. Efforts in 'explainable AI' (XAI) aim to address this.