The Evolution of AI in Healthcare: Ethical Frameworks and Explainability

The integration of artificial intelligence (AI) into healthcare continues to evolve, with a notable shift towards addressing ethical considerations and enhancing the explainability of AI systems. Recent developments highlight a growing emphasis on the ethical collaboration between AI and human experts, particularly in precision medicine and intensive care settings. This trend underscores the need for AI systems that not only perform efficiently but also align with ethical standards and are interpretable by clinicians. Federated learning and explainable AI (XAI) are emerging as key technologies in overcoming data privacy concerns and improving the transparency of AI models in medical applications. Additionally, the ethical landscape of AI-driven technologies, such as robot-assisted surgery and multi-robot systems, is being systematically reviewed to ensure that these advancements do not compromise patient safety or trust. The field is also witnessing a focus on balancing emotional tensions in the design of healthcare technologies, such as remote patient monitoring, to ensure equitable care for diverse patient groups.

Noteworthy Developments

• Ethical Collaboration Framework: A framework for developing and evaluating ethical collaboration between experts and AI in precision medicine, addressing challenges like generalizability and explainability.
• Federated Learning in Pediatric Echocardiography: The use of federated learning and explainable AI to enhance diagnostic workflows in pediatric echocardiography, overcoming data privacy and transparency issues.
• Emotional Tensions in Remote Patient Monitoring: A qualitative analysis exploring emotional needs in remote patient monitoring, proposing design recommendations to balance emotional tensions and foster health equity.