Artificial intelligence technologies are rapidly advancing and significantly impacting healthcare, particularly in critical care environments where rapid, precise decision-making is crucial. They promise reductions in clinical errors, enhanced diagnostic accuracy, optimized treatment plans, and better resource allocation. Artificial intelligence applications are widespread across medical fields, with numerous artificial intelligence/machine learning-enabled medical devices approved by regulatory bodies, like the US Food and Drug Administration, aiding in diagnosis, monitoring, and personalized patient care. However, integrating artificial intelligence into healthcare presents challenges, notably the potential to exacerbate existing biases and disparities, especially when systems are trained on homogeneous datasets lacking diversity. Biased artificial intelligence can negatively affect patient outcomes for underrepresented groups, perpetuating health disparities. Additional concerns include data privacy and security, lack of transparency, algorithmic bias, and regulatory hurdles. Addressing these risks requires ensuring diverse and representative datasets, implementing robust auditing and monitoring practices, enhancing transparency, involving diverse perspectives in artificial intelligence development, and promoting critical thinking among healthcare professionals. Furthermore, the environmental impact of artificial intelligence, huge models reliant on energy-intensive data centers, poses challenges due to increased greenhouse gas emissions and resource consumption, disproportionately affecting low-income countries and exacerbating global inequalities. Systemic changes driven by corporate responsibility, government policy, and adopting sustainable artificial intelligence practices within healthcare are necessary. This narrative review explores the current landscape of artificial intelligence in healthcare, highlighting its potential benefits and delineating associated risks and challenges, underscoring the importance of mitigating biases and environmental impacts to ensure equitable and sustainable integration of artificial intelligence technologies in healthcare settings.
Woite et al. (Wed,) studied this question.