Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism

The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.

Automated summary

The use of AI and ML in clinical care shows promise in improving patient health outcomes and reducing health disparities, but inherent biases and potential risks of harm need to be addressed. Developing multi-modal workflows to minimize limitations and considering rapidly evolving AI technologies, expanding data sources, and changing regulatory landscapes are crucial in enhancing clinical decision making and reducing health inequity.