What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems

The application of machine learning (ML) in medicine has seen success, yet its foundational epistemic and methodological warrants remain uncertain. Research proposes a generative analogy between clinical translation processes and ML system development, suggesting that the established standards from medical practice could inform and improve the reliability and trustworthiness of ML systems.

Why it matters

The increasing reliance on advanced algorithmic systems across various sectors necessitates robust frameworks for their development and deployment. Adopting principles from a highly regulated domain like medicine could provide a strategic blueprint for establishing trust, ensuring reliability, and justifying the outputs of complex AI systems in other critical applications, thereby mitigating risks and fostering broader adoption.

Key insights

• Machine learning has been widely and successfully implemented in the medical domain.
• Significant uncertainties persist regarding the epistemic (knowledge-related) and methodological (process-related) warrants for ML systems.
• A parallel has been identified between medicine and ML, advocating for the adoption of clinical translation standards for ML development.
• The proposed parallel is characterized as a 'generative analogy,' leveraging existing frameworks to establish robust standards for ML.
• The analysis seeks to precisely identify which epistemic and methodological warrants from clinical translation are most relevant to building reliable ML systems.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.18186