Bridging the Safety Gap: Integrating AI Foundation Models into Clinical Workflows

The analysis, conducted by an interdisciplinary team at the Else Kröner Fresenius Center for Digital Health, concludes that risks permeate the entire AI lifecycle—from model design and training data to deployment—and demand coordinated oversight to prevent patient harm.

Risk Taxonomy and the "Shadow Use" Problem

The review identifies risks spanning technical, structural, and ethical domains. Hallucinations and data leaks are prominent technical concerns. Structurally, the prevalence of non-locally hosted systems introduces privacy and control uncertainties. Critically, the authors flag "shadow use"—the informal, often unguided adoption of LLMs by clinicians already occurring in practice. This emergent behavior operates entirely outside official safety protocols, creating accountability gaps.

From Paper to Practice: The Implementation Gap

The core takeaway for the applied research community is the stark gap between model capability and safe integration. The authors argue that safety cannot be an afterthought; it must be "systematically and comprehensively" addressed before and alongside clinical implementation. Proposed mitigations include secure development processes, curated training data, continuous monitoring, and clear institutional responsibilities. The analysis emphasizes that human oversight remains non-negotiable, positioning LLMs as tools to support—not replace—clinical judgment.

Unresolved Architectural Trade-Offs

While the review outlines a risk framework, it surfaces open questions for model architects. The tension between the utility of general-purpose foundation models and the need for domain-specific safety guarantees presents a significant design challenge. The call for rigorous evaluation of safety, transparency, and clinical value suggests that benchmarking for clinical AI must evolve beyond accuracy metrics to incorporate these new risk dimensions.