arXiv:2510.04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure. Medicine's rapidly growing AI stack has no equivalent. As medicine deploys AI tools at scale, there is no standard way to record how, when, by whom, and for whom these models are used. Without such records, it is difficult to measure real-world performance and outcomes, detect adverse events, or identify bias and dataset drift. Here we introduce MedLog, a protocol for event-level logging of medical AI. Each time an AI
The rapid deployment and scaling of AI in medical settings necessitate a standard for tracking real-world performance, accountability, and the identification of adverse events or biases.
A standardized logging protocol addresses critical challenges in medical AI adoption, paving the way for safer, auditable, and more trustworthy integration into healthcare systems.
The introduction of MedLog creates a universal framework for recording medical AI interactions, enabling better governance, performance measurement, and risk mitigation previously absent.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Regulatory bodies
- · Unaccountable AI systems
- · Medical AI companies resisting transparency
Improved accountability and transparency for medical AI deployments across healthcare systems.
Faster identification and mitigation of AI biases or adverse effects, leading to enhanced patient safety and trust.
The development of new AI governance and auditing tools built upon standardized logging data, pushing responsible AI innovation.
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Read at arXiv cs.AI