This standard establishes a risk quantification and digital auditing framework for human-machine synergy in medical Artificial Intelligence (AI) applications. It provides a standardized digital methodology for AI developers, healthcare institutions, and digital finance entities (e.g., medical liability insurance providers) to objectively quantify system autonomy and functional risk. The standard defines a six-level matrix (Level M0 to M5) to classify system autonomy, functional allocation boundaries, and Human-in-the-Loop (HITL) oversight requirements. Furthermore, the standard specifies technical criteria for continuous dynamic supervision mechanisms–including secure data logging, algorithmic transparency, and immutable audit trails. These requirements help ensure digital auditability, mitigate operational risks, and facilitate verifiable evidence collection during human-AI collaborative workflows.
- Standard Committee
- CTS/DFESC - Digital Finance and Economy Standards Committee
- Status
- Active PAR
- PAR Approval
- 2026-06-04
Working Group Details
- Society
- IEEE Consumer Technology Society
- Standard Committee
- CTS/DFESC - Digital Finance and Economy Standards Committee
- Working Group
-
AIMS - Artificial Intelligence-Medical Synergy Working Group
- IEEE Program Manager
- Soo Kim
Contact Soo Kim - Working Group Chair
- Jiahong Dong
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