Aubin Charley

A radiology agentic framework separates a large-language-model orchestrator from a modular tool interface and uses 1,989 finding classes consolidated from FDA-cleared radiology AI devices as its tool catalog, simulating structured report generation on the PARROT fictional-report corpus. Planning from the clinical indication alone, actionable-finding recall is 0.290 with the cleared catalog and 0.437 with an unlimited catalog; when the planner reads the target report, these rise to 0.426 and 0.740. Tool outputs are simulated from the reference report, so the comparison isolates tool coverage and orchestration rather than tool error.

A radiology agentic framework separates a large-language-model orchestrator from a modular tool interface and uses 1,989 finding classes consolidated from FDA-cleared radiology AI devices as its tool catalog, simulating structured report generation on the PARROT fictional-report corpus. Planning from the clinical indication alone, actionable-finding recall is 0.290 with the cleared catalog and 0.437 with an unlimited catalog; when the planner reads the target report, these rise to 0.426 and 0.740. Tool outputs are simulated from the reference report, so the comparison isolates tool coverage and orchestration rather than tool error.