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The feasibility of using a configurable, source-grounded extraction workflow to convert fragmented oncology documentation into reusable structured data is supported.
The NimbleMind Multi-Agent System (nMAS) is evaluated, a configurable workflow that separates clinician-defined field specifications from extraction models and returns report-level predictions with source-linked evidence within a clinician-reviewable workflow.
This work presents a source-grounded framework to construct progressive multimodal diagnostic dialogues from case reports and an evaluation strategy for assessing MLLMs on final diagnosis, diagnostic reasoning, and image-finding interpretation and demonstrates that fluent responses do not necessarily reflect evidence-g...
Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practice, including progressive disclosure of multimodal information, dynamic updating of diagnostic hypotheses, and continuous refinement of clinical reasoning. However, existi...
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