Public articles linked to the same research event.
medRxiv The study developed and evaluated the Multimodal Anonymizer, a modular, locally deployable multi-agent framework integrating multimodal large language models, task-specific neural networks, and rule-based transformations; on benchmarks spanning text, tables, PDFs, imaging, metadata, filenames, audio, handwriting, and 3D imaging, its best local configuration (orchestrator Qwen3-VL-235B-A22B-Thinking) achieved 98.80% per-patient deidentification sensitivity (95%-CI 97.20; 100) and 99.60% critical clinical preservation (95%-CI 98.80; 100), reached 100% sensitivity and critical preservation on 250 local Charité partograms, and outperformed established tools across most modalities.
The study developed and evaluated the Multimodal Anonymizer, a modular, locally deployable multi-agent framework integrating multimodal large language models, task-specific neural networks, and rule-based transformations; on benchmarks spanning text, tables, PDFs, imaging, metadata, filenames, audio, handwriting, and 3D imaging, its best local configuration (orchestrator Qwen3-VL-235B-A22B-Thinking) achieved 98.80% per-patient deidentification sensitivity (95%-CI 97.20; 100) and 99.60% critical clinical preservation (95%-CI 98.80; 100), reached 100% sensitivity and critical preservation on 250 local Charité partograms, and outperformed established tools across most modalities.
The study developed and evaluated the Multimodal Anonymizer, a modular, locally deployable multi-agent framework integrating multimodal large language models, task-specific neural networks, and rule-based transformations; on benchmarks spanning text, tables, PDFs, imaging, metadata, filenames, audio, handwriting, and 3D imaging, its best local configuration (orchestrator Qwen3-VL-235B-A22B-Thinking) achieved 98.80% per-patient deidentification sensitivity (95%-CI 97.20; 100) and 99.60% critical clinical preservation (95%-CI 98.80; 100), reached 100% sensitivity and critical preservation on 250 local Charité partograms, and outperformed established tools across most modalities.
The study developed and evaluated the Multimodal Anonymizer, a modular, locally deployable multi-agent framework integrating multimodal large language models, task-specific neural networks, and rule-based transformations; on benchmarks spanning text, tables, PDFs, imaging, metadata, filenames, audio, handwriting, and 3D imaging, its best local configuration (orchestrator Qwen3-VL-235B-A22B-Thinking) achieved 98.80% per-patient deidentification sensitivity (95%-CI 97.20; 100) and 99.60% critical clinical preservation (95%-CI 98.80; 100), reached 100% sensitivity and critical preservation on 250 local Charité partograms, and outperformed established tools across most modalities.