AIDx: a locally deployable AI system for physician clinical decision support
AIDx: a locally deployable AI system for physician clinical decision support
https://www.nature.com/articles/s41598-026-47470-1
Publish Date: 2026-06-11 05:18:00
Source Domain: www.nature.com
Overview
The goal of this work was to design and assess a locally deployable LLM-based clinical assistant using de-identified EHR data for training and public benchmarks for evaluation. I conducted no clinical, prospective, or user studies. All experiments are single-modal (text). Imaging and other multimodal inputs are outside scope.
Methodological contributions and scope
This work documents: (1) a patient-timeline construction procedure that restructures de-identified EHR notes into temporally indexed snapshots suitable for supervised instruction tuning; (2) a retrieval pipeline grounded in open medical references and integrated into prompting for answer grounding; (3) a locally deployable inference stack (quantized model + OpenAI-compatible API) that interoperates with EHR retrieval; and (4) ablation experiments and error analysis isolating the contributions of fine-tuning and RAG.
This paper evaluates the end-to-end system under multiple configurations (with and without RAG) and a deterministic protocol. (Fig. 1)
AIDx-Copilot training and implementation
Fig. 1The alternative text for this image may have been generated using AI.
Data processing pipeline for preparing supervised training data for AIDx-Copilot. De-identified EHR records from emergency, inpatient, and ICU settings are consolidated into a holistic visit representation. Static attributes (e.g., demographics, history) and dynamic attributes (e.g., labs, orders, diagnoses) are separated, and dynamic events are organized into temporally ordered visit timelines. Timeline snapshots are used to generate predictive question–answer pairs for instruction tuning. The patient timeline shown is illustrative and not a complete clinical record.
AIDx-Copilot was trained using de-identified clinical records derived from MIMIC-IV10,11,12. I consolidated records from emergency, inpatient, and intensive care settings to cover common hospital documentation patterns.
Data preparation and temporal alignment
Each patient chart…
