arXiv AI
Jun 24
A global log for medical AI
arXiv:2510. 04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure.
Understanding the Limits of Agentic ICD Coding
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arXiv AI
Aug 3
EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
arXiv AI
Jul 17
MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
arXiv:2607. 15166v1 Announce Type: new Abstract: Most medical AI benchmarks measure whether a model knows the correct answer.
arXiv AI
Sep 1
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
arXiv AI
Aug 19
Foundation Agents Meet Agentic Deep Research: Evidence-Grounded Clinical Code Forecasting
The paper introduces ICD-Deepresearch, a workflow that combines foundation models for electronic health records (EHR) and language models with medical search and ICD dictionaries to forecast future ICD codes for upcoming clinical encounters. It evaluates candidate code transitions by linking patient evidence, external clinical relations, and exact code semantics within a fixed top‑K budget, using SparseEHR for initial priors, GPT‑5 for complementary forecasts, and a final selection step that validates, deduplicates, and ranks candidates. The method achieves patient‑averaged precision/recall of 24.60/35.09% on MIMIC‑III and 25.14/48.32% on MIMIC‑IV, with physicians rating 51–68% of its retrieved documents as useful, outperforming standalone GPT‑5 web search and Medical Deep Research.
arXiv AI
Jul 15
Graph-Constrained Policy Learning for Extreme Clinical Code Prediction
arXiv:2607. 11954v1 Announce Type: cross Abstract: Clinical code prediction maps unstructured discharge summaries to ICD-10-CM leaf codes in a large, sparse, and deeply hierarchical label space.
arXiv AI
Sep 18
LearnActCoder: Role-Aware Error Memory for Adaptive Clinical Coding Agents
LearnActCoder introduces a role‑aware error memory system for clinical coding agents, converting labeled errors into a structured Mistake Knowledge Database (MistakeKDB). The framework routes false‑negative lessons to a recall‑oriented coder and false‑positive lessons to a precision‑oriented judge, forming a Coder‑Judge pipeline. On MIMIC‑III and MIMIC‑IV datasets, the MistakeKDB improves CPT F1 by 5.9 percentage points and shifts ICD‑10 coding toward higher precision, while maintaining stable performance on held‑out notes.
arXiv Computation and Language
Oct 27
EdgeDeID: A 0.6B Clinical De-identifier Trained Without Real Patient Data
arXiv:2610.33839v1 Announce Type: new Abstract: De-identifying clinical notes with a hosted language model discloses the information the step is meant to protect. We present EdgeDeID, a 0.6B-paramete...
arXiv AI
Oct 27
Auditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis
arXiv:2610.33592v1 Announce Type: new Abstract: Augmented large language model (LLM) systems have reported high benchmark accuracies in rare disease diagnosis, yet their real-world clinical utility r...
arXiv AI
Jun 2
AutoMedBench: Towards Medical AutoResearch with Agentic AI Models
arXiv:2606. 01961v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering.
arXiv AI
Sep 2
AI Morbidity and Mortality: A Framework for Clinical AI Failure Review
AI Morbidity and Mortality (AI M&M) is a structured, blameless framework designed to review clinical AI failures. It combines standardized case intake, evidence preservation, investigator reconstruction, tool‑in‑loop attribution, and corrective‑action tracking, classifying each event across four linked dimensions: Trigger, Mechanism, Clinical Pathway, and Corrective Action. The authors demonstrate the framework with five outpatient medication and clinical decision‑support cases, achieving full agreement among reviewers on all classification axes.
Hugging Face Trending Papers
Oct 13
Beyond ICD Codes: Semantic IDs for Generative Medical Coding
Generative approaches to automatic International Classification of Diseases (ICD) coding use large language models (LLMs) to predict ICD codes from clinical documents. However, original ICD code strin...