Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines. However, its practical application often depends on the availability of formal, machine-interpretable representations of guidelines, such as Computer-Interpretable Guidelines (CIGs), which are seldom available in real-world clinical settings.
arXiv:2606. 09489v1 Announce Type: new Abstract: Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines.
arXiv:2608.30022v1 Announce Type: new Abstract: Introduction: NICE guidelines provide evidence-based recommendations for clinical care but remain largely in unstructured natural language. Existing ap...
arXiv:2607. 06802v1 Announce Type: cross Abstract: Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints.
The paper introduces MEGA-CDP, a benchmark designed to evaluate medical large language models (LLMs) on their ability to generate clinical decision pathways (CDPs) that adhere to clinical practice guidelines. MEGA-CDP is built from 2,274 English and Chinese guidelines, producing 42,353 clinical cases with explicit reference CDPs, and supports both single-turn and multi-turn interactions. Experiments on 16 LLMs reveal that reliable guideline adherence remains difficult, underscoring the need for CDP-focused evaluation and the potential of MEGA-CDP to advance medical LLM performance.
arXiv:2606. 05463v1 Announce Type: new Abstract: Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts.
The paper introduces ALLMTS, an automated medical triage system that uses large language models (LLMs) to execute commercially maintained telehealth guidelines rather than directly triage callers. By selecting the appropriate guideline and evaluating its triage questions, ALLMTS deterministically derives dispositions, achieving 0.95 accuracy on 566 synthetic scenarios and 0.98 on a 50-scenario benchmark, matching expert nurse performance. The system dramatically reduces over‑triage rates (to ≤0.02) while keeping under‑triage low, demonstrating the effectiveness of coupling LLMs with executable clinical guidelines.
arXiv:2607. 04907v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over tabular patient data, and omissions in vector retrieval.
The paper introduces a tool‑augmented large language model framework that improves the reliability of medical calculations derived from free‑form patient notes. It separates clinical language understanding from rule execution by retrieving calculator specifications, generating executable tools, and using a constrained extractor to map notes to calculator inputs. On the MedCalc‑Bench dataset, the method achieves 86.73% final‑answer accuracy, outperforming one‑shot prompting and fine‑tuned baselines by 24.0 and 17.27 percentage points.
arXiv:2606. 09852v1 Announce Type: cross Abstract: High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainability are essential.
SafeTune is a source‑available library that consolidates four safety‑intervention paradigms—post‑hoc weight recovery, safety‑constrained fine‑tuning, gradient‑based unlearning, and inference‑time steering—into a single, configuration‑driven workflow. It offers shared interpretability, evaluation, and deployment tools, and its modular registry allows easy addition of new methods, benchmarks, judges, models, and fine‑tuning domains. The authors demonstrate SafeTune with controlled comparisons and case studies in finance and medical deployments, showing how it characterizes safety drift, evaluates interventions on refusal‑behavior and capability metrics, and supports calibrated or layered mitigation.
arXiv:2601. 22324v3 Announce Type: replace Abstract: Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules.
arXiv:2607. 24371v1 Announce Type: cross Abstract: Healthcare interoperability requires AI systems to produce structured outputs conforming to standardized schemas including ICD-10 for diagnostic coding, CPT for procedure billing, and HL7 FHIR for data exchange.