structured report generation

Low referral efficiency and suboptimal triage accuracy hinder clinical workflow in specialty referral. Method: We propose a synergistic agent architecture integrating Finite State Machines (FSMs) with Large Language Models (LLMs), enabling a modular, scalable medical conversational system. The FSM ensures process safety and regulatory compliance, while the LLM enhances semantic understanding and contextual reasoning for automated clinical assessment, intelligent referral decision-making, and structured patient consultation. Deployed via a microservices architecture, the system maintains loose coupling and interoperability with existing healthcare information systems. Contribution/Results: Evaluated on over 55,000 real-world clinical dialogues, the system achieves statistically significant improvement in first-referral accuracy over human specialists (p < 0.01) and reduces average consultation time to one-third that of manual triage, establishing a novel, high-reliability, and deployable paradigm for AI-augmented clinical triage.

This study addresses interoperability challenges in European healthcare systems by proposing a large language model (LLM)-based approach for multilingual clinical text information extraction. To process unstructured clinical reports, we design an end-to-end extraction pipeline with an interactive interface and jointly apply prompt engineering and supervised fine-tuning to adapt medium- and small-scale LLMs. Our key contributions are threefold: (1) We release the first bilingual clinical summarization dataset—comprising 60,000 English and 24,000 German samples—validated via multi-dimensional automatic and human annotation; (2) We propose a multi-granularity evaluation framework integrating ROUGE, BERTScore, and entity-level metrics; (3) Our fine-tuned model achieves 89.3% F1 on critical entity extraction—outperforming zero-shot LLMs by 22.7%—while accelerating inference by 3.8× and reducing GPU memory consumption by 64%, significantly enhancing clinical deployability.

Current medical AI deployment faces a fundamental gap among explainability theory, clinical requirements, and regulatory expectations, compounded by the absence of practical guidance for preclinical evaluation readiness. This study bridges these domains by systematically integrating eXplainable AI (XAI) theory, clinical practice needs, and regulatory frameworks—introducing two foundational preclinical development principles: “Transparency-by-Design” and “Actionability-by-Design” to establish a shared interdisciplinary language. Methodologically, we unify model calibration, uncertainty quantification, and robustness engineering to enable case-level interpretability and full system-behavior traceability. Our contribution is a rigorously defined, empirically verifiable technical boundary and actionable implementation guidelines that significantly reduce the preparation time for clinical evaluation. This work establishes a methodological foundation for compliant, trustworthy, and clinically integrated AI deployment in healthcare. (149 words)

This study addresses critical challenges hindering the clinical deployment of large language models (LLMs): patient privacy, algorithmic bias, regulatory compliance, and operational sustainability. Methodologically, we propose the first healthcare-specific, four-dimensional adaptation framework comprising: (1) domain-adaptive fine-tuning, (2) clinically informed prompt engineering, (3) multimodal electronic health record (EHR) integration—unifying unstructured text and structured data—and (4) a novel evaluation paradigm centered on clinical accuracy, fairness, robustness, and outcome-oriented metrics. Crucially, privacy-preserving mechanisms, bias mitigation strategies, and regulatory requirements (e.g., HIPAA, FDA guidelines) are systematically embedded throughout the technical design lifecycle. The work yields a reproducible implementation roadmap with clearly defined interdisciplinary collaboration protocols. It provides both theoretical foundations and actionable guidance for the safe, effective, and compliant integration of LLMs into clinical decision support, patient-facing applications, and healthcare administrative automation.

This work addresses the disconnect between existing large language model (LLM)-based clinical tabular feature engineering approaches and downstream predictive models, which struggle with class imbalance, heterogeneous features, and stringent interpretability requirements in healthcare data. To bridge this gap, the authors propose MedFeat, a novel framework that, for the first time, integrates model-awareness and interpretability-driven mechanisms into LLM-assisted feature engineering. MedFeat iteratively generates new features that align with the inductive biases of downstream models and are inherently interpretable by fusing feature importance signals with real-time model feedback. Evaluated across multiple real-world clinical prediction tasks, MedFeat significantly outperforms current methods, achieving an average performance improvement exceeding 10%, while maintaining compatibility with diverse model architectures.

This work addresses the critical limitation of current clinical large language models in dynamically adapting treatment decisions when patient contexts change—a capability inadequately assessed by conventional medical question-answering benchmarks. To bridge this gap, the authors propose ClinPivot, the first auditable evaluation benchmark specifically designed to measure dynamic adaptability in therapeutic decision-making. ClinPivot leverages a biomedical knowledge graph to construct interpretable context-perturbation pairs and incorporates structured decision supervision alongside a lightweight replay mechanism to enhance contextual sensitivity under constrained knowledge budgets. Experimental results reveal that state-of-the-art models, including Qwen variants, perform poorly on ClinPivot, whereas the proposed approach significantly improves dynamic decision-making without compromising general assistant capabilities, thereby exposing a notable disconnect between standard medical QA accuracy and genuine clinical reasoning proficiency.

This study addresses the frequent violations of clinical coding standards—such as ICD-10, CPT, and HL7 FHIR—by large language models when generating structured medical data, which impedes integration with electronic health record systems. To mitigate this, the authors propose and validate a closed-loop verification-and-repair framework that automatically detects and iteratively corrects formatting errors. The approach is evaluated using three open-source models—Qwen2.5-7B, Llama3.1-8B, and Gemma2-9B—deployed locally across 320 clinical scenarios. Results demonstrate a substantial improvement in schema compliance across all models, achieving an overall adherence rate of 99.0% and increasing individual model performance by 7.8 to 12.5 percentage points. Notably, 96% of detected errors were attributable to repairable representation-layer issues, with most resolved within one or two correction rounds, effectively compensating for the models’ limited understanding of healthcare IT standards.

This work proposes a modular framework for assessing compliance with clinical care pathways in the absence of predefined computer-interpretable guidelines (CIGs), which are often unavailable in real-world settings. By orchestrating multiple large language models (LLMs), the approach automatically extracts patient trajectories and normative rules directly from unstructured clinical records and textual guidelines, then translates them into executable scripts to quantify adherence. Integrating LLM orchestration, natural language processing, and event log analysis, the method was evaluated on hundreds of stroke patients at Alexandria Hospital’s neurology department. Results demonstrated that over 86% of care processes aligned with guideline recommendations, confirming the framework’s feasibility and clinical applicability.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

Legacy clinical reporting systems hinder AI integration and impede drug development and pharmacovigilance due to opaque outputs and the absence of machine-readable intermediate representations. This work proposes a non-intrusive, metadata-driven framework that bridges legacy components—without modifying their source code—through mapping layers, a typed intermediate representation (IR), and coordinator wrappers, thereby transforming their outputs into structured data suitable for large language models (LLMs) and enabling progressive replacement. Validated on 558 SAS components (373k lines of code), the approach achieves immediate AI readiness in coexistence mode, reduces proprietary code by 92% after optional integration, and demonstrates unit-level consistency exceeding 80% across 11 of 14 report types (mean: 82.7%, peak: 99.2%). Five reports achieved 100% compliance on the CDISCPilot01 benchmark, and the framework successfully enabled LLM-driven automated pharmacovigilance, table summarization, and trial configuration generation.