The architecture and validation of Medi-Gemma, a Clinical Decision Support System (CDSS) for wound pathology triage and workflow automation, are presented, which supports Medi-Gemma as a safer pattern for LLM-based clinical decision support where structured data fidelity, retrieval grounding, and deterministic safeguards are essential.
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. This paper presents the architecture and validation of Medi-Gemma, a Clinical Decision Support System (CDSS) for wound pathology triage and workflow automation. The platform introduces a decoupled framework that separates clinical perception from data orchestration while preserving traceable reasoning. Medi-Gemma uses a multi-stage pipeline coordinated by a centralized ClinicalOrchestrator. Data requests are handled without generative inference by a DataManager that cleans unstructured Electronic Medical Record (EMR) files through type coercion. Natural language queries are processed by a hierarchical IntentRouter, which routes requests to deterministic analytics paths executed by a PandasQueryEngine or to patient-specific reasoning managed by a ClinicalRAGEngine using a CPU-optimized vector store. A key contribution is the Ground Truth Injection Module, which intercepts patient-specific queries, extracts numeric identification tokens, queries the structured dataframe via Pandas, retrieves the latest validated clinical state, and embeds this snapshot as an overriding context block in the LLM prompt before generation. Safety compliance is enforced by a deterministic ProtocolManager that maps clinical terminology to fixed evidence-based risk pathways, while a SafetyVerifier phrase filter prevents output rule violations. Validation shows that this architecture eliminates semantic context drift, prevents database compilation crashes, and improves factual adherence to backend clinical repositories. These results support Medi-Gemma as a safer pattern for LLM-based clinical decision support where structured data fidelity, retrieval grounding, and deterministic safeguards are essential.
This work introduces MedReaMM, a benchmark specifically designed to evaluate models'ability to synthesize heterogeneous clinical evidence consisting of detailed patient histories alongside multiple medical images into accurate differential diagnoses under a complete-information paradigm.
Lai Wei, Yu-Chao Chen, Zhenbiao Cao et al.· 0 citations
By decoupling clinical information retrieval from generative chitchat, LENOHA enhances safety, preserves privacy, and markedly reduces energy use, offering a practical blueprint for sustainable and equitable medical AI deployment across diverse care settings.
Motoki Sato, Sou Nagata, Mizuho Ohnuma et al.· JMIR Medical Informatics· 0 citations
Managing diabetic kidney disease (DKD) is inherently complex, requiring clinicians to synthesize patient history, fluctuating biomarkers, and evolving treatment guidelines. While large language models (LLMs) show promise in medical decision support, their clinical adoption is hindered by factual inaccuracies and a lack of specific reasoning required for individualized patient management. To address this, we developed a hierarchical multi-agent system that integrates a locally deployed retrieval-augmented generation (RAG) framework with a cloud-based advanced reasoning engine, grounding responses in a curated corpus of clinical guidelines. We conducted a multi-center retrospective validation using 267 patient cases. The system's performance was evaluated against baseline models through a blinded review by twelve independent physicians across clinical dimensions including accuracy, safety, and factuality. Our evaluation reveals that the RAG-enhanced system significantly outperforms unaugmented models in providing accurate, guideline-compliant recommendations. Notably, it substantially reduced safety-critical errors, particularly in identifying medication contraindications related to renal function stages, while achieving high inter-rater reliability. This study demonstrates that anchoring LLMs with authoritative knowledge effectively mitigates hallucination risks and enhances clinical reliability. The proposed framework functions as a reliable on-demand assistant for DKD management, providing guideline-grounded decision support for primary care providers.
Xuan Tao, Lan Tian, Chenhao Fang et al.· npj Digital Medicine· 0 citations
The emergence of large language models offers unprecedented opportunities to transform clinical workflows. However, reliance on proprietary closed-source models poses significant risks to data privacy, institutional autonomy, and reasoning transparency. This study investigates the optimization of open-weight architectures, specifically the Llama-3.1-8B and Qwen2.5-14B-Instruct families, to function as specialized diagnostic tools rather than general-purpose conversational agents. Utilizing the MedQA USMLE-style benchmark, we evaluate the synergistic impact of context window scaling and multi-stage supervised fine-tuning within a localized Retrieval-Augmented Generation framework. Our findings identify a critical scaling threshold at 512 tokens, beyond which diagnostic accuracy stabilizes while preserving the computational efficiency required for on-site clinical deployment. Furthermore, we document a pronounced Brevity Shift, in which multi-stage supervised fine-tuning reduced model verbosity by over 99.7%. The models transitioned from long-form reasoning with an average of 465 tokens to single-token outputs for Qwen and near-single-token outputs for Llama. This shift effectively mitigated verbosity compensation, defined as the tendency of models to mask uncertainty through excessive text, while simultaneously improving diagnostic performance, which peaked at 70.0% accuracy for the Qwen architecture on the benchmark task. Additionally, our analysis reveals that systematic fine-tuning substantially reduces option-preference bias, as measured by Total Variation Distance, leading to more objective and consistent model outputs. Conducted under the INFOSTRATEG Strategic Program (PARROT AI) and funded by the National Centre for Research and Development (NCBR), Poland, this work establishes a robust technical framework for developing trustworthy, high-efficiency medical AI systems capable of operating entirely within hospital-controlled infrastructure.
L. Pawlik, Stanisław Deniziak· Scientific Reports· 0 citations
Laboratory medicine sits at the intersection of clinical science and data management. A single hospital admission can generate dozens of analyte values, yet most electronic health record (EHR) interfaces present them as rows in a table, leaving interpretation entirely to the clinician. Alert fatigue, driven in part by poorly calibrated notifications remains one of the most documented usability problems in modern EHR design [1].
This paper describes a prompt-driven pipeline that converts FHIR R4 laboratory panels into structured, paragraph-length clinical narratives paired with a calibrated 0-1 risk score, using GPT-4o-mini as the generation engine. The full behavioral specification is encoded in the prompt and output schema. We evaluated the system on 200 laboratory panels, each drawn from a distinct synthetic patient, from Synthea-generated FHIR bundles spanning seven panel categories (metabolic, lipid, blood count, diabetes monitoring, kidney, liver, and urine). We compared three configurations: a rule-only template baseline, the LLM alone (no seed), and the hybrid pipeline in which a deterministic rule-based risk seed is supplied to the LLM. Abnormal-analyte detection was near ceiling and statistically indistinguishable for both LLM configurations (F1 ≈ 0.97), indicating that the model recovers out-of-range analytes directly from the structured table with or without the seed. The seed's measurable contribution is to risk-score calibration: the correlation between the model's 0–1 risk score and the reference rule score rose from r = 0.87 (no seed) to r = 0.96 (with seed). The rule seed thus functions as a calibration mechanism rather than a detection aid.
These are proof-of-concept results on synthetic structured data. They demonstrate feasibility for abnormality flagging and narrative generation; they do not constitute a claim of clinical validity, which would require real-world data and clinician review. The paper contributes a reproducible architectural framework, a systematic quantitative benchmark on synthetic panels, and a grounded discussion of the integration challenges and future directions that separate a research prototype from a clinically deployed tool.
Rahul Reddy Hanumanthgari· EPJ Web of Conferences· 0 citations
Embedding domain-specific knowledge into LLMs may improve performance on specialized exam-style thoracic-surgery questions on this text-only benchmark, however, the present 56-item evaluation does not establish clinical equivalence, diagnostic accuracy in practice, multimodal competence, or readiness for real-world clinical decision support.
Qian Li, Yongxin Li, Chao Ye et al.· Frontiers in Artificial Inte...· 0 citations
