Jul 2026· JMIR Medical Informatics· Vol 14, pp. e89173-e89173· 0 citations· 34 references
Medicine
TL;DR
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.
Abstract
Abstract Background Patients undergoing invasive procedures frequently experience anxiety and often have unanswered questions regarding the procedure. Although large language models show considerable promise for supporting patient communication in many cases, their deployment in health care is limited by the risk of hallucinations, data-privacy constraints, and high energy costs—factors that impede equitable access in resource-limited settings. Objective This study aims to develop and evaluate LENOHA (Low Energy, No Hallucination, Leave No One Behind Architecture), a locally executable dialog system for safe, equitable, and sustainable preprocedural communication. Methods We built expert-curated FAQ (frequently asked question) databases and independent test sets for 2 domains (tooth extraction and gastroscopy; 200 utterances per domain: 100 clinical questions and 100 casual). A sentence-transformer classifier routed inputs: clinical questions were answered verbatim from the vetted FAQs (nongenerative path), while casual conversation was handled by a locally hosted 8-billion-parameter small language model (Swallow-8B). We evaluated 4 sentence-transformer models (including E5-large-instruct) against cloud large language models (ChatGPT [GPT-4o] and Gemini Advanced) using accuracy, F1-score, and area under the receiver operating characteristic curve, and measured the on-device inference energy on a consumer graphics processing unit (RTX 3080). Results Across both domains (N=400), E5-large-instruct achieved an accuracy of 98.3% (393/400; 95% CI 96.4%‐99.1%) and an area under the curve of 0.996, with only 7 out of 400 (1.8%) misclassifications. This performance was not statistically different from that of ChatGPT (GPT-4o), which had 6 out of 400 (1.5%) errors (McNemar test with Holm adjustment; P>.99). Sustainability measurements showed approximately 2.23 mWh per request (latency≈0.10 s; video RAM≈2.2 GiB average, ≈2.5 GiB peak) for the nongenerative clinical path vs approximately 168.27 mWh (latency≈8.51 s; video RAM≈13.3 GiB average, ≈14.0 GiB peak) for small language model small talk—approximately a 75-fold higher energy footprint per reply for the generative path. Conclusions High-precision, nongenerative clinical support is feasible using local, low-cost hardware without cloud dependence. 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.
Developing artificial intelligence capable of clinical language comprehension and reliable diagnostic reasoning has remained a core challenge in biomedical engineering. While Large Language Models (LLMs) demonstrate significant potential in general natural language processing tasks, their direct application in the medical domain is severely constrained by parametric hallucinations and data silos. This paper introduces an end-to-end, resource-efficient, multilingual speech-driven Question-Answering (QA) framework optimized for localized clinical support. To accommodate deployment on consumer-grade execution environments, we implement Parameter-Efficient Fine-Tuning (PEFT) using Low-Rank Adaptation (LoRA) and 4-bit Quantized LoRA (QLoRA) configurations across open-source 3B and 7B parameter architectures. Human preference alignment is enforced via a stateful Reinforcement Learning with Human Feedback (RLHF) loop applying Proximal Policy Optimization (PPO). Crucially, to mitigate the vulnerabilities of passive information retrieval, we introduce an Active Validation Loop powered by Corrective Retrieval-Augmented Generation (CRAG). This validation engine is decoupled from the model harness using the Model Context Protocol (MCP), standardizing asynchronous lookups across dense vector repositories, clinical guidelines, and real-time electronic health registries.
Misha Patel· International Journal of Sci...· 0 citations
A dual-view approach that connects clinical practice with computational methods is presented, establishing a five-level competency scheme following Miller’s Pyramid and linking deductive, inductive, and abductive reasoning patterns to common medical goals and tasks.
A benchmark evaluation framework for measuring the faithfulness of LLM-generated clinical trial summaries across three stakeholder audiences is introduced and a knowledge-graph-augmented retrieval system was developed and evaluated, producing statistically significant improvements in NLI-based faithfulness scores.
Smart Medical Microbiology showed strong domain adaptability in medical microbiology knowledge organization, semantic generation, and retrieval-augmented reasoning, which supports its potential use in educational support, infectious disease knowledge assistance, and retrieval-enhanced medical question answering.
Yongqian Gong, Ruiqiang Ma, Xicheng Wang et al.· Applied Informatics· 0 citations
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.
Mohammed Sharique Ahmed Quadri, Yunzhe Xue, Justin W. Ady et al.· 0 citations
Safety evaluation of large language models is conducted predominantly in English and predominantly on frontier systems. Neither condition describes how such models are encountered in low-resource health settings, where small quantised systems are run locally and queried in local languages. We ask whether clinical safety established in English transfers to Hausa, and whether any failure is attributable to the language, the clinical task, or the class of model that low-resource deployment admits. Matched English-Hausa question pairs were built for three conditions of high burden in northern Nigeria: malaria, sickle cell disease, and tuberculosis, probing knowledge recall, emergency triage, a leading question inviting a contraindicated action, and a traditional-remedy claim. Six models were evaluated: five locally deployable systems of 4-9 billion parameters, two medically fine-tuned, and one frontier system. All 128 responses were scored against Nigerian national treatment guidelines by two fluent Hausa speakers working independently and blind to one another. Among locally deployable models, mean clinical correctness fell from 1.57 in English to -0.03 in Hausa, on a scale where 2 denotes a correct answer and -1 an actively harmful one. The frontier model moved from 2.00 to 1.75 and produced no response judged harmful in either language. Drift was consistent across all three conditions. Inter-rater agreement was substantial for clinical correctness (kappa = 0.70); agreement on harm was initially poor (kappa = 0.22) and is examined in detail. Because a frontier model answers the same questions competently in Hausa, the deficit is a property neither of the language nor of the clinical material, but of the deployable tier.
Anthonio Oladimeji Gabriel, Dimeji AbdulSobur Olawuyi, T. Ajayi et al.· 0 citations