Jul 2026· Journal of Evaluation In Clinical Practice· Vol 32· 0 citations· 31 references
Medicine
TL;DR
The proposed ontology addresses key ambiguities within WHO‐FIC, including the relationship between Activity and Participation and the perceived hierarchical privileging of diagnosis, and offers a more coherent and cognitively usable framework while respecting limits on human reasoning and managing the complexity‐coherence trade‐off.
Abstract
ABSTRACT Background Clinical reasoning in contemporary practice often involves ill‐structured, poorly defined problems that span the biopsychosocial domain and require reasoning under high relational complexity. Despite sustained scholarly attention to clinical reasoning, few models are both theoretically grounded and usable for clinicians who face this complexity. At the same time, the World Health Organization Family of International Classifications (WHO‐FIC) offers a rich but complex ontology that was not designed primarily to support clinical reasoning in practice. Aim To develop a two‐stage WHO‐FIC‐based ontological model of clinical reasoning that is conceptually coherent yet remains cognitively tractable in the face of complexity. Methods A conceptual analysis was undertaken. It integrated three strands: (1) WHO‐FIC classification theory, including recent work on harmonisation; (2) empirical and theoretical literature on clinical reasoning; and (3) cognitive theories of bounded rationality, fast‐and‐frugal heuristics, relational complexity, and framing. These strands were used to derive design constraints for a clinically usable ontology. They were then synthesised into a two‐stage, graph‐based model designed to manage the complexity‐coherence trade‐off in clinical reasoning. Results Stage 1 introduces a parsimonious triad—Body, Activity, and Environment—represented as a complete three‐node graph with bidirectional relations. This triad provides an etiologically neutral and cognitively economical starting point for framing complex clinical problems. Stage 2 introduces three emergent constructs—Health Condition, Participation, and Intervention—derived compositionally from Stage 1 relations and aligned with ICD‐11, ICF, and ICHI, respectively. The model supports iterative movement between stages through composition and decomposition. It incrementally increases complexity while keeping relational load within cognitively feasible bounds and preserves the value of diagnosis while reducing its tendency to dominate problem framing. A worked case illustrates how the ontology supports reasoning about complex, time‐dependent problems through repeated movement between stages. Conclusions The proposed ontology addresses key ambiguities within WHO‐FIC, including the relationship between Activity and Participation and the perceived hierarchical privileging of diagnosis. It offers a more coherent and cognitively usable framework while respecting limits on human reasoning and managing the complexity‐coherence trade‐off. The model provides a theoretically grounded heuristic scaffold for clinicians and educators who work with complexity. It also contributes to clinical reasoning literature by emphasising reasoning about concepts, not only cases, with implications for interprofessional education and practice.
Computational physiology needs representations that connect traits across biological scales while distinguishing causal, constitutive, and mathematical dependencies. We present PhysioMap, an ontology-grounded knowledge base of contextualized physiological traits and precisely defined relation types. A versioned projection maps entailed ontology patterns to a typed causal knowledge graph that constrains quantitative structural causal models. Derivative signs provide a separate qualitative abstraction, which the PhysioMap solver uses to analyze steady-state responses in the presence of feedback. A stratified expert review across all relation types supported most sampled relations and isolated a minority for correction or further investigation. In a rare metabolic disease application, nearly all determinate predictions agreed with the HPO-derived reference before post-hoc review; after the discordant reference directions were excluded, all remaining determinate predictions agreed. Shortest signed paths produced directional errors, particularly on cases for which the PhysioMap solver did not determine a direction, indicating that its abstentions concentrated difficult cases. Abduction usually narrowed the candidate set but often did not identify a unique cause. PhysioMap therefore connects ontology-grounded physiological content to interventional prediction and abduction under incomplete quantitative knowledge. Because PhysioMap curation and the HPO-derived reference may share supporting literature, and because abduction used a closed candidate pool, these analyses do not constitute independent clinical validation.
R. Hoehndorf, Paul N. Schofield, G. Gkoutos· bioRxiv· 0 citations
Scientific and clinical decision-making depends on evidence from the primary literature, but existing standards for representing that evidence (FHIR Evidence, ECO, SEPIO, and the GA4GH Genomic Knowledge Standards) are oriented toward clinical-trial workflows, evidence codes, or single-variant assertions, and do not capture the fine-grained, domain-specific structure of claims in basic and pre-clinical research. We introduce a semantic model for scientific evidence with three core classes, specialize it for genetics, align it structurally to FHIR Evidence with a SEPIO-anchored credibility decomposition, and attach a compact dimensional vocabulary whose conditional-activation rules are validated by a SHACL schema for the implemented constraints. Using clinical variant interpretation as the driving use case, we evaluate the model through a human-AI annotation pilot over six genetics papers, yielding 28 evidence items and 95 source-anchored assertions, with a workflow that keeps curator-authored reference annotations distinct from AI-drafted annotations. Treating the pilot as a feasibility study rather than a benchmark, we argue that the model is a useful increment toward trustworthy, AI-ready infrastructure for variant interpretation: a reference data model and validation schema for representing genetic evidence.
This study explores the role of ontology in healthcare by surveying numerous research articles to provide a comprehensive overview of its applications, benefits, and challenges. Ontologies, which enable structured representation and integration of complex healthcare knowledge, have been increasingly employed to enhance data interoperability, improve clinical decision making, and support personalized medicine. Despite their potential, the development and implementation of ontologies in healthcare face challenges, including issues with data consistency, interoperability across systems, and adaptation to rapidly evolving medical knowledge. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, we systematically identified, screened, and reviewed relevant studies. This model enabled a rigorous process for article selection, ensuring inclusion of high-quality research that addressed key themes in ontology-based healthcare systems. The survey identifies prevalent issues, such as limited standardization, difficulty in updating ontologies to reflect the latest medical insights, and obstacles in integrating heterogeneous datasets. Additionally, gaps are noted in addressing patient privacy and ethical concerns, which are crucial in healthcare applications. This review contributes by highlighting these challenges and proposing areas for further research, such as developing adaptable, scalable ontologies that are ethically aligned and capable of supporting advanced technologies like AI. The findings underscore the need for collaborative efforts among healthcare providers, data scientists, and policymakers to build robust ontology frameworks that can sustainably support healthcare advancements.
U. Priyadharshini, R. Vijayan· Frontiers in Artificial Inte...· 0 citations
A single LLM verifier lacks sufficient reliability to serve as a stand-alone judge of clinical reasoning at scale, and that structured human oversight remains essential.
Hyunjung Byun, Dahyoun Lee, Munyoung Jung et al.· Journal of medical systems· 1 citation· ⚡1
Background Clinical trials conducted in Germany are registered across multiple registries, including the German Clinical Trials Register (DRKS), ClinicalTrials.gov, the EU Clinical Trials Register (EUCTR), and, since 2023, the Clinical Trials Information System (CTIS). These registries record health conditions using different classification systems and terminologies, including ICD-10-GM, MeSH, MedDRA, and free text, making cross-registry analyses difficult. We developed and evaluated a pipeline for harmonizing trial condition descriptions to WHO ICD-10 and compared its performance with that of a large language model (LLM) and to health conditions coded by humans. Methods We developed a four-stage, registry-aware mapping pipeline consisting of: (i) condition mention extraction and normalization; (ii) classification of ICD-mappable versus non-mappable mentions; (iii) ontology-based candidate generation using UMLS links between MeSH, MedDRA, ICD-10-GM, and WHO ICD-10; and (iv) SapBERT-based semantic retrieval with hybrid confidence scoring. A second variant additionally applied cross-encoder reranking of the top candidate codes. A stratified sample of 500 condition mentions was manually coded to create an expert reference standard. GPT-4o was evaluated in parallel using the same structured decision framework as the human reviewers. Performance was assessed using accuracy, precision, F1 score, and Cohen's {kappa} at the three-character, block, and chapter levels of ICD-10. Results The pipeline was applied to 23,061 clinical trials and identified 39,512 ICD-mappable condition mentions, of which 72.4% received a high-confidence assignment. Against 390 expert-coded mentions, the baseline pipeline achieved 49.0% accuracy at the three-character ICD-10 level ({kappa} = 0.487), increasing to 58.7% at the chapter level ({kappa} = 0.561). The cross-encoder method produced small but consistent improvements across all evaluation levels. Candidate-recall analysis showed that the correct code was present in the retrieved candidate set in only 73.7% of cases. The LLM substantially outperformed both pipeline variants, achieving 96.7% accuracy and near-perfect agreement with expert coding ({kappa} = 0.966) at the three-character level. The LLM also assigned clinically plausible codes to 82.4% of rejected mentions, 62.8% of Tier-3 exclusions, and 92.3% of review-band mentions. Conclusion Automated harmonization of clinical trial condition data across heterogeneous registries is feasible and supports the use of a common ICD-10 framework for cross-registry analyses. The LLMs achieved high agreement with expert coding, and performed better than the deterministic ontology and embedding pipeline, which achieved moderate agreement. These findings indicate that LLMs can support analyses of the distribution of health conditions investigated in clinical trials in Germany.They are a promising tool for classification of other non-standardised trial characteristics in registries. Keywords: Clinical trial registries; ICD-10; disease harmonization; UMLS; entity linking; SapBERT; large language models; clinical research; natural language processing.
R. Ndabashinze, D. Franzen, E. Kozuch et al.· medRxiv· 0 citations
Abstract Background Semantic interoperability, the ability of disparate health information systems to exchange and consistently interpret clinical data, is a cornerstone of modern digital health, underpinning cross-institutional research, real-world evidence generation, and global health surveillance. Laboratory tests constitute one of the richest clinical data sources, yet multilingual variation and institution-specific naming conventions severely impede their standardized integration across systems. Objective We propose LabBridge, a large language model (LLM)–based agentic framework designed to standardize laboratory tests to the Logical Observation Identifiers Names and Codes (LOINC) standard, enabling cross-lingual semantic interoperability while minimizing reliance on language-specific rules and manual curation. Methods LabBridge integrates linguistic normalization, hybrid retrieval (combining domain-adapted embeddings with the LOINC ontology structure), and constrained LLM reasoning within an agentic workflow that enforces terminological consistency and traceability. We evaluated the framework on 2 real-world laboratory datasets from emergency department patients, one in Chinese and one in English, representing cross-lingual and cross-institutional heterogeneity. Performance was assessed across 5 LLMs and compared with vector-based baseline (BGE-M3, Beijing Academy of Artificial Intelligence) and retrieval-augmented generation (RAG) approaches, using mapping accuracy against a curated reference set of clinically relevant LOINC core codes as the primary metric. Results At full coverage (Top@100%), LabBridge achieved 81% to 90% LOINC mapping accuracy across 5 LLMs on both Chinese and English datasets, outperforming all baseline methods (P<.01). On the Chinese dataset, it improved over BGE-M3 by 66 percentage points (90% vs 24%) and over the best RAG method by 41 percentage points (90% vs 49%). On the English dataset, gains ranged from +4 to +19 percentage points over RAG baselines. The framework maintained robust performance across frequency strata, including the relatively lower-frequency stratum within the selected evaluation datasets. Notably, on the Guangdong Provincial People’s Hospital (GDPH) dataset, LabBridge achieved 85% accuracy on high-frequency terms (Top@30%) compared to 93% on low-frequency terms (Bottom@30%). The highest accuracy, 90% in both the languages, was achieved using DeepSeek-V3 (Hangzhou DeepSeek Artificial Intelligence Co, Ltd), with GPT-4o (OpenAI; 88%‐89%) and GPT-4o-mini (OpenAI; 87%‐90%) showing comparable results. Conclusions LabBridge demonstrates that embedding LLMs with an ontology-aware, agent-coordinated architecture enables effective standardization of laboratory data. By unifying semantic retrieval, linguistic normalization, and constrained reasoning, the framework accelerates the terminology standardization process by transforming expert effort from manual code lookup to candidate verification. These findings offer a practical pathway toward scalable, auditable semantic interoperability in health care ecosystems.
Lijuan Wu, Jinxin Huang, Hongnian Wang et al.· Journal of Medical Internet...· 0 citations
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