cs.AIOct 16, 2026
SaveUnified Multiparadigm Fusion Intelligence for Rare Disease Diagnosis
Organizations: The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, China. · The College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Abstract
Rare diseases (RDs) affect an estimated 300 million people globally, leaving patients to navigate a protracted diagnostic odyssey constrained by clinical scarcity and fragmented medical knowledge. Artificial intelligence (AI) has progressively advanced RD diagnostics, from symbolic methods and deep learning to large language model (LLM)-based systems. However, each paradigm carries structural limitations: symbolic methods are bounded by knowledge completeness, neural models sacrifice interpretability, and LLM-based systems are vulnerable to evaluation biases like data leakage. In this study, we introduce FusionRare, a unified multiparadigm fusion framework integrating knowledge-driven symbolic reasoning, deep-learning pattern recognition, case-based patient retrieval, and LLM-based heuristic inference via statistical meta-aggregation and knowledge-enhanced agentic reflection. Evaluated on 2,528 strictly isolated clinical cases, FusionRare achieves state-of-the-art RD prioritization, attaining a macro-averaged Recall @1 of 51% for phenotype-only diagnosis and 79% for phenotype-genotype joint analysis. These results represent decisive advantages over the advanced agentic system DeepRare, frontier LLMs including GPT 5.5, and specialized tools like PhenoBrain and LIRICAL under stringent anti-leakage protocols. A randomized, crossover clinician-AI validation demonstrated that FusionRare elevates the true diagnostic rank in 45% of decisions, improves physician confidence in 62%, and reduces time to diagnosis by 21%. These findings indicate that reconciling these orthogonal AI paradigms significantly enhances diagnostic capabilities, bridging the gap between computational benchmarks and actionable clinical reliability. More broadly, this work provides a replicable framework for deploying trustworthy diagnostic AI across complex, high-stakes, and underserved medical domains.
Figures & tables
Figure 1: Study overview. (a) Data foundation. Construction of the Rare Disease Knowledge Base (RDKB) and Patient Bank under rigorous antileakage protocols, ensured strict isolation between developmental and evaluation datasets. (b) FusionRare diagnosis engine. The multiparadigm pipeline integrates four orthogonal reasoning modules whose outputs are synthesized via statistical aggregation and refined by agentic LLM reflection. The pipeline yields prioritized diagnostic candidates and their spatial localization on the navigable RDmap. (c) Performance benchmarking and clinical validation. Evaluation of FusionRare across phenotype-only, cross-specialty, phenotype-genotype, and clinical application settings, revealed that the performance of FusionRare was consistently superior to that of SOTA methods and that it has tangible clinical utility.
Figure 2: Diagnostic performance across datasets, specialties, and complexities. (a) Benchmarking on three independent cohorts. (b) Performance across medical specialties. Radar charts illustrating Recall@ at varying ranks ( ) across 12 major disease categories: I, Nervous ( =1442); II, Musculoskeletal (828); III, Development/Morphogenesis (765); IV, Cancer/Tumour (740); V, Endocrine (681); VI, Immune (613); VII, Connective tissue (598); VIII, Metabolic (591); IX, Cardiovascular (465); X, Haematological (396); XI, Visual (380); and XII, Reproductive (355). (c) Impact of phenotypic complexity. Diagnostic recall is stratified by the number of HPO terms per case. (d) Clinical consistency evaluation of FusionRare against SOTA systems (DeepRare, GPT-5.5, PhenoBrain, and DeepSeek-R1) using manually curated challenge subsets. Figure Notes: Data in (a, c, d) are presented as the mean 95%CI. Statistical significance was determined using a one-sided bootstrap test: * , ** , *** .
Figure 3: Integrated phenomic-genomic diagnostic performance and stability analysis . (a) Comparative benchmarking. Recall@ for gene prioritization (top), disease prioritization (middle), and phenotype-only disease prioritization (bottom). (b) Diagnostic stability. Performance curves showing the mean Std across ten independent trials with heterogeneous genomic backgrounds. Figure Notes: Data in (a) are presented as the mean 95%CI; Shaded areas in (b) denote Std; Statistical significance was determined using a one-sided bootstrap test: * , ** , *** .
Figure 4: Clinical validation of FusionRare. (a) Multispecialty randomized crossover clinical trial design. (b) Diagnostic accuracy of clinicians across three evaluation stages. Accuracy is represented as the macroaverage Recall@ ( ) across individual clinicians for initial routine evaluation, re-evaluation with FusionRare, and crossover evaluation of de novo cases with FusionRare in the form of box plots. Box plots show the median (centreline), upper and lower quartiles (box limits), and whiskers ( interquartile range). The horizontal dashed lines indicate the performance of FusionRare. (c) Clinician diagnostic efficiency. The bars represent the mean time to diagnosis per case. (d, e) Trajectories of diagnostic refinement and confidence modulation. Sankey diagrams illustrate the flow of 500 paired decisions from initial routine assessment to FusionRare-assisted re-evaluation. Red flows (d) highlight the restratification of diagnostic ranks, while green flows (e) depict the evolution of subjective diagnostic confidence. (f) Case-level harmonization of diagnostic accuracy and consensus. The scatter&vector plot visualizes the topological shift of 50 individual cases from routine diagnosis (empty markers) to FusionRare-assisted diagnosis (filled markers) states (Y-axis: proportion of correct clinician diagnoses; X-axis: pairwise agreement rate among clinician diagnoses). Marginal density curves illustrate the score distributions of the correct diagnosis rate (right) and consistency (upper) among clinicians operating in routine (blue curves) versus assisted (orange curves) diagnostic settings. Figure Notes: In (b), the statistical significance between re-evaluation and initial evaluation was assessed using the Wilcoxon signed-rank test, whereas the significance between crossover evaluation and initial evaluation was assessed using the Mann-Whitney U test. * , ** , *** .
Figure 5: Ablation study and mechanistic insights into FusionRare. (a) Synergy of source paradigms. Comparison of diagnostic recall across three cohorts for the full pipeline: source paradigms, statistical aggregation (FusionL2), agentic refinement (ReflectionL3), and ablated versions of FusionL2. (b) Genomic enhancement. Significant performance improvement provided by the FusionL2 (Joint) model for the public-variant cohort. (c, d) Foundation LLM sensitivity. Effects of the underlying LLM backbones (DeepSeek-R1, DeepSeek-V3, GPT-4o, and GPT-5.5) on FusionL2 performance (c) and absolute recall gains (versus the FusionL2 baseline) of ReflectionL3 (d). (e, f) Robustness across complexities and specialties. Performance stability of the fusion hierarchy stratified by HPO term count (e) and across 12 medical specialties (f). (g) Evolution of the “Fusion” workflow. Upset plot (left) showing the intersection of successful diagnoses among source paradigms, and a Sankey diagram (right) illustrating the stepwise promotion of true diagnoses from Round 1 (source paradigms) through Round 2 (statistical aggregation) to Round 3 (agentic reflection). Figure Notes: Data in (a-e) are presented as the mean 95%CI; statistical significance was determined based on a one-sided bootstrap test: * , ** , *** ; statistical significance is assessed against FusionL2 (Meta) for (a), FusionL2 (Joint) for (b), FusionL2 powered by DeepSeek-R1 for (c), FusionL2 for (d), and ReflectionL3 for (e); statistically significant markers: black markers indicate significantly lower performance than baseline, while red indicates significantly higher performance; the disease category labels in (f) correspond to those in Fig. 2 b.
Figure 1: Overview of FusionRare system.
Figure 2: Details of RD case dataset curation. The flowchart delineates the systematic pipeline for data standardization, quality control, and rigorous isolation between the developmental and evaluation sets. (i) Data acquisition and preliminary filtering. Initial patient records ( across 12 sources) were aggregated from public literature, web registries, and multicentre EHR systems (PUMCH and ZJUMCH). To ensure diagnostic fidelity, cases were excluded if their clinical diagnoses were not mappable to the RDKB (OMIM/ORPHA). (ii) Quality control and standardization. Phenotypic profiles were subjected to a minimum-density threshold, requiring at least three clinical descriptors (signs or symptoms). All free-text clinical notes were standardized to HPO terms. To align with the scope of the diagnostic engine, we excluded cases with ground-truth diseases that lacked phenotype annotations in the RDKB. (iii) Anti-leakage and strategic partitioning. After global deduplication, the datasets were stratified on the basis of provenance and utility. The training/retrieval dataset ( ) was exclusively sourced from the literature and web registries (e.g., RareArena and DECIPHER) to populate the RNC classifier and RPB index. For the Public Test Cohort ( ), a secondary similarity-based filter was applied: any case sharing a PMID with the training set was retained only if its HPO Jaccard similarity to all training instances was , effectively precluding pattern memorization. (iv) Independent clinical validation. The Testing Dataset ( ) comprised the filtered Public cases and two strictly isolated EHR cohorts (PUMCH and ZJUMCH). Within this testing dataset, a Challenge Subset ( ) was randomly sampled for clinician-led manual audits (100 cases per source). This multilayered isolation ensured that the evaluation measured genuine cross-institutional generalizability rather than data-driven artefacts.
Figure 3: User interface and clinical decision-making workflow of the FusionRare web platform (http://rdx.nbscn.org). (a) Clinical input module, demonstrating the extraction of standardized HPO terms from unstructured clinical narratives. (b) Automated diagnosis panel, illustrating the hierarchical pipeline from source-level diagnostic engines to statistical aggregation and final agentic refinement. (c) Knowledge-grounded Chain-of-Thought reflection, where the LLM provides an auditable, transparent reasoning process that synthesizes evidence across paradigms to justify the final prioritized differential diagnosis. (d, e) The global disease-phenotype landscape, rendered as a navigable RDmap, allows clinicians to visualize the topological proximity between the patient’s profile and broader RD disease clusters. The RDmap in (d) is colour-coded according to medical specialty, while the RDmap in (e) highlights the top 10 candidate diseases.
| Original Features | Processed Features | |||||||||
| Source | Country/ Region | Case-level PMID | Diag. Code | HPO Anno. | Var. | Total Case | Total Dis. | Valid Cases | Valid Dis. | Avg. HPOs |
| A. Literature-from Public Datasets | ||||||||||
| RareArena 29 | Multi-Country | ● | ORPHA | ○ | ◐ | 49,760 | 4,597 | 27,185 | 2,510 | 11.5 |
| Phenopacket 30 | Multi-Country | ● | OMIM | ● | ● | 8,101 | 516 | 5,561 | 349 | 9.0 |
| LIRICAL 9 | Multi-Country | ● | OMIM | ● | ● | 384 | 262 | 374 | 256 | 11.3 |
| RDmaster 51 | Multi-Country | ● | ORPHA | ● | ○ | 201 | 62 | 199 | 62 | 10.0 |
Table 1: Summary of rare disease case collections across public and clinical sources
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 1: An example of the automated extraction and annotation of rare disease case reports. The workspace presents a structured summary of a Kabuki syndrome case (PMID 34675602), consisting of: (i) Basic Information and Case Report (left): Extracted clinical metadata, disease classification codes, and the original narrative text of the case. (ii) Phenotypes (right): A circular visualization mapping the patient’s presentation to Human Phenotype Ontology (HPO) terms, along with a hierarchical tree view highlighting specific features (e.g., unilateral microphthalmos). (iii) Images (bottom): Extracted figures from the source article, including patient clinical photographs, pedigree charts, and Sanger sequencing electropherograms showing the de novo mutation.
Figure 1: Ablation analysis of the Residual Network Classifier (RNC) and training data augmentation. (a) Component-level ablation study of the RNC architecture across three evaluation cohorts. The “Full RNC” model integrates the knowledge-based prior and the residual branch, whereas the “Prior knowledge branch only” and “Residual branch only” configurations demonstrate hierarchical dependency within the architecture. The negligible performance of the “Residual branch only” model indicates that the residual branch functions as an error-rectification layer rather than an independent predictor. (b) Impact of synthetic patient data augmentation on diagnostic performance. We compare the training efficacy of utilizing both historical RPB cases and simulated cases against subsets using only historical data or only simulated cases. While training on historical data alone is sufficient for the Public dataset, it demonstrates limited cross-institutional generalizability; the integration of simulated cases across the full RD spectrum significantly improves performance for clinical cohort (ZJUMCH and PUMCH) data, confirming that synthetic augmentation enhances model robustness and cross-centre generalization.
Figure 1: Automated weight optimization and discrete regularization for meta-aggregation. The figure illustrates the two-stage process for defining the optimal consensus logic within the FusionR2 engine. Left: Weight Optimization Result. We utilized the Optuna framework to conduct an automated hyperparameter search across eight distinct mathematical philosophies (Bates 1 , CombSUM 2 , CombMNZ 2 , Min 1 , Geometric 1 , RRA 1 , Copeland 3 , and Stuart 4 ). The bars represent the mean performance contribution (with standard deviation) calculated for an independent validation set to maximize cumulative recall. Right: Weight Regularization. To prevent overfitting to specific validation distributions and ensure numerical stability, continuous weights were subjected to tri-level quantization (mapped to discrete values of 0, 0.5, 1.0). Four methods (CombSUM, RRA, Copeland, and Stuart) were regularized to zero because of marginal or redundant contributions and were subsequently excluded from the primary architecture. This sparse-weight strategy ensures that the final meta-aggregation focuses on the most complementary and robust reasoning vectors.
Table 1: NASA Task Load Index (NASA-TLX) questionnaire for cognitive load assessment.
More in this topic
Rare Disease Diagnosis
Rare disease diagnosis uses clinical, genetic, and imaging evidence to identify uncommon conditions and guide patient care.
See all 31 papers