AI and Rare Diseases ── 2026-10-10
New papers on rare diseases (other than neurological and cancer) and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 8 disease groups, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by disease: Platforms（1） · Inborn errors of metabolism（0） · Genetic syndromes（1） · Rare blood disorders（0） · Immune and connective tissue（0） · Heart and lung（2） · Kidney, liver and gut（0） · Skin and bone（0）
Rare-disease diagnosis and drug discovery platforms and AI
- Abstract
Background: The low prevalence and heterogeneous nature of rare diseases make them particularly difficult to diagnose and prognosticate. These issues are amplified in the paediatric population, who are disproportionately affected by rare diseases. Non-generative artificial intelligence (AI) is a powerful tool that can aid clinicians in diagnostics, risk stratification and patient subgrouping. The extent to which this has been applied to rare diseases, in particular paediatric rare disease cohorts, is not well defined.
Objective: This scoping review aimed to identify how AI techniques have been used to aid diagnosis and prognosis of rare diseases, with a focus on their use in paediatric populations.
Methods: Embase, Medline, Web of Science, IEEE Xplore and Scopus databases were searched for original articles using the search terms “(‘machine learning’ OR ‘artificial intelligence’) AND (‘rare’ OR ‘orphan’) AND (‘condition*’ OR ‘disease*’ OR ‘disorder*’)”.
Results: One hundred and thirty-six studies met inclusion criteria, 28 (20.6%) of which used paediatric cohorts, with a variety of study types [diagnostic ( n = 61, 44.9%), prognostic ( n = 39, 28.7%), classification ( n = 19, 14.0%) and screening ( n = 17, 12.5%)]. The number of participants per study ranged from 16 to over 3 million. Tree-based models were the most frequently used in the studies ( n = 62, 45.6%) followed by neural networks ( n = 49, 36.1%) and linear models ( n = 46, 33.9%); the most frequent methods used in paediatric cohorts were neural networks ( n = 11, 39.2%), tree-based ( n = 10, 35.7%), and linear ( n = 10, 35.7%) models. Pre-processing steps were only described in a small number of studies, including feature selection ( n = 41, 30.1%), handling data missingness ( n = 36, 19.1%), data cleaning ( n = 44, 32.4%), addressing data imbalance ( n = 21, 15.4%), and data augmentation ( n = 23, 16.9%). Only 27 (19.9%) models were externally validated. Clinician involvement was described in 56 studies (41.2%), and no models had been translated into regular use in clinical practice.
Conclusions: AI tools to aid diagnosis and prognosis in rare diseases are increasingly being investigated, but their use for paediatric rare disease cohorts remains scarce. Limited methodological description of model development, clinician involvement and lack of external validation means there has been poor translation of models into clinical practice.
Journal IF-equivalent: 3.4 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Taylor CS, Lai A, Kaski JP, Norrish G. A scoping review and comparison of the current state of non-generative artificial intelligence in the diagnostics and prognostics of rare diseases – focusing on paediatric populations. Orphanet J Rare Dis. 2026 Oct 8 [Epub ahead of print]. doi:10.1186/s13023-026-04619-5.Checked: Abstract only
Inborn errors of metabolism: treatment, trials and AI
No new qualifying paper for this issue.
Genetic syndromes and paediatric rare diseases: treatment, trials and AI
- Abstract
OBJECTIVE: To conduct a pilot study to assess whether saliency-based explainable artificial intelligence (XAI) affects the recognition of genetic conditions from facial images. METHODS: Forty-four medical geneticists, divided into AI-only and XAI-supported groups, assessed 18 images of individuals with or without genetic conditions. Diagnostic accuracy and confidence were recorded before and after viewing an AI classifier's prediction probability, with or without XAI explanations. Mediation analyses were conducted to better interpret how geneticists interact with AI and XAI in decision-making. RESULTS: AI-only and XAI support improved accuracy for correct AI classifications, while incorrect AI classifications decreased accuracy. Average confidence increased with correct and decreased with incorrect classification. Geneticists reported that AI prediction probability was useful, whereas XAI explanations were viewed less favorably. For incorrect AI classifications, there was a negative correlation between accuracy improvement and perceived AI usefulness. When AI was correct (without XAI), the model prediction probability acted as a mediator between user confidence and the user's decision to choose the same answer as AI. CONCLUSION: The lack of accuracy or confidence improvements indicates that participants did not integrate saliency-based XAI into decisions. AI prediction probability had a greater impact on participants' decision-making. These data may help inform larger studies.
Journal IF-equivalent: 0.5 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Sümer Ö, Huber T, Cheng J, Duong D, Ledgister Hanchard SE, Conati C, et al. Application of deep learning and explainable AI-supported medical decision-making for facial phenotyping in genetic syndromes. Clinical Dysmorphology. 2026 Oct 8 [Epub ahead of print]. doi:10.1097/mcd.0000000000000581.Checked: Abstract only
See all 1 Genetic syndromes papers →
Rare blood disorders: treatment, trials and AI
No new qualifying paper for this issue.
Autoinflammatory, immunodeficiency and rare connective-tissue diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare cardiovascular and respiratory diseases: treatment, trials and AI
- Abstract
Timely detection is crucial to improve outcomes in patients with cardiac amyloidosis (CA) by initiation of guideline-directed disease-modifying treatments. Although confirmatory bone scintigraphy is highly accurate for CA detection, identifying at-risk patients for referral remains challenging. This study aimed to develop and validate a machine learning model, Amylo-Detect , using structured multimodal electronic health record (EHR) data to guide referrals for confirmatory scintigraphy and monoclonal protein testing. Consecutive all-comer patients (n = 11,616) referred for bone scintigraphy at the Vienna General Hospital (2010–2023) were retrospectively included. Patients referred before August 2020 formed the development cohort. The remaining patients comprised the internal validation cohort. External validation was performed at the University Hospital Essen (n = 1,521). Amylo-Detect was trained using 50 routinely available parameters to predict CA-suggestive uptake (Perugini grade ≥2) and compared with an existing score and clinical routine. High-grade uptake was present in 388 patients (3.0%). Amylo-Detect demonstrated excellent performance in development (AUC 0.93), independent internal validation (AUC 0.91), and external validation cohort (AUC 0.91), outperforming existing scoring systems and clinical routine. Results were consistent across subgroups, even when crucial predictors were missing. Of the 42/388 (10.8%) patients missed in clinical routine, 12/42 (29%) were additionally detected by Amylo-Detect . The model further conveyed significant prognostic value for mortality and heart failure hospitalization. We present Amylo-Detect , a validated EHR-based tool for CA risk prediction, available as a web app, allowing application and further evaluation. By improving timely detection and referral, Amylo-Detect may help address diagnostic delays, pending prospective evaluation.
Journal IF-equivalent: 6.5 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Spielvogel CP, Kersting D, Haberl D, Autherith M, Hauptmann L, Yu J, et al. Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study. PLOS Digit Health. 2026 Oct 8;5(10):e0001637. doi:10.1371/journal.pdig.0001637; PMCID: PMC13649026.Checked: Full text checked - Abstract
Differentiating pulmonary arterial hypertension (PAH) from pulmonary hypertension associated with left heart disease (PH-LHD) is clinically important because management differs substantially, but definitive classification requires invasive haemodynamic assessment together with clinical evaluation. We retrospectively studied 905 patients with PAH or PH-LHD treated at Shanghai Pulmonary Hospital and developed an adaptive heterogeneous graph neural network (AHGNN) that integrates contrast-enhanced thoracic CT images with noninvasive clinical variables. The model combines differentiable graph construction using Gumbel-Softmax reparameterization, hierarchical cross-modal attention, and dual-level self-supervised contrastive learning. Across 100 outer test folds from repeated patient-level five-fold cross-validation, AHGNN achieved a mean area under the receiver operating characteristic curve (AUC) of 0.946 ± 0.023 and a precision–recall AUC of 0.952 ± 0.009. At a fixed probability threshold of 0.50, sensitivity was 0.867 ± 0.070 and specificity was 0.867 ± 0.072. The Brier score was 0.139, although a calibration slope of 3.547 indicated that recalibration may be required. These findings support the potential of multimodal noninvasive data to assist referral and diagnostic triage. Prospective external validation, recalibration, and clinical safety evaluation are required before clinical use or any change to the role of right heart catheterization.
Journal IF-equivalent: 13.7 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Shen Y, Mi J, Zhao X, Liu G, Yang G, Yang B, et al. Adaptive heterogeneous graph neural networks for differentiating pulmonary arterial hypertension from left heart disease. npj Digit Med. 2026 Oct 8 [Epub ahead of print]. doi:10.1038/s41746-026-03305-x.Checked: Abstract only
See all 2 Heart and lung papers →
Rare kidney, liver and gastrointestinal diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare skin and bone diseases: treatment, trials and AI
No new qualifying paper for this issue.