AI and Rare Diseases ── 2026-10-17
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（0） · Rare blood disorders（0） · Immune and connective tissue（0） · Heart and lung（1） · Kidney, liver and gut（0） · Skin and bone（0）
Rare-disease diagnosis and drug discovery platforms and AI
- Abstract
Phenotyping is fundamental to rare disease diagnosis, but manually curating structured phenotypes from clinical notes is labor intensive and difficult to scale. Existing artificial intelligence (AI) approaches optimize individual components of phenotyping but generally do not operationalize the full workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing rare disease-relevant HPO terms. We developed RARE-PHENIX, an end-to-end AI framework for rare disease phenotyping that integrates large language model-based phenotype extraction, ontology-grounded standardization to HPO terms, and supervised ranking of HPO terms. We trained RARE-PHENIX using data from 2671 patients across 11 Undiagnosed Diseases Network clinical sites, and externally validated it on 16,357 real-world clinical notes from 143 patients at Vanderbilt University Medical Center. Our findings suggest RARE-PHENIX generates structured, prioritized HPO terms highly concordant with clinician curation, demonstrates potential downstream disease-prioritization signal, and may support human-in-the-loop rare disease phenotyping in real-world settings.
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: Shyr C, Hu Y, Tinker RJ, Cassini TA, Byram KW, Hamid R, et al. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models. npj Digit Med. 2026 Oct 15 [Epub ahead of print]. doi:10.1038/s41746-026-03275-0.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
No new qualifying paper for this issue.
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
BACKGROUND Pulmonary arterial hypertension (PAH) is a rare and progressive disease associated with substantial clinical heterogeneity and high health care costs. While machine learning (ML) has been increasingly applied to predict clinical outcomes, approaches that integrate clinical prediction with downstream economic modeling remain limited. OBJECTIVE This study proposes and evaluates an ML-based framework that combines prediction of future functional status with estimation of associated health care costs in patients with PAH. METHODS In this retrospective cohort study, data from 181 patients with PAH were analyzed. A total of 6 supervised ML algorithms (Least Absolute Shrinkage and Selection Operator [LASSO] regression, ridge regression, k-nearest neighbors, decision trees, random forest, and gradient boosting) were trained to predict the relative change in World Health Organization functional class (WHO-FC) from baseline to 12-month follow-up using baseline clinical variables. Predicted change rates were subsequently converted to predicted WHO-FC values at the 12-month follow-up for downstream health care cost estimation. Missing values in predictor variables were imputed using median substitution. Median imputation was performed before the dataset was split into training and test sets. Model performance was evaluated using Pearson correlation coefficient (r), and error metrics (RMSE, MAE, and MedAE), based on cross-validation and independent test set validation. Predicted WHO-FC values were subsequently linked to externally validated, stratified real-world health care cost estimates to approximate future economic burden. RESULTS Among the evaluated models, k-nearest neighbors showed the highest predictive performance (r=0.40; R2=0.16), followed by LASSO regression (r=0.30; R2=0.09). Prediction errors were comparable across models. Estimated mean health care costs ranged from US $12,481 to $12,614 per patient per month. Despite a significant improvement in WHO-FC over 12 months, estimated health care costs remained largely unchanged, reflecting persistent economic burden across functional class strata. CONCLUSIONS This study demonstrates the feasibility of an ML-based framework that integrates prediction of functional status with health care cost estimation in PAH. While predictive performance was moderate, the approach illustrates how ML models can be extended beyond clinical end points toward system-level applications such as cost forecasting and resource planning. Further validation in larger, multicenter cohorts is required before clinical or health system implementation.
Journal IF-equivalent: 5.9 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)Reference: Kramer M, Kramer T. A Machine Learning Framework for Predicting Functional Status and Estimating Health Care Costs in Pulmonary Arterial Hypertension: Retrospective Cohort Study. JMIR Med Inform. 2026 Oct 15;14:e97372. doi:10.2196/97372.Checked: Abstract only
See all 1 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.