AI and Rare Diseases ── 2026-10-08
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（1） · Immune and connective tissue（0） · Heart and lung（0） · Kidney, liver and gut（0） · Skin and bone（0）
Rare-disease diagnosis and drug discovery platforms and AI
- Abstract
Diagnostic performance varies across glomerular disease (GD) subtypes, and whether artificial intelligence (AI) outperforms pathologists with different experience levels remains uncertain. To evaluate pathology-based AI models for GD classification and compare their performance with pathologists. PubMed, Embase, Web of Science, and Cochrane Library were searched through 15 July 2026. Studies using pathology images and pathology diagnosis as the reference standard were included. Random-effects models pooled sensitivity, precision, accuracy, F1 score and area under the curve (AUC). Fifteen studies comprising 39,536 validation sample units, not necessarily unique patients, were included. For subtypes with at least 10 validation datasets, AI achieved high performance for membranous nephropathy (MN; sensitivity 0.96, precision 0.94, accuracy 0.96, F1 score 0.95, AUC 0.98), IgA nephropathy (IgAN; sensitivity 0.92, precision 0.91, accuracy 0.94, F1 score 0.90, AUC 0.96), and minimal change disease (MCD; sensitivity 0.92, precision 0.87, accuracy 0.96, F1 score 0.89, AUC 1.00). AI also showed higher accuracy than senior pathologists for IgAN, MN, and MCD; however, comparator evidence was sparse and should be interpreted cautiously. Most included studies were retrospective, and substantial heterogeneity was observed across datasets, imaging modalities, model architectures, and validation strategies. Pathology-based AI shows strong potential for GD classification, but current head-to-head evidence is insufficient to establish superiority over pathologists, particularly senior pathologists. Prospective multicenter studies integrating multimodal clinical data and standardized external validation are needed. What is known: Artificial intelligence based on renal pathology images is being developed to support classification of glomerular disease. What this study adds: This systematic review synthesizes pathology-image AI models and evaluates performance in relation to validation context and heterogeneity. Potential impact: These models may support reproducible screening and quantitative assessment under pathologist oversight, but prospective multicenter validation remains necessary. Artificial intelligence can help analyze kidney biopsy images. Across 15 studies, AI models often showed high performance for several glomerular diseases, including membranous nephropathy, IgA nephropathy, and minimal change disease. However, most studies were retrospective and differed in imaging methods and validation designs, so these results should be viewed as promising evidence for decision support rather than proof that AI can replace pathologists.
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Artificial intelligence-powered renal pathology for glomerular disease classification: a systematic review and meta-analysis. Figshare. [Epub ahead of print]. doi:10.6084/m9.figshare.34120440.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
- Abstract
Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage–RBC signaling with Dissipative Particle Dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for simultaneous parameter inference, hidden-state reconstruction, and integration of multiscale mechanistic constraints. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage–RBC contact, generating spatially resolved trajectories of CD47–SIRPα signaling and antibody–receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47–SIRPα–SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRPα antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis.
Journal IF-equivalent: 7.1 (OpenAlex 2-year mean citedness, value as of 2026-10-07, retrieved 2026-10-08; not the official Clarivate IF)Reference: Chai Z, Daryakenari NA, Karniadakis GE. A multiscale signaling–biophysical framework reveals mechanisms of macrophage-mediated RBC clearance in sickle cell and gaucher disease. PNAS Nexus. 2026 Oct 6 [Epub ahead of print]. doi:10.1093/pnasnexus/pgag348.Checked: Abstract only
See all 1 Rare blood disorders papers →
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
No new qualifying paper for this issue.
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.