AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-24
New papers on neurological rare and neuroimmune diseases 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 9 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: Basic research（1） · ALS（0） · Parkinson's（0） · Hereditary neurodegeneration（0） · Neuromuscular（0） · Multiple sclerosis（0） · NMOSD/MOGAD（0） · Myasthenia gravis（0） · Autoimmune encephalitis/CIDP/GBS（0）
Neurodegeneration and neuroimmunology research and AI
- Abstract
Background: Artificial intelligence (AI) has achieved notable success in closed‐set classification of neurodegenerative diseases, but its performance degrades significantly in more realistic open‐set settings, such as discovering rare or novel disease subtypes from unlabeled data that are not seen during training.
Purpose: This study introduces, to our knowledge, the first framework for generalized category discovery (GCD) in neurodegenerative diseases using MRI. It aims to leverage labeled data from known categories to automatically identify novel categories within unlabeled datasets.
Methods: We propose a GCD framework integrating semi‐supervised contrastive learning with global‐local representation learning to jointly optimize feature learning. Globally, we enforce consistency between weakly augmented views of the same instance by aligning their similarity relationships with other embeddings in a support queue. Locally, we treat neighborhood embeddings from the support queue as positive pairs for cross‐instance contrastive learning, enhancing fine‐grained discrimination. This dual strategy results in a unified learning objective that is applicable to both labeled and unlabeled data, effectively narrowing the representation gap among semantically similar instances.
Results: We evaluate our method on a benchmark dataset comprising six neurodegenerative diseases, with over 1500 patient samples. Experimental results demonstrate that our framework consistently outperforms representative state‐of‐the‐art methods. Specifically, our method achieves 66.02% Accuracy and 60.59% Normalized Mutual Information on all categories, and 54.53% Accuracy and 16.67% Normalized Mutual Information on novel categories, surpassing existing state‐of‐the‐art methods.
Conclusions: Our framework underscores the model's robustness and its practical value in discovering new neurodegenerative disease conditions, highlighting its potential for advancing open‐set diagnostic AI.
Journal IF-equivalent: 3.0 (OpenAlex 2-year mean citedness, value as of 2026-10-23, retrieved 2026-10-24; not the official Clarivate IF)Reference: Shen Y, Yang X, Hu Y, Qiao K, Hai J, Chen J, et al. Learning global and local representations for generalized category discovery in neurodegenerative diseases. Medical Physics. 2026 Oct 22;53(11):e70686. doi:10.1002/mp.70686.Checked: Abstract only
See all 1 Basic research papers →
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
No new qualifying paper for this issue.
Hereditary neurodegenerative diseases: treatment, trials and AI
No new qualifying paper for this issue.
Neuromuscular diseases (muscular dystrophy, SMA): treatment, trials and AI
No new qualifying paper for this issue.
Multiple sclerosis: treatment, trials and AI
No new qualifying paper for this issue.
NMOSD and MOGAD: treatment, trials and AI
No new qualifying paper for this issue.
Myasthenia gravis: treatment, trials and AI
No new qualifying paper for this issue.
Autoimmune encephalitis, CIDP and Guillain-Barre syndrome: treatment, trials and AI
No new qualifying paper for this issue.