AI and Rare Diseases ── 2026-10-29
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（0） · 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
No new qualifying paper for this issue.
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
Duplicate views, annotation overlays and acquisition characteristics can bias performance measures, while small microscopy datasets can enable valuable image-classification experiments. This study assesses a reproducible classical machine-learning workflow for the classification of sickle-cell positive and negative blood-smear fields, to include explicit checks for these risks. The provided archive had 991 JPEG files. Positive views were ignored if they were annotated, duplicate pixels were deleted, and groups of candidate duplicate images with conflicting annotations were quarantined. The final analysis consisted of 565 images (421 development; 144 held-out test) split by similarity-group-disjoint partitioning. Five-fold development cross validation was used to compare four image models. We used a separate random forest model designed for prediction of non-biological predictive signal. The hybrid-SVM was chosen prior to the final test scoring, and it obtained accuracy 94.69%, macro-F1 93.19%, sensitivity 95.24%, specificity 93.10% and ROC-AUC 0.961. The accuracy interval for 95% of the grouped-bootstrap values was 90.27–98.33%. The metadata-only model has acquired a ROC-AUC of 0.767, showing acquisition-associated label information.
Conclusions: The workflow offers an image-level benchmark reference and an implementation that can be reused. Internal discrimination alone does not provide patient level diagnostic validity and patient-linked and external validation are still required.
Journal IF-equivalent: 0.1 (OpenAlex 2-year mean citedness, value as of 2026-10-27, retrieved 2026-10-29; not the official Clarivate IF)Reference: Samea Sakban Abd. Towards Reliable AI-Assisted Detection of Sickle Cell Disease: A Machine Learning Framework Accounting for Acquisition Bias, Designed for the Classification of Blood Smear Images. JFES. 2026 Oct 26;5(1):607-23. doi:10.59746/tzwyez52.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.