AI and Rare Diseases ── 2026-10-05
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（0） · Immune and connective tissue（1） · 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
No new qualifying paper for this issue.
Autoinflammatory, immunodeficiency and rare connective-tissue diseases: treatment, trials and AI
- Abstract
Early diagnosis of Systemic Sclerosis (SSc), especially in the edematous phase, is challenging. This study developed an Artificial Intelligence (AI) model capable of distinguishing the edematous phase of SSc from other clinically similar conditions and to evaluating its accuracy. Short video clips were collected from two groups: (a) SSc patients with puffy skin in the hands and/or feet during the edematous phase and (b) non-SSc conditions with edema requiring differentiation from SSc. AI development involved analyzing skin responses to finger pressure, recording the pressing and rebound phases. The collected videos were divided into three sets: 70% for AI training, 10% for validation, and 20% for accuracy testing. A total of 2,080 videos from 22 SSc and 38 non-SSc patients were analyzed. Among the 38 non-SSc cases, the most common underlying conditions were renal disease and nephrotic syndrome (15 cases, 39.5%), followed by deep venous thrombosis (6 cases, 15.8%) and left- or right-sided heart failure (5 cases, 13.2%). Five AI models were evaluated at accuracy thresholds of 70%, 75%, 80%, and 85%. At thresholds of 70–80%, all models achieved sensitivity above 99% and specificity exceeding 98%. The extra-large model demonstrated the highest sensitivity, specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) at the 85% threshold, outperforming the nano, small, medium, and large models. The developed AI model demonstrated high accuracy in distinguishing the edematous phase of SSc from other similar skin edema across multiple confidence thresholds, supporting its potential as an effective diagnostic tool.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Foocharoen C, Sonsilphong S, Onchan T, Pongkulkiat P, Mahakkanukrauh A, Suwannaroj S, et al. Artificial Intelligence Distinguishing Edematous Phase of Systemic Sclerosis from Mimickers: A Video-Based Diagnostic Study. Eng Technol Appl Sci Res. 2026 Oct 2;16(5):40056-64. doi:10.48084/etasr.20438.Checked: Abstract only
See all 1 Immune and connective tissue papers →
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.