Back

GEN-KnowRD: Reframing AI for Rare Disease Recognition

2026-03-03 health informatics Title + abstract only
View on medRxiv
Show abstract

Rare diseases affect over 300 million people worldwide, yet patients often endure years-long diagnostic delays that limit timely intervention and trial opportunities. Computational rare disease recognition (RDR) remains constrained by knowledge resources that are often incomplete, heterogeneous, and dependent on extensive multi-disciplinary expert curation that cannot scale. Large language models (LLMs) applied directly for end-to-end diagnosis or disease discrimination face similar knowledge bo...

Predicted journal destinations

1
npj Digital Medicine
85 training papers
Top 0.3% 17.7%
2
Journal of the American Medical Informatics Association
53 training papers
Top 1% 11.8%
3
Nature Communications
483 training papers
Top 8% 11.0%
4
Journal of Biomedical Informatics
37 training papers
Top 0.8% 7.8%
5
Scientific Reports
701 training papers
Top 27% 7.8%
6
JAMIA Open
35 training papers
Top 2% 6.4%
7
PLOS Digital Health
88 training papers
Top 4% 5.9%
8
BMC Medical Informatics and Decision Making
36 training papers
Top 4% 4.2%
9
JMIR Medical Informatics
16 training papers
Top 2% 1.9%
10
PLOS ONE
1737 training papers
Top 92% 1.9%
11
Journal of Medical Internet Research
81 training papers
Top 9% 1.9%
12
International Journal of Medical Informatics
25 training papers
Top 4% 1.5%
13
Computers in Biology and Medicine
39 training papers
Top 5% 1.5%
14
Nature Medicine
88 training papers
Top 9% 1.2%
15
PLOS Computational Biology
141 training papers
Top 10% 1.2%
16
Bioinformatics
24 training papers
Top 2% 1.2%
17
Communications Medicine
63 training papers
Top 5% 1.0%
18
BMC Medical Research Methodology
41 training papers
Top 7% 0.9%
19
Patterns
15 training papers
Top 2% 0.9%
20
Nature Genetics
72 training papers
Top 10% 0.7%
21
Biology Methods and Protocols
19 training papers
Top 1% 0.7%
22
eLife
262 training papers
Top 55% 0.7%
23
Genetics in Medicine
57 training papers
Top 7% 0.5%
24
The Lancet Digital Health
25 training papers
Top 3% 0.5%