Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
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Volume 6 Number 1 2024
REVIEWS (Open Access)
Artificial Intelligence for Rare Disease Diagnosis: Machine Learning Performance, Predictive Modelling, and Clinical Translation
Mohammad Asaduzzaman 1*
Bioinfo Chem 6 (1) 1-10 https://doi.org/10.25163/bioinformatics.6110584
Submitted: 24 August 2024 Revised: 18 October 2024 Accepted: 26 October 2024 Published: 28 October 2024
Abstract
Artificial intelligence (AI) is increasingly being considered a potential turning point in rare disease research—although its role, at least for now, remains somewhat complex and not entirely settled. Rare diseases, despite affecting millions globally, continue to present significant diagnostic challenges, often driven by fragmented data, limited clinical familiarity, and inherently small, heterogeneous patient populations. In this narrative review, we synthesize current evidence on the application of artificial intelligence—particularly machine learning and deep learning—in improving diagnostic accuracy, predictive modelling, and clinical translation in rare diseases. Across the literature, AI-based models demonstrate a notable capacity to identify subtle disease patterns, particularly in imaging and high-dimensional datasets. Deep learning approaches frequently outperform traditional methods in pattern recognition tasks, while multimodal machine learning frameworks provide a more integrated understanding of disease mechanisms. Still, these outcomes are not entirely consistent. Model performance varies—sometimes substantially—depending on dataset size, diversity, and validation strategies. Smaller or less representative datasets, in particular, may produce overly optimistic estimates of diagnostic accuracy, raising concerns about generalizability. What becomes increasingly apparent is that artificial intelligence is not a standalone solution, but rather a data-dependent tool whose effectiveness is closely tied to data quality and validation rigor. While AI shows strong potential to reduce diagnostic delays and enhance clinical decision-making in rare diseases, meaningful clinical translation will likely depend on improved validation, transparency, and integration into real-world healthcare systems.
Keywords: Artificial intelligence; rare diseases; diagnostic accuracy; machine learning; deep learning; clinical decision support; predictive modeling
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