International Journal of Science and Technology (IJST)

Artificial Intelligence in Rare Disease Diagnostics: Shortening the Path to Early Detection

Rare diseases, despite individually affecting a small number of patients, collectively impact millions worldwide, often presenting significant challenges in timely diagnosis and treatment. The complexity, heterogeneity, and scarcity of data related to rare diseases contribute to frequent diagnostic delays, misdiagnoses, and prolonged patient suffering. Artificial Intelligence (AI) has emerged as a powerful tool to address these challenges by analyzing vast and complex datasets, uncovering subtle clinical patterns, and supporting clinicians in decision-making processes. This paper provides an extensive review of AI applications in rare disease diagnostics, highlighting machine learning algorithms, natural language processing (NLP), and predictive modeling techniques. The integration of AI with genomic sequencing data and electronic health records (EHRs) facilitates personalized and accurate diagnostic pathways, ultimately shortening the time to detection and improving patient outcomes. We also discuss the challenges posed by limited data availability, model interpretability, privacy concerns, and ethical issues. The paper concludes by exploring future prospects for AI in rare disease diagnosis, emphasizing collaborative efforts, advanced computational methods, and patient-centric approaches.