The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Abstract

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1–9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

Applications of artificial intelligence in clinical laboratory genomics

“…In addition, large‐scale population data from genome‐wide association studies are increasingly being leveraged to identify polygenic risk for disease, and AI‐based methods are being used to conduct these types of studies and develop risk scores (Nicholls et al, 2020; Steinfeldt et al, 2022). Together, often with the support of AI methods, these complementary advances in genomics and phenomics are expected to catalyze faster drug discovery for hereditary diseases (Alves et al, 2022; Boniolo et al, 2021; Visibelli et al, 2023) and, importantly, extend to groups historically underserved by clinical genomics.…”

Artificial intelligence in rare disease diagnosis and treatment

“…These systems have previously been effectively utilized for a variety of well‐known use cases. Recently, it has been harnessed for the early detection and diagnosis of coronavirus disease 2019 (COVID‐19) through monitoring of demographic, clinical, and epidemiological characteristics of patients 6 . These systems are also useful for implementation for rare diseases (RDs).…”

“…The RDs, also sometimes referred to as orphan diseases, can stand to benefit from quicker and more efficient diagnoses. Algorithms have been designed and are already used to compile networks and register information through patients on rare diseases to identify new cases 6 . For instance, a combination of brain function and structural imaging data can be harnessed to determine whether a person with Huntington's disease (HD) will receive a clinical diagnosis within 5 years (pre‐HD) or quantifiable assessments of oculomotor function preceding HD 6 .…”

“…Algorithms have been designed and are already used to compile networks and register information through patients on rare diseases to identify new cases 6 . For instance, a combination of brain function and structural imaging data can be harnessed to determine whether a person with Huntington's disease (HD) will receive a clinical diagnosis within 5 years (pre‐HD) or quantifiable assessments of oculomotor function preceding HD 6 . These use cases show promising potential for future utilization of AI in rare disease diagnoses.…”

“…However, based on previous studies and information from the National Institutes of Health (NIH) Office of Rare Diseases Research and the US Food and Drug Administration (FDA) Office of Orphan Products Development, it is clear that recent government support has led to the approval of many drugs for RDs, thanks in part to advances in AI technology 8 . Furthermore, the passage of the US Orphan Drug Act in 1983 and the European Union Regulation on Orphan Medicines in 2000 has rewarded innovation in rare disease treatment 6 . Despite this, the conventional drug discovery pipeline has significantly hindered the research and development of new drugs to treat RDs.…”