Can Artificial Intelligence Revolutionize the Diagnosis and Treatment of Rare Diseases?

In the United States, more than 30 million people live with a rare disease, a group of over 10,000 often unknown and difficult-to-identify conditions. For many, obtaining an accurate diagnosis takes an average of five to eight years, a delay that postpones access to appropriate treatments. Less than 5% of these diseases currently have an approved treatment, leaving the majority of patients without therapeutic solutions. The economic cost is immense: over $1 trillion is spent annually on these diseases in the United States, with a therapeutic market estimated at $400 billion by 2030.

The journey of patients with rare diseases often resembles a medical odyssey. Symptoms are sometimes vague or similar to those of more common conditions, complicating the work of general practitioners who already face a heavy workload. Genetic tests, while increasingly accessible, generate colossal amounts of data that are difficult to interpret. Comprehensive genomic analyses can miss certain anomalies, such as repeat expansions or large chromosomal aberrations, requiring more advanced and costly technologies.

Artificial intelligence could transform this situation by unifying the diagnostic and treatment process. Through models capable of analyzing genomic, clinical, and environmental data, AI can identify patterns invisible to the human eye. It can detect rare diseases before irreversible damage occurs by cross-referencing information from medical records, images, genetic sequences, or even audio and video recordings. These systems offer faster and more accurate diagnoses while reducing costs related to errors and delays.

However, major challenges remain. Current biological models do not capture the full complexity of the human body, which limits the reliability of predictions. The production of personalized treatments remains slow and expensive, with manufacturing timelines much longer than those for AI-designed treatments. Finally, funding these tailored therapies raises questions: their high cost and ultra-specific nature make it difficult to integrate them into traditional healthcare systems.

To overcome these obstacles, close collaboration between governments, industries, and foundations is essential. The goal is to create a sustainable infrastructure capable of supporting research, production, and distribution of innovative treatments. AI will not replace human experts, but it can assist them by automating the analysis of massive data sets and suggesting therapeutic approaches tailored to each patient. In the long term, this approach could make personalized care accessible to all, thereby transforming the management of rare diseases.

Sources and Credits

Source Study

DOI: https://doi.org/10.1007/s12553-026-01057-y

Title: Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy

Journal: Health and Technology

Publisher: Springer Science and Business Media LLC

Authors: Mai-Lan Ho; Marinka Zitnik; Ronen Azachi; Sanjay Basu; Pranav Rajpurkar; Richard Sidlow