How AI is Revolutionizing Rare Disease Diagnosis and Treatment Despite Lack of Diversity in Patient Populations

AI's potential to revolutionize the diagnosis and treatment of rare diseases faces challenges due to the lack of diversity in patient populations, but efforts to address this issue are underway through cross-border collaboration and engagement with patient communities according to a guest column published on Clinical Leader. 

Rare diseases collectively impact over 400 million people worldwide, presenting a significant challenge for health care systems. The inconsistent definitions and scarcity of patient data in rare disease research create complexities in diagnosing and treating these conditions. AI is poised to revolutionize rare disease research by improving diagnosis and treatment methods. The low prevalence of rare diseases makes patient data scarce and dispersed, hindering research efforts. Recent advances in DNA sequencing and global databases have improved our ability to diagnose and understand rare diseases, despite the challenges posed by their complexity and rarity.

The authors noted a growing interest in using AI, specifically machine learning algorithms, to analyze genomic data and improve the accuracy of rare disease (RD) diagnoses. These advanced AI models can identify clinical clues that may be overlooked in early detection of RDs, such as detecting complications in systemic sclerosis patients before deterioration. AI tools are already being utilized to support various aspects of RD diagnosis, including image recognition and genetic analysis, ultimately improving survival rates and reducing health care costs. The potential of AI in predicting disease progression and identifying new cases of RDs demonstrates the valuable role it can play in the future of rare disease diagnoses.

AI tools primarily train on data from databases like GWAS/PheWAS, clinicaltrials.gov, EHRs, and the UK Biobank’s whole genome sequencing database, which are mainly derived from Caucasian populations. As these populations only represent a small percentage of the global population, addressing the underrepresentation of diverse groups, including those of Asian, Middle Eastern, and African descent, is crucial. Achieving inclusive and equitable universal health coverage with AI requires the participation of people from diverse backgrounds in genomic studies and clinical trials through cross-border collaboration and resource sharing. 

Overcoming language, accessibility, and regulatory barriers is key to ensuring global participation in genetic testing and clinical studies. Collaboration between governments, industry leaders, advocacy groups, and organizations is essential to drive innovation and advance rare disease research for the development of effective treatments on a global scale.

Reference
Rajasimha HK, Dubet D. AI's Role In Advancing Rare Disease Research. Clinical Leader. Published February 27, 2024. Accessed September 23, 2024. https://www.clinicalleader.com/doc/ai-tools-for-advancing-rare-disease-research-0001