AI-Assisted Rare Disease Diagnosis: Integrating Genomic Evidence and Clinical Intelligence

Tauhid Zaman

AI-Assisted Rare Disease Diagnosis: Integrating Genomic Evidence and Clinical Intelligence

Author(s)	Tauhid Zaman
Country	United States
Abstract	Rare diseases present major diagnostic challenges because of their low individual prevalence, substantial genetic heterogeneity, overlapping clinical manifestations and limited availability of disease-specific expertise. Patients may experience prolonged diagnostic journeys involving repeated consultations, inconclusive investigations and inappropriate or delayed treatment. Advances in artificial intelligence (AI), genomic sequencing and clinical informatics are creating new opportunities to improve rare disease diagnosis by integrating molecular evidence with phenotypic and clinical information. This paper examines the emerging role of AI-assisted diagnostic systems that combine genomic variants, electronic health records, clinical phenotypes, medical imaging, laboratory findings and biomedical knowledge. It proposes an Integrated Genomic-Clinical Intelligence Framework comprising five major components: multimodal clinical data acquisition, genomic variant interpretation, phenotype representation, AI-based evidence integration and clinician-guided diagnostic decision support. Applications of machine learning, deep learning, natural language processing, knowledge graphs and large language models in rare disease diagnosis are discussed. Particular attention is given to variant prioritisation, phenotype-driven gene discovery, genotype-phenotype matching and differential diagnosis. The paper also examines challenges related to incomplete genomic penetrance, variant interpretation, data quality, population diversity, explainability, privacy, algorithmic bias and clinical validation. The analysis suggests that AI should function primarily as an evidence-integration and decision-support technology rather than an autonomous diagnostic authority. Future rare disease diagnosis will increasingly depend on interoperable genomic and clinical datasets, multimodal AI models, continuously updated knowledge bases and human-AI collaboration. The paper concludes that responsible integration of genomic evidence and clinical intelligence could shorten diagnostic journeys, improve diagnostic accuracy and facilitate earlier personalised management for patients with rare diseases.
Keywords	Artificial Intelligence, Rare Diseases, Genomic Medicine, Clinical Intelligence, Machine Learning, Variant Interpretation, Phenotype Analysis, Precision Medicine, Clinical Decision Support, Genotype-Phenotype Integration.
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-01-06

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Assisted Rare Disease Diagnosis: Integrating Genomic Evidence and Clinical Intelligence

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