AI accelerates genomic variant interpretation and rare disease screening despite integration and equity challenges
The review examines how artificial intelligence is being applied to clinical genetics, particularly for managing the high volume of variants generated by next-generation and long-read sequencing. Investigators note that AI currently supports variant prioritization, rare disease screening, and precision medicine workflows. However, routine clinical adoption remains limited by population underrepresentation, ethical concerns, data governance gaps, and poor integration with hospital information systems. The authors conclude that continued development will likely shift these tools from experimental settings into standard molecular diagnostics pipelines.
The original study
Artificial Intelligence in Clinical Genetics: Current Applications and Challenges.
- Authors
- Sadanand R, Gupta N
- Journal
- Indian journal of pediatrics
- Type
- Journal Article, Review
- PMID
- 42443618
Original abstract
Multiomics, next-generation, and long-read sequencing approaches have transformed the practice of medical genetics. Complex cases often require several person-hours to make sense of the tens of thousands to millions of variants and biochemical patterns in each patient. Availability of massive datasets challenges traditional analytical and interpretive approaches. Artificial intelligence offers powerful ways to handle the growing volume and complexity of genomic and phenotypic data in clinical genetics. It is already influencing several areas of practice, including variant prioritization and interpretation, rare disease screening, and aspects of precision medicine. However, translating these advances into routine clinical use has proven difficult due to the underrepresentation of various populations, ethical issues, and issues related to data governance. As the majority of these tools are used in isolation, separate from hospital information systems and routine reporting pipelines, they are not optimally utilized. With continued progress in precision medicine and genomics, these AI genomic tools are likely to be integrated more into medical genetics practice, rather than remaining restricted to specialised or experimental settings.