Applications of artificial intelligence in clinical laboratory genomics
Problem
Genomic data volumes are exceeding human capacity to rapidly and reproducibly analyze complex molecular data for timely diagnosis and management of genomic disorders.
Why it matters
Timely and accurate genomic diagnosis directly affects patient care and management decisions.
What the AI did
The paper reviews AI methods already being introduced into clinical laboratory genomics, including variant identification in sequencing data, prediction of DNA variant effects, phenotype-variant linking, NLP literature retrieval, and interactive chatbots.
Results
not stated in source
Impact
Describes AI already assisting variant identification and clinical interpretation in genomics workflows.
Record
| Venue | American Journal of Medical Genetics Part C Seminars in Medical Genetics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Swaroop Aradhya, Flavia M. Facio, Hillery C. Metz, Toby R. Manders, Alexandre Colavin, Yuya Kobayashi, Keith Nykamp, Britt A. Johnson et al. |
| Citations | 56 (OpenAlex, at import time) |
| References | 118 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/ajmg.c.32057 |
| Open access | hybrid |
| Keywords | clinical genomics, variant interpretation, artificial intelligence, diagnostics, natural language processing |
Importance rating
significant — thousands to millions affected
Abstract states genomic big data will exceed human capacity and AI is expected to significantly enhance geneticists' ability to translate complex data for clinicians managing patients at scale.