When Your Data Is Too Much to Process, Turn to AI/ML

‍
Many clinical programs can benefit from AI/ML software to remove initial legwork

‍

What are the clinical diagnostic applications of artificial intelligence (AI) and machine learning (ML)?

‍
AI and ML have many applications in the clinical space. For instance, in clinical diagnostics, they can prioritize genetic variants for review. A patient’s sequencing results typically contain numerous genetic variants that may be contributing to disease. Sometimes, in well-established disease-gene relationships (such as CFTR variants and cystic fibrosis), the causative variants are obvious. In other cases, especially for more clinically heterogeneous diseases or those with less specific clinical findings, the causative variant is not so obvious. In these situations, AI/ML models trained on historical and empirical data can be useful to nominate or prioritize potential diagnostic variants.

‍

‍