Medical advancements in genetics and artificial intelligence

Developments in artificial intelligence and genetic testing are enhancing diagnostic precision and transforming preventive medicine by predicting future health risks.

In prenatal care, machine learning models such as Convolutional Neural Networks and Random Forests are being applied to improve the accuracy of cell-free DNA screening. Researchers at SickKids are utilizing episignatures to help clinicians classify variants of uncertain significance, which currently affect approximately one-third of detected genetic changes.

In oncology, expanded genetic testing is reportedly capable of altering treatment plans for nearly one-third of cancer patients through the identification of specific mutations. Technological advancements include the development of liquid biopsy tools to monitor tumor DNA in the blood and the use of AI to analyze MRI studies for improved prostate disease detection.

Recent progress includes the use of polygenic risk scores (PRS) to estimate the probability of developing complex conditions, such as type 2 diabetes, Alzheimer’s, breast cancer, and coronary artery disease, by analyzing thousands of genetic variants. While these scores act as a “probability map rather than absolute predictions,” they facilitate personalized precision medicine.

Furthermore, AI is being applied to repurpose routine screenings. A study presented at the European Society of Cardiology Congress showed that deep learning models can detect cardiovascular disease signals through mammography. By analyzing nearly 100,000 mammograms, the model identified markers for hypertension, ischemic heart disease, and stroke history, achieving an AUROC of 0.86 for stroke detection.