Advancements in genomics and machine learning algorithms are bringing us closer to the reality of personalized preventive healthcare. Imagine a scenario where a new virus is spreading rapidly. When you sign up for a vaccine, you’re sent a vial to collect a saliva sample. After analysis, you’re told which specific vaccine is best for you based on your individual genetics, age, gender, and other factors. This concept, known as precision prevention, has been made possible through the decoding of the human genome in 2003.
New Zealand, for instance, has a newborn screening program that includes genome sequencing machines and a genetic health service. The expansion of such programs and the use of artificial intelligence and machine learning will change how public healthcare is delivered. However, these developments also raise concerns about individual choice, personal privacy, and the protection of health information.
Precision prevention involves tailoring public health actions to the individual rather than broader groups. This is achieved by balancing a variety of variables like genes, life history, and environment with your risks, which change as you age. Data from sources like social media and wearable devices help train algorithms to match medical prevention measures with individuals.
Artificial intelligence and machine learning can predict your current and future health state with remarkable accuracy, helping to prevent disease. However, there are challenges to overcome. For instance, there’s a need to reduce digital literacy and online access barriers. Also, AI has a significant environmental impact, with large AI models emitting substantial amounts of carbon dioxide.
Furthermore, privacy and choice must be maintained, especially for children and marginalized communities. While precision healthcare can reduce the financial burden on the health system, there’s a need for more public education and awareness about machine learning algorithms before they become part of our everyday lives.
