The way we treat diseases is changing. We used to treat everyone the same way, but now we're realizing that each person is different. Even if people have the same disease, they may respond differently to the same treatment. This is because of their unique genetics, lifestyle, and environment. Artificial intelligence (AI) is helping us overcome this challenge and develop personalized care.

One big problem with personalized healthcare is managing and analyzing all the different kinds of medical data. This data comes from many sources, like doctor's notes, lab reports, health records, patient registries, DNA tests, clinical trials, and wearable devices. It's too much for a person to handle. But AI can process all this data, find patterns, and provide insights. For example, machine learning can be used to identify relationships between different tests, medical history, and allergies to suggest a personalized treatment plan.

AI can also help develop precision medicine by finding new biomarkers that predict how a patient will respond to a drug. For example, Ocean Genomics uses AI to identify predictive variants in mRNA, which are then used by pharmaceutical companies to develop personalized treatments.

Other platforms, like Paige AI and Certis Oncology Solutions, are also using AI to identify biomarkers and improve treatment strategies. AI relies on big data to work effectively. As AI models process and train on more data, they become more accurate and efficient. This leads to better insights that can improve drug development, personalize treatment, and reduce the time and cost of bringing new drugs to market.