Artificial intelligence (AI) and big data are transforming healthcare with high-throughput analyses of complex diseases. Machine learning and sophisticated computational methods can efficiently interpret human genomes and other biomarkers, providing insights for patient treatment and significant applications in diagnostics and preventive care.
A personalized treatment plan may include preventive care for diseases at a higher risk of developing, such as increased cancer screening if a patient possesses the BRCA 1 or BRCA 2 gene mutation. Additionally, AI can generate insights from genetic information, biomarkers, and other physiological data to predict how a patient will respond to different treatment options, which may help avoid adverse reactions, reduce the use of expensive or unnecessary treatments on patients that are unlikely to respond, and ultimately reduce hospitalization and outpatient costs. For more information, GlobalData’s latest report, Precision and Personalized Medicine – Thematic Research, provides insight into the most prevalent uses of personalized medicine, new applications, and the healthcare, macroeconomic, and technology themes driving growth.
Big data and bioinformatics can also offer human-centered data to be used for early drug research instead of, or in combination with, conventional methods like a cell or animal models. This could help streamline the drug discovery process by reducing the time and money spent on inviable drug candidates, especially for conditions that translate poorly between animal models and humans. For example, laboratory mice have historically been utilized in early phase drug trials but are a poor model for genetic diversity and human age-related diseases. So, treatments for neurodegenerative and other age-related conditions could greatly benefit from including human genetics in research and development (R&D).