Predicting how a patient will react to medication or treatment involves understanding many factors, including their genetic makeup. Each patient's genome can hold clues about how they might respond to certain drugs, their risk of adverse drug reactions, or their susceptibility to particular diseases. However, the sheer volume and complexity of genomic data make it challenging for traditional analysis methods to efficiently process and interpret this information.
AI algorithms are capable of sifting through vast amounts of genomic data at speeds and with a level of detail unattainable by human analysts. AI can analyse genetic variations and link them to a diagnosis, or to how patients metabolise drugs, all helping to predict the best treatment plan for that particular patient. For example, deep learning is being used to recognise potentially cancerous lesions in radiology images.
AI's predictive power is not limited to drug responses. It extends to forecasting disease progression, identifying potential health risks, and even suggesting preventive measures tailored to individual genetic profiles. Moreover, AI-driven genomics can integrate other types of data, such as clinical records, lifestyle information, and environmental factors, offering a more holistic view of a patient's health. In one instance, predictive modelling based on supervised learning was able to identify patients at the highest risk for adverse reactions, extended length of hospital stays, 30-day readmissions, and even deaths within a hospital encounter. This comprehensive approach enables more personalised and precise healthcare, moving away from a one-size-fits-all model to a more tailored strategy
By harnessing the power of AI, healthcare providers can make more informed decisions about treatments and medications, leading to better patient care and outcomes. This utilisation of AI in genomics represents a significant step forward in the field of personalised medicine.
For more information, download our whitepaper, "Data-driven healthcare: How artificial intelligence and machine learning are transforming genomics":
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