The classic one-size-fits-all method of treatment is changing to tailored ones that consider individual differences in genetics, lifestyle, and environment. This shift acknowledges that even if patients share the same diagnosis, their unique characteristics can lead to different responses to the same treatment, affecting treatment outcomes. Subsequently, AI has become an important tool to overcome challenges and support the development of personalised care.
The benefits of AI
One key obstacle to personalised healthcare is the management and analysis of complex and diverse medical data (eg. medical history, reports, activity data with wearable devices, and real-time monitoring). The healthcare and pharmaceutical industries generate data in abundance from a multitude of sources, including physician notes, pathology reports, EHRs, patient registries, genomics, clinical trials, wearable devices, and many more. A human cannot efficiently process and analyse all this data to create a personalised treatment plan for each patient. AI on the other hand can process all the medical data, recognise patterns, and provide insights from these datasets. For example, machine learning (ML) algorithms can be used to identify relations between a mix of tests, medical history, and allergy information to propose a tailored treatment plan.
AI can also significantly advance the development of precision and personalised medicine by identifying novel predictive biomarkers that are critical to tailoring specific treatments to individual patients. For example, Ocean Genomics鈥� AI platform identifies predictive variants in mRNA regarding a patient鈥檚 expected response to a drug, and the technology is then used in drug discovery by pharmaceutical companies to develop personalised treatments.
Similarly, platforms such as Paige AI focus on identifying predictive and prognostic biomarkers to help connect patients with the correct treatment while Certis Oncology Solutions鈥� CertisAI predictive medicine platform, launched in April 2023, uses big data and ML to study predictive biomarkers, enhance treatment strategies, and improve drug success rates. The effectiveness of AI heavily relies on the integration of big data, which is essential for the development and application of AI in precision and personalised medicine. AI models rely on high-quality data to enhance their decision-making capabilities. As these models process and train on more data, their accuracy and efficiency improve. This leads to actionable insights that enhance drug development, give greater precision in treatment, and reduce the time and cost of bringing new drugs to market.
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